Dissertations / Theses: 'Semantic Recommender'

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Sulieman, Dalia. "Towards Semantic-Social Recommender Systems." Phd thesis, Université de Cergy Pontoise, 2014. http://tel.archives-ouvertes.fr/tel-01017586.

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In this thesis we propose semantic-social recommendation algorithms, that recommend an input item to users connected by a collaboration social network. These algorithms use two types of information: semantic information and social information.The semantic information is based on the semantic relevancy between users and the input item; while the social information is based on the users position and their type and quality of connections in the collaboration social network. Finally, we use depth-first search and breath-first search strategies to explore the graph.Using the semantic information and the social information, in the recommender system, helps us to partially explore the social network, which leads us to reduce the size of the explored data and to minimize the graph searching time.We apply our algorithms on real datasets: MovieLens and Amazon, and we compare the accuracy an the performance of our algorithms with the classical recommendation algorithms, mainly item-based collaborative filtering and hybrid recommendation.Our results show a satisfying accuracy values, and a very significant performance in execution time and in the size of explored data, compared to the classical recommendation algorithms.In fact, the importance of our algorithms relies on the fact that these algorithms explore a very small part of the graph, instead of exploring all the graph as the classical searching methods, and still give a good accuracy compared to the other classical recommendation algorithms. So, minimizing the size of searched data does not badly influence the accuracy of the results.

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MENDONCA, DIOGO SILVEIRA. "PROBABILISTIC LATENT SEMANTIC ANALYSIS APPLIED TO RECOMMENDER SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13073@1.

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Os sistemas de recomendação são um tema de pesquisa constante devido a sua grande quantidade de aplicações práticas. Estes sistemas podem ser abordados de diversas maneiras, sendo uma das mais utilizadas a filtragem colaborativa, em que para recomendar um item a um usuário são utilizados dados de comportamento de outros usuários. Porém, nem sempre os algoritmos de filtragem colaborativa atingem níveis de precisão necessários para serem utilizados em aplicações reais. Desta forma este trabalho tem como objetivo avaliar o desempenho da análise probabilística de semântica latente (PLSA) aplicado a sistemas de recomendação. Este modelo identifica grupos de usuários com comportamento semelhante através de atributos latentes, permitindo que o comportamento dos grupos seja utilizado na recomendação. Para verificar a eficácia do método, apresentamos experimentos com o PLSA utilizando os problemas de recomendação de anúncios na web e a recomendação de filmes. Evidenciamos uma melhoria de 18,7% na precisão da recomendação de anúncios na web e 3,7% de melhoria no erro quadrático sobre a Média das Médias para o corpus do Netflix. Além dos experimentos, o algoritmo foi implementado de forma flexível e reutilizável, permitindo adaptação a outros problemas com esforço reduzido. Tal implementação também foi incorporada como um módulo do LearnAds, um framework de recomendação de anúncios na web.
Recommender systems are a constant research topic because of their large number of practical applications. There are many approaches to address these problems, one of the most widely used being collaborative filtering, in which in order to recommend an item to a user, data of other users` behaviors are employed. However, collaborative filtering algorithms do not always reach levels of precision required for the use in real applications. Within this context, the present work aims to evaluate the performance of the probabilistic latent semantic analysis (PLSA) applied to recommender systems. This model identifies groups of users with similar behaviors through latent attributes, allowing the use of these behaviors in the recommendation. To check the effectiveness of the method, there were presented experiments with problems of both web ad recommending and film recommending. An improvement of 18,7% were found in the accuracy of the recommendation of ads on the web and we also found 3.7% of improvement in Root Mean Square Error over the Means of Means baseline system for the Netflix corpus. Apart from the aforementioned experiments, the algorithm was implemented in a flexible and reusable way, allowing its adaptation to other problems with reduced effort. This implementation has also been incorporated as a module of LearnAds, a framework for the recommendation of ads on the web.

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Garden, Matthew. "On the use of semantic feedback in recommender systems." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82237.

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This thesis presents a new approach to recommender systems. Previous recommender systems based on collaborative filtering typically solicit user feedback on domain items as overall ratings which are then recorded as numeric values. This paradigm limits the semantic richness of the user's interaction with the system and the depth to which the system can understand user preferences. We propose a new recommender system, Recommendz, which allows the user to comment not only about the overall quality of the item but also about the quantity and quality of features of the item. This allows the user to justify his or her ratings and allows the system to compare users not only with respect to overall preference, but also to compare the reasons behind those preferences.
We have developed an implementation of our approach, and have collected extensive empirical data based on movie ratings. We demonstrate the effectiveness of our approach, and describe the details of the implementation.

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Borràs, Nogués Joan. "Semantic recommender systems Provision of personalised information about tourist activities." Doctoral thesis, Universitat Rovira i Virgili, 2015. http://hdl.handle.net/10803/310219.

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Aquesta tesi estudia com millorar els sistemes de recomanació utilitzant informació semàntica sobre un determinat domini (en el cas d’aquest treball, Turisme). Les ontologies defineixen un conjunt de conceptes relacionats amb un determinat domini, així com les relacions entre ells. Aquestes estructures de coneixement poden ser utilitzades no només per representar d'una manera més precisa i refinada els objectes del domini i les preferències dels usuaris, sinó també per millorar els procediments de comparació entre els objectes i usuaris (i també entre els mateixos usuaris) amb l'ajuda de mesures de similitud semàntica. Les millores al nivell de la representació del coneixement i al nivell de raonament condueixen a recomanacions més precises i a una millora del rendiment dels sistemes de recomanació, generant nous sistemes de recomanació semàntics intel•ligents. Les dues tècniques bàsiques de recomanació, basades en contingut i en filtratge col•laboratiu, es beneficien de la introducció de coneixement explícit del domini. En aquesta tesi també hem dissenyat i desenvolupat un sistema de recomanació que aplica els mètodes que hem proposat. Aquest recomanador està dissenyat per proporcionar recomanacions personalitzades sobre activitats turístiques a la regió de Tarragona. Les activitats estan degudament classificades i etiquetades d'acord amb una ontologia específica, que guia el procés de raonament. El recomanador té en compte molts tipus diferents de dades: informació demogràfica, les motivacions de viatge, les accions de l'usuari en el sistema, les qualificacions proporcionades per l'usuari, les opinions dels usuaris amb característiques demogràfiques similars o gustos similars, etc. Un procés de diversificació que calcula similituds entre objectes s'aplica per augmentar la varietat de les recomanacions i per tant augmentar la satisfacció de l'usuari. Aquest sistema pot tenir un impacte positiu a la regió en millorar l'experiència dels seus visitants.
Esta tesis estudia cómo mejorar los sistemas de recomendación utilizando información semántica sobre un determinado dominio, en el caso de este trabajo el Turismo. Las ontologías definen un conjunto de conceptos relacionados con un determinado dominio, así como las relaciones entre ellos. East estructuras de conocimiento pueden ser utilizadas no sólo para representar de una manera más precisa y refinada los objetos del dominio y las preferencias de los usuarios, sino también para aplicar mejor los procedimientos de comparación entre los objetos y usuarios (y también entre los propios usuarios) con la ayuda de medidas de similitud semántica. Las mejoras al nivel de la representación del conocimiento y al nivel de razonamiento conducen a recomendaciones más precisas y a una mejora del rendimiento de los sistemas de recomendación, generando nuevos sistemas de recomendación semánticos inteligentes. Las dos técnicas de recomendación básicas, basadas en contenido y en filtrado colaborativo, se benefician de la introducción de conocimiento explícito del dominio. En esta tesis también hemos diseñado y desarrollado un sistema de recomendación que aplica los métodos que hemos propuesto. Este recomendador está diseñado para proporcionar recomendaciones personalizadas sobre las actividades turísticas en la región de Tarragona. Las actividades están debidamente clasificadas y etiquetadas de acuerdo con una ontología específica, que guía el proceso de razonamiento. El recomendador tiene en cuenta diferentes tipos de datos: información demográfica, las motivaciones de viaje, las acciones del usuario en el sistema, las calificaciones proporcionadas por el usuario, las opiniones de los usuarios con características demográficas similares o gustos similares, etc. Un proceso de diversificación que calcula similitudes entre objetos se aplica para generar variedad en las recomendaciones y por tanto aumentar la satisfacción del usuario. Este sistema puede tener un impacto positivo en la región al mejorar la experiencia de sus visitantes.
This dissertation studies how new improvements can be made on recommender systems by using ontological information about a certain domain (in the case of this work, Tourism). Ontologies define a set of concepts related to a certain domain as well as the relationships among them. These knowledge structures may be used not only to represent in a more precise and refined way the domain objects and the user preferences, but also to apply better matching procedures between objects and users (or between users themselves) with the help of semantic similarity measures. The improvements at the knowledge representation level and at the reasoning level lead to more accurate recommendations and to an improvement of the performance of recommender systems, paving the way towards a new generation of smart semantic recommender systems. Both content-based recommendation techniques and collaborative filtering ones certainly benefit from the introduction of explicit domain knowledge. In this thesis we have also designed and developed a recommender system that applies the methods we have proposed. This recommender is designed to provide personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labelled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. A diversification process that computes similarities between objects is applied to produce diverse recommendations and hence increase user satisfaction. This system can have a beneficial impact on the region by improving the experience of its visitors.

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Eryol, Erkin. "Probabilistic Latent Semantic Analysis Based Framework For Hybrid Social Recommender Systems." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611921/index.pdf.

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Today, there are user annotated internet sites, user interaction logs, online user communities which are valuable sources of information concerning the personalized recommendation problem. In the literature, hybrid social recommender systems have been proposed to reduce the sparsity of the usage data by integrating the user related information sources together. In this thesis, a method based on probabilistic latent semantic analysis is used as a framework for a hybrid social recommendation system. Different data hybridization approaches on probabilistic latent semantic analysis are experimented. Based on this flexible probabilistic model, network regularization and model blending approaches are applied on probabilistic latent semantic analysis model as a solution for social trust network usage throughout the collaborative filtering process. The proposed model has outperformed the baseline methods in our experiments. As a result of the research, it is shown that the proposed methods successfully model the rating and social trust data together in a theoretically principled way.

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Joseph, Daniel. "Linking information resources with automatic semantic extraction." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/linking-information-resources-with-automatic-semantic-extraction(ada2db36-4366-441a-a0a9-d76324a77e2c).html.

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Knowledge is a critical dimension in the problem solving processes of human intelligence. Consequently, enabling intelligent systems to provide advanced services requires that their artificial intelligence routines have access to knowledge of relevant domains. Ontologies are often utilised as the formal conceptualisation of domains, in that they identify and model the concepts and relationships of the targeted domain. However complexities inherent in ontology development and maintenance have limited their availability. Separate from the conceptualisation component, domain knowledge also encompasses the concept membership of object instances within the domain. The need to capture both the domain model and the current state of instances within the domain has motivated the import of Formal Concept Analysis into intelligent systems research. Formal Concept Analysis, which provides a simplified model of a domain, has the advantage in that not only does it define concepts in terms of their attribute description but object instances are simultaneously ascribed to their appropriate concepts. Nonetheless, a significant drawback of Formal Concept Analysis is that when applied to a large dataset, the lattice with which it models a domain is often composed of a copious amount of concepts, many of which are arguably unnecessary or invalid. In this research a novel measure is introduced which assigns a relevance value to concepts in the lattice. This measure is termed the Collapse Index and is based on the minimum number of object instances that need be removed from a domain in order for a concept to be expunged from the lattice. Mathematics that underpin its origin and behaviour are detailed in the thesis showing that if the relevance of a concept is defined by the Collapse Index: a concept will eventually lose relevance if one of its immediate subconcepts increasingly acquires object instance support; and a concept has its highest relevance when its immediate subconcepts have equal or near equal object instance support. In addition, experimental evaluation is provided where the Collapse Index demonstrated comparable or better performance than the current prominent alternatives in: being consistent across samples; the ability to recall concepts in noisy lattices; and efficiency of calculation. It is also demonstrated that the Collapse Index affords concepts with low object instance support the opportunity to have a higher relevance than those of high supportThe second contribution to knowledge is that of an approach to semantic extraction from a dataset where the Collapse Index is included as a method of selecting concepts for inclusion in a final concept hierarchy. The utility of the approach is demonstrated by reviewing its inclusion in the implementation of a recommender system. This recommender system serves as the final contribution featuring a unique design where lattices represent user profiles and concepts in these profiles are pruned using the Collapse Index. Results showed that pruning of profile lattices enabled by the Collapse Index improved the success levels of movie recommendations if the appropriate thresholds are set.

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Akther, Aysha. "Social Tag-based Community Recommendation Using Latent Semantic Analysis." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23238.

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Collaboration and sharing of information are the basis of modern social web system. Users in the social web systems are establishing and joining online communities, in order to collectively share their content with a group of people having common topic of interest. Group or community activities have increased exponentially in modern social Web systems. With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this study, we address the problem of recommending communities to individual users. Recommender techniques that are based solely on community affiliation, may fail to find a wide range of proper communities for users when their available data are insufficient. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both user’s personal tag usage and other community members’ tagging patterns in the latent space. We specially focus on the challenging problem of recommending communities to users who have joined very few communities or having no prior community membership. Our evaluation on two heterogeneous datasets shows that our approach can significantly improve the recommendation quality.

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FIGUEROA, MARTINEZ CRISTHIAN NICOLAS. "Recommender Systems based on Linked Data." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2669963.

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Backgrounds: The increase in the amount of structured data published using the principles of Linked Data, means that now it is more likely to find resources in the Web of Data that describe real life concepts. However, discovering resources related to any given resource is still an open research area. This thesis studies Recommender Systems (RS) that use Linked Data as a source for generating recommendations exploiting the large amount of available resources and the relationships among them. Aims: The main objective of this study was to propose a recommendation tech- nique for resources considering semantic relationships between concepts from Linked Data. The specific objectives were: (i) Define semantic relationships derived from resources taking into account the knowledge found in Linked Data datasets. (ii) Determine semantic similarity measures based on the semantic relationships derived from resources. (iii) Propose an algorithm to dynami- cally generate automatic rankings of resources according to defined similarity measures. Methodology: It was based on the recommendations of the Project management Institute and the Integral Model for Engineering Professionals (Universidad del Cauca). The first one for managing the project, and the second one for developing the experimental prototype. Accordingly, the main phases were: (i) Conceptual base generation for identifying the main problems, objectives and the project scope. A Systematic Literature Review was conducted for this phase, which highlighted the relationships and similarity measures among resources in Linked Data, and the main issues, features, and types of RS based on Linked Data. (ii) Solution development is about designing and developing the experimental prototype for testing the algorithms studied in this thesis. Results: The main results obtained were: (i) The first Systematic Literature Re- view on RS based on Linked Data. (ii) A framework to execute and an- alyze recommendation algorithms based on Linked Data. (iii) A dynamic algorithm for resource recommendation based on on the knowledge of Linked Data relationships. (iv) A comparative study of algorithms for RS based on Linked Data. (v) Two implementations of the proposed framework. One with graph-based algorithms and other with machine learning algorithms. (vi) The application of the framework to various scenarios to demonstrate its feasibility within the context of real applications. Conclusions: (i) The proposed framework demonstrated to be useful for develop- ing and evaluating different configurations of algorithms to create novel RS based on Linked Data suitable to users’ requirements, applications, domains and contexts. (ii) The layered architecture of the proposed framework is also useful towards the reproducibility of the results for the research community. (iii) Linked data based RS are useful to present explanations of the recommen- dations, because of the graph structure of the datasets. (iv) Graph-based algo- rithms take advantage of intrinsic relationships among resources from Linked Data. Nevertheless, their execution time is still an open issue. Machine Learn- ing algorithms are also suitable, they provide functions useful to deal with large amounts of data, so they can help to improve the performance (execution time) of the RS. However most of them need a training phase that require to know a priory the application domain in order to obtain reliable results. (v) A log- ical evolution of RS based on Linked Data is the combination of graph-based with machine learning algorithms to obtain accurate results while keeping low execution times. However, research and experimentation is still needed to ex- plore more techniques from the vast amount of machine learning algorithms to determine the most suitable ones to deal with Linked Data.

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PALUMBO, ENRICO. "Knowledge Graph Embeddings for Recommender Systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850588.

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Sundaramurthy, Roshni. "Recommender System for Gym Customers." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166147.

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Recommender systems provide new opportunities for retrieving personalized information on the Internet. Due to the availability of big data, the fitness industries are now focusing on building an efficient recommender system for their end-users. This thesis investigates the possibilities of building an efficient recommender system for gym users. BRP Systems AB has provided the gym data for evaluation and it consists of approximately 896,000 customer interactions with 8 features. Four different matrix factorization methods, Latent semantic analysis using Singular value decomposition, Alternating least square, Bayesian personalized ranking, and Logistic matrix factorization that are based on implicit feedback are applied for the given data. These methods decompose the implicit data matrix of user-gym group activity interactions into the product of two lower-dimensional matrices. They are used to calculate the similarities between the user and activity interactions and based on the score, the top-k recommendations are provided. These methods are evaluated by the ranking metrics such as Precision@k, Mean average precision (MAP) @k, Area under the curve (AUC) score, and Normalized discounted cumulative gain (NDCG) @k. The qualitative analysis is also performed to evaluate the results of the recommendations. For this specific dataset, it is found that the optimal method is the Alternating least square method which achieved around 90\% AUC for the overall system and managed to give personalized recommendations to the users.

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Liu, Liwei. "The implication of context and criteria information in recommender systems as applied to the service domain." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/the-implication-of-context-and-criteria-information-in-recommender-systems-as-applied-to-the-service-domain(c3b8e170-8ae0-4e5c-a9b1-508f9c54316a).html.

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Recommender systems support online customers by suggesting products and services of likely interest to them. Research in recommender systems is now starting to recognise the importance of multiple selection criteria and the role of customer context in improving the recommendation output. This thesis investigates the inclusion of criteria and context information in the recommendation process. Firstly, a novel technique for multi-criteria recommendation is proposed. It assumes that some selection criteria for an item (product or a service) will dominate the overall rating, and that these dominant criteria will be different for different users. Following this assumption, users are clustered based on their criteria preferences, creating a “preference lattice”. The recommendation output for a user is then based on ratings by other users from the same or nearby clusters. Secondly, a context similarity metric for context aware recommendation is presented. This metric can help improve the prediction accuracy in two ways. On the one hand, the metric can guide the aggregation of the feedback from similar context to improve the prediction accuracy. This aggregation is important because the recommendation generation based on prior feedback by similar customers reduces the quantum of feedback used, resulting in a reduction in recommendation quality. On the other hand, the value returned by the context similarity metric can also be used to indicate the importance of the context information in the prediction process for a context aware recommendation.The validation of the two proposed techniques and their applications are conducted in the service domain because the relatively high degree of user involvement attracts users to provide detailed feedback from multiple perspectives, such as from criteria and context perspectives. In particular, hotel services and web services areas are selected due to their different levels of maturity in terms of users’ feedback. For each area, this thesis proposes a different recommendation approach by combining the proposed techniques with a traditional recommendation approach. The thesis concludes with experiments conducted on the datasets from the two aforementioned areas to evaluate the proposed techniques, and to demonstrate the process and the effectiveness of the techniques-based recommendation approaches.

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Martínez, García Miriam. "Enhancing the ELECTRE decision support method with semantic data." Doctoral thesis, Universitat Rovira i Virgili, 2018. http://hdl.handle.net/10803/665102.

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Prendre una decisió quan les opcions es defineixen mitjançant un conjunt divers de criteris no és fàcil. Aqueta tesi es centra en ampliar la metodologia ELECTRE, que és el mètode del tipus "outranking" més utilitzat. En aquesta tesi ens centrem en problemes de decisió que involucren informació no numèrica, tal com els criteris semàntics multivaluats, que poden prendre com a valors els conceptes d'una ontologia de domini determinada. Primer proposo una nova manera de manipular els criteris semàntics per evitar l'agregació de les puntuacions numèriques abans del procediment de classificació. Aquest mètode, anomenat ELECTRE-SEM, segueix els mateixos principis que el clàssic ELECTRE però, en aquest cas, els índexs de concordança i discordança es defineixen en termes de la comparació per parelles de les puntuacions que indiquen l'interès de l'usuari sobre diferents conceptes de l'ontologia. En segon lloc, proposo crear un perfil d'usuari semàntic mitjançant el emmagatzemant de puntuacions de preferències a l'ontologia. Es vincula una puntuació d'interès numèrica als conceptes més específics, això permet distingir millor les preferències de l'usuari, i també s'incorpora un procediment d'agregació per inferir les preferències de l'usuari considerant les relacions taxonòmiques entre conceptes. La metodologia proposada s'ha aplicat en dos casos d’estudi: l'avaluació de plantes de generació d'energia i la recomanació d'activitats turístiques a Tarragona.
Tomar una decisión cuando las opciones se definen sobre un conjunto diverso de criterios no es fácil. Esta tesis se centra en ampliar la metodología ELECTRE, que es el método del tipo "outranking" más utilizado. En esta tesis nos centramos en problemas de decisión que involucren información no numérica, tal como los criterios semánticos multi-valuados, que pueden tomar como valores los conceptos de una ontología de dominio determinada. Primero propongo una nueva forma de manejar los criterios semánticos para evitar la agregación de puntuaciones numéricas antes del procedimiento de clasificación. Este método, llamado ELECTRE-SEM, sigue los mismos principios que el clásico ELECTRE, pero en este caso los índices de concordancia y discordancia se definen en términos de la comparación por pares de unas puntuaciones que indican el interés del usuario sobre distintos conceptos de la ontología. En segundo lugar, propongo crear un perfil de usuario semántico mediante el almacenamiento de puntuaciones de preferencias en la ontología. Se asocian puntuaciones numéricas a los conceptos más específicos, lo cual permite distinguir mejor las preferencias del usuario, y se incorpora un proceso de agregación para inferir las preferencias del usuario mediante las relaciones taxonómicas entre conceptos. La metodología propuesta ha sido aplicada en dos casos de estudio: la evaluación de las plantas de generación de energía y la recomendación de actividades turísticas en Tarragona.
Reach a decision when options are defined on a set of diverse criteria is not easy. This thesis is focused on improving the methodology ELECTRE, which is the most used outranking-based method. In this dissertation, we focus on decision problems involving non-numerical information, such as multi-valued semantic criteria, which may take as values the concepts of a given domain ontology. First, I propose a new way of handling semantic criteria to avoid the aggregation of the numerical scores before the ranking procedure. This method, called ELECTRE-SEM, follows the same principles than the classic ELECTRE but in this case the concordance and discordance indices are defined in terms of the pairwise comparison of the interest scores. Second, I also propose to create a semantic user profile by storing preference scores into the ontology. The numerical interest score attached to the most specific concepts permits to distinguish better the preferences of the user, improving the quality of the decision by the incorporation of an aggregation methodology to infer the user's preferences by considering taxonomic relations between concepts. The proposed methodology has been applied in two case studies: the assessment of power generation plants and the recommendation of touristic activities in Tarragona.

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Li, Siying. "Context-aware recommender system for system of information systems." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.

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Travailler en collaboration n’est plus une question mais une réalité, la question qui se pose aujourd’hui concerne la mise en œuvre de la collaboration de façon à ce qu’elle soit la plus réussie possible. Cependant, une collaboration réussie n’est pas facile et est conditionnée par différents facteurs qui peuvent l’influencer. Il est donc nécessaire de considérer ces facteurs au sein du contexte de collaboration pour favoriser l’efficacité de collaboration. Parmi ces facteurs, le collaborateur est un facteur principal, qui est étroitement associé à l’efficacité et à la réussite des collaborations. Le choix des collaborateurs et/ou la recommandation de ces derniers en tenant compte du contexte de la collaboration peut grandement influencer la réussite de cette dernière. En même temps, grâce au développement des technologies de l’information, de nombreux outils numériques de collaboration sont mis à la disposition tels que les outils de mail et de chat en temps réel. Ces outils numériques peuvent eux-mêmes être intégrés dans un environnement de travail collaboratif basé sur le web. De tels environnements permettent aux utilisateurs de collaborer au-delà de la limite des distances géographiques. Ces derniers laissent ainsi des traces d’activités qu’il devient possible d’exploiter. Cette exploitation sera d’autant plus précise que le contexte sera décrit et donc les traces enregistrées riches en description. Il devient donc intéressant de développer les environnements de travail collaboratif basé sur le web en tenant d’une modélisation du contexte de la collaboration. L’exploitation des traces enregistrés pourra alors prendre la forme de recommandation contextuelle de collaborateurs pouvant renforcer la collaboration. Afin de générer des recommandations de collaborateurs dans des environnements de travail collaboratifs basés sur le web, cette thèse se concentre sur la génération des recommandations contextuelles de collaborateurs en définissant, modélisant et traitant le contexte de collaboration. Pour cela, nous proposons d’abord une définition du contexte de collaboration et choisissons de créer une ontologie du contexte de collaboration compte tenu des avantages de l’approche de modélisation en l’ontologie. Ensuite, une similarité sémantique basée sur l’ontologie est développée et appliquée dans trois algorithmes différents (i.e., PreF1, PoF1 et PoF2) afin de générer des recommandations contextuelles des collaborateurs. Par ailleurs, nous déployons l’ontologie de contexte de collaboration dans des environnements de travail collaboratif basés sur le web en considérant une architecture de système des systèmes d’informations du point de vue des environnements de travail collaboratif basés sur le web. À partir de cette architecture, un prototype correspondant d’environnement de travail collaboratif basé sur le web est alors construit. Enfin, un ensemble de données de collaborations scientifiques est utilisé pour tester et évaluer les performances des trois algorithmes de recommandation contextuelle des collaborateurs
Working collaboratively is no longer an issue but a reality, what matters today is how to implement collaboration so that it is as successful as possible. However, successful collaboration is not easy and is conditioned by different factors that can influence it. It is therefore necessary to take these impacting factors into account within the context of collaboration for promoting the effectiveness of collaboration. Among the impacting factors, collaborator is a main one, which is closely associated with the effectiveness and success of collaborations. The selection and/or recommendation of collaborators, taking into account the context of collaboration, can greatly influence the success of collaboration. Meanwhile, thanks to the development of information technology, many collaborative tools are available, such as e-mail and real-time chat tools. These tools can be integrated into a web-based collaborative work environment. Such environments allow users to collaborate beyond the limit of geographical distances. During collaboration, users can utilize multiple integrated tools, perform various activities, and thus leave traces of activities that can be exploited. This exploitation will be more precise when the context of collaboration is described. It is therefore worth developing web-based collaborative work environments with a model of the collaboration context. Processing the recorded traces can then lead to context-aware collaborator recommendations that can reinforce the collaboration. To generate collaborator recommendations in web-based Collaborative Working Environments, this thesis focuses on producing context-aware collaborator recommendations by defining, modeling, and processing the collaboration context. To achieve this, we first propose a definition of the collaboration context and choose to build a collaboration context ontology given the advantages of the ontology-based modeling approach. Next, an ontologybased semantic similarity is developed and applied in three different algorithms (i.e., PreF1, PoF1, and PoF2) to generate context-aware collaborator recommendations. Furthermore, we deploy the collaboration context ontology into web-based Collaborative Working Environments by considering an architecture of System of Information Systems from the viewpoint of web-based Collaborative Working Environments. Based on this architecture, a corresponding prototype of web-based Collaborative Working Environment is then constructed. Finally, a dataset of scientific collaborations is employed to test and evaluate the performances of the three context-aware collaborator recommendation algorithms

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VAGLIANO, IACOPO. "Content Recommendation Through Linked Data." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2670692.

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Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace. This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships. To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets. In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia.

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Lully, Vincent. "Vers un meilleur accès aux informations pertinentes à l’aide du Web sémantique : application au domaine du e-tourisme." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUL196.

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Cette thèse part du constat qu’il y a une infobésité croissante sur le Web. Les deux types d’outils principaux, à savoir le système de recherche et celui de recommandation, qui sont conçus pour nous aider à explorer les données du Web, connaissent plusieurs problématiques dans : (1) l’assistance de la manifestation des besoins d’informations explicites, (2) la sélection des documents pertinents, et (3) la mise en valeur des documents sélectionnés. Nous proposons des approches mobilisant les technologies du Web sémantique afin de pallier à ces problématiques et d’améliorer l’accès aux informations pertinentes. Nous avons notamment proposé : (1) une approche sémantique d’auto-complétion qui aide les utilisateurs à formuler des requêtes de recherche plus longues et plus riches, (2) des approches de recommandation utilisant des liens hiérarchiques et transversaux des graphes de connaissances pour améliorer la pertinence, (3) un framework d’affinité sémantique pour intégrer des données sémantiques et sociales pour parvenir à des recommandations qualitativement équilibrées en termes de pertinence, diversité et nouveauté, (4) des approches sémantiques visant à améliorer la pertinence, l’intelligibilité et la convivialité des explications des recommandations, (5) deux approches de profilage sémantique utilisateur à partir des images, et (6) une approche de sélection des meilleures images pour accompagner les documents recommandés dans les bannières de recommandation. Nous avons implémenté et appliqué nos approches dans le domaine du e-tourisme. Elles ont été dûment évaluées quantitativement avec des jeux de données vérité terrain et qualitativement à travers des études utilisateurs
This thesis starts with the observation that there is an increasing infobesity on the Web. The two main types of tools, namely the search engine and the recommender system, which are designed to help us explore the Web data, have several problems: (1) in helping users express their explicit information needs, (2) in selecting relevant documents, and (3) in valuing the selected documents. We propose several approaches using Semantic Web technologies to remedy these problems and to improve the access to relevant information. We propose particularly: (1) a semantic auto-completion approach which helps users formulate longer and richer search queries, (2) several recommendation approaches using the hierarchical and transversal links in knowledge graphs to improve the relevance of the recommendations, (3) a semantic affinity framework to integrate semantic and social data to yield qualitatively balanced recommendations in terms of relevance, diversity and novelty, (4) several recommendation explanation approaches aiming at improving the relevance, the intelligibility and the user-friendliness, (5) two image user profiling approaches and (6) an approach which selects the best images to accompany the recommended documents in recommendation banners. We implemented and applied our approaches in the e-tourism domain. They have been properly evaluated quantitatively with ground-truth datasets and qualitatively through user studies

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Lopes, Giseli Rabello. "Sistema de recomendação para bibliotecas digitais sob a perspectiva da web semântica." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/10747.

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Atualmente, pesquisadores e acadêmicos têm beneficiado-se muito com o crescimento acelerado das tecnologias Web, pois os resultados de pesquisa podem ser publicados e acessados eletronicamente tão logo a mesma tenha sido realizada. Esta possibilidade é vantajosa na medida em que minimiza as barreiras de tempo e espaço associadas à publicação tradicional. Neste contexto, surgem as Bibliotecas Digitais como repositórios de dados que, além dos documentos digitais propriamente ditos, ou de apontadores para estes documentos, armazenam os metadados associados. Para permitir que diferentes Bibliotecas Digitais possam interoperar surgiu a Open Archives Initiative (OAI) e, para resolver a questão da padronização dos metadados utilizados pelos repositórios, foi criado o formato Dublin Core (DC). Por outro lado, a enorme quantidade de documentos digitais disponíveis na Web tem causado o fenômeno conhecido como “sobrecarga de informação”. Com o objetivo de suprir esta dificuldade, Sistemas de Recomendação têm sido propostos e desenvolvidos. Estes sistemas visam prover uma interface alternativa para tecnologias de filtragem e recuperação de informações, tendo como foco a predição daqueles itens ou partes da informação que o usuário acharia interessante e útil. Portanto, os Sistemas de Recomendação atuam baseados em personalização da informação sendo que as predições geralmente são realizadas utilizando-se um perfil de cada usuário. A personalização está relacionada com o modo pelo qual a informação e serviços podem ser ajustados às necessidades específicas de um usuário ou comunidade. Esta dissertação descreve um Sistema de Recomendação de artigos científicos, armazenados em bibliotecas digitais. Este sistema é dirigido à comunidade científica da área da Ciência da Computação. Tecnologicamente, o sistema proposto foi desenvolvido sob a perspectiva da Web Semântica, à medida que faz uso de suas tecnologias emergentes tais como: uso de metadados padrão para a descrição de documentos - Dublin Core, uso do padrão XML para a descrição do perfil do usuário - Currículo Lattes, e provedores de serviços e de dados (OAI) envolvidos no processo de geração das recomendações. Este trabalho ainda apresenta e discute alguns resultados de experimentos baseados em avaliações quantitativas e qualitativas de recomendações geradas pelo sistema.
Currently, researchers and academics have been benefited by the expressive growth of web technologies, due to the possibility of publishing and accessing research results as soon as they are achieved. This possibility is advantageous as it minimizes the time and space barriers that traditional publications present. In this context, Digital Libraries emerged as data repositories that, beyond digital documents or links to them, store associated metadata. To allow the interoperability among different Digital Libraries, the Open Archives Initiative (OAI) was defined and, to solve the problem of metadata standardization, the Dublin Core standard (DC) was created. On the other hand, the great amount of available digital documents in the Web has caused the phenomenon known as “information overload”. In order to avoid this difficulty, Recommender Systems have been proposed and developed. These systems intend to provide an alternative interface for information filtering and retrieval technologies, focusing on the prediction of items or information parts that are interesting and useful for the user. Therefore, Recommender Systems act based on information personalization, and the predictions are generally generated using each user’s profile. The personalization is related to the way the information and the provided services can be adjusted to the specific necessities of a user or community. This dissertation describes a Recommender System for scientific articles stored in digital libraries. This system is geared towards the Computer Science scientific community. Technologically, the proposed system was developed under the Semantic Web perspective, as it explores its emergent technologies such as: use of standard metadata for document description - Dublin Core, use of the XML standard for users’ profile description - Lattes Curriculum Vitae, and services and data providers (OAI) involved on the recommendations generation process. In addition, this work presents and discusses some experimental results; the experiments are based on quantitative and qualitative evaluations of recommendations generated by the system.

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Alshareef, Abdulrhman M. "Academic Recommendation System Based on the Similarity Learning of the Citation Network Using Citation Impact." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39111.

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In today's significant and rapidly increasing amount of scientific publications, exploring recent studies in a given research area and building an effective scientific collaboration has become more challenging than any time before. Scientific production growth has been increasing the difficulties for identifying the most relevant papers to cite or to find an appropriate conference or journal to submit a paper to publish. As a result, authors and publishers rely on different analytical approaches in order to measure the relationship among the citation network. Different parameters have been used such as the impact factor, number of citations, co-citation to assess the impact of the produced research publication. However, using one assessing factor considers only one level of relationship exploration, since it does not reflect the effect of the other factors. In this thesis, we propose an approach to measure the Academic Citation Impact that will help to identify the impact of articles, authors, and venues at their extended nearby citation network. We combine the content similarity with the bibliometric indices to evaluate the citation impact of articles, authors, and venues in their surrounding citation network. Using the article metadata, we calculate the semantic similarity between any two articles in the extended network. Then we use the similarity score and bibliometric indices to evaluate the impact of the articles, authors, and venues among their extended nearby citation network. Furthermore, we propose an academic recommendation model to identify the latent preferences among the citation network of the given article in order to expose the concealed connection between the academic objects (articles, authors, and venues) at the citation network of the given article. To reveal the degree of trust for collaboration between academic objects (articles, authors, and venues), we use the similarity learning to estimate the collaborative confidence score that represents the anticipation of a prospect relationship between the academic objects among a scientific community. We conducted an offline experiment to measure the accuracy of delivering personalized recommendations, based on the user’s selection preferences; real-world datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to baseline recommendation algorithms that consider co-citation information.

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Werner, David. "Indexation et recommandation d'informations : vers une qualification précise des items par une approche ontologique, fondée sur une modélisation métier du domaine : application à la recommandation d'articles économiques." Thesis, Dijon, 2015. http://www.theses.fr/2015DIJOS078/document.

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La gestion efficace de grandes quantités d’informations est devenue un défi de plus en plus importantpour les systèmes d’information. Tous les jours, de nouvelles sources d’informations émergent surle web. Un humain peut assez facilement retrouver ce qu’il cherche, lorsqu’il s’agit d’un article,d’une vidéo, d’un artiste précis. En revanche, il devient assez difficile, voire impossible, d’avoir unedémarche exploratoire pour découvrir de nouveaux contenus. Les systèmes de recommandationsont des outils logiciels ayant pour objectif d’assister l’humain afin de répondre au problème desurcharge d’informations. Les travaux présentés dans ce document proposent une architecturepour la recommandation efficace d’articles d’actualité. L’approche ontologique utilisée repose surun modèle permettant une qualification précise des items sur la base d’un vocabulaire contrôlé.Contenu dans une ontologie, ce vocabulaire constitue une modélisation formelle de la vue métier surle domaine traité. Réalisés en collaboration avec la société Actualis SARL, ces travaux ont permis lacommercialisation d’un nouveau produit hautement compétitif, FristECO Pro’fil
Effective management of large amounts of information has become a challenge increasinglyimportant for information systems. Everyday, new information sources emerge on the web. Someonecan easily find what he wants if (s)he seeks an article, a video or a specific artist. However,it becomes quite difficult, even impossible, to have an exploratory approach to discover newcontent. Recommender systems are software tools that aim to assist humans to deal withinformation overload. The work presented in this Phd thesis proposes an architecture for efficientrecommendation of news. In this document, we propose an architecture for efficient recommendationof news articles. Our ontological approach relies on a model for precise characterization of itemsbased on a controlled vocabulary. The ontology contains a formal vocabulary modeling a view on thedomain knowledge. Carried out in collaboration with the company Actualis SARL, this work has ledto the marketing of a new highly competitive product, FristECO Pro’fil

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Benouaret, Idir. "Un système de recommandation contextuel et composite pour la visite personnalisée de sites culturels." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2332/document.

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Notre travail concerne les systèmes d’aide à la visite de musée et l’accès au patrimoine culturel. L’objectif est de concevoir des systèmes de recommandation, implémentés sur dispositifs mobiles, pour améliorer l’expérience du visiteur, en lui recommandant les items les plus pertinents et en l’aidant à personnaliser son parcours. Nous considérons essentiellement deux terrains d’application : la visite de musées et le tourisme. Nous proposons une approche de recommandation hybride et sensible au contexte qui utilise trois méthodes différentes : démographique, sémantique et collaborative. Chaque méthode est adaptée à une étape spécifique de la visite de musée. L’approche démographique est tout d’abord utilisée afin de résoudre le problème du démarrage à froid. L’approche sémantique est ensuite activée pour recommander à l’utilisateur des œuvres sémantiquement proches de celles qu’il a appréciées. Enfin l’approche collaborative est utilisée pour recommander à l’utilisateur des œuvres que les utilisateurs qui lui sont similaires ont aimées. La prise en compte du contexte de l’utilisateur se fait à l’aide d’un post-filtrage contextuel, qui permet la génération d’un parcours personnalisé dépendant des œuvres qui ont été recommandées et qui prend en compte des informations contextuelles de l’utilisateur à savoir : l’environnement physique, la localisation ainsi que le temps de visite. Dans le domaine du tourisme, les points d’intérêt à recommander peuvent être de différents types (monument, parc, musée, etc.). La nature hétérogène de ces points d’intérêt nous a poussé à proposer un système de recommandation composite. Chaque recommandation est une liste de points d’intérêt, organisés sous forme de packages, pouvant constituer un parcours de l’utilisateur. L’objectif est alors de recommander les Top-k packages parmi ceux qui satisfont les contraintes de l’utilisateur (temps et coût de visite par exemple). Nous définissons une fonction de score qui évalue la qualité d’un package suivant trois critères : l’appréciation estimée de l’utilisateur, la popularité des points d’intérêt ainsi que la diversité du package et nous proposons un algorithme inspiré de la recherche composite pour construire la liste des packages recommandés. L’évaluation expérimentale du système que nous avons proposé, en utilisant un data-set réel extrait de Tripadvisor démontre sa qualité et sa capacité à améliorer à la fois la précision et la diversité des recommandations
Our work concerns systems that help users during museum visits and access to cultural heritage. Our goal is to design recommender systems, implemented in mobile devices to improve the experience of the visitor, by recommending him the most relevant items and helping him to personalize the tour he makes. We consider two mainly domains of application : museum visits and tourism. We propose a context-aware hybrid recommender system which uses three different methods : demographic, semantic and collaborative. Every method is adapted to a specific step of the museum tour. First, the demographic approach is used to solve the problem of the cold start. The semantic approach is then activated to recommend to the user artworks that are semantically related to those that the user appreciated. Finally, the collaborative approach is used to recommend to the user artworks that users with similar preferences have appreciated. We used a contextual post filtering to generate personalized museum routes depending on artworks which were recommended and contextual information of the user namely : the physical environment, the location as well as the duration of the visit. In the tourism field, the items to be recommended can be of various types (monuments, parks, museums, etc.). Because of the heterogeneous nature of these points of interest, we proposed a composite recommender system. Every recommendation is a list of points of interest that are organized in a package, where each package may constitute a tour for the user. The objective is to recommend the Top-k packages among those who satisfy the constraints of the user (time, cost, etc.). We define a scoring function which estimates the quality of a package according to three criteria : the estimated appreciation of the user, the popularity of points of interest as well as the diversity of packages. We propose an algorithm inspired by composite retrieval to build the list of recommended packages. The experimental evaluation of the system we proposed using a real world data set crawled from Tripadvisor demonstrates its quality and its ability to improve both the relevance and the diversity of recommendations

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Góis, Marcos de Meira. "Melhorias para um sistema de recomendação baseado em conhecimento a partir da representação semântica de conteúdos." Universidade do Vale do Rio dos Sinos, 2015. http://www.repositorio.jesuita.org.br/handle/UNISINOS/4870.

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Nenhuma
Os Sistemas de Recomendação já estão consolidados como ferramentas que apoiam os usuários a superar as dificuldades geradas pelo volume excessivo de conteúdos disponíveis em formato digital, tendo sido projetados para realizar de forma automatizada as tarefas de classificação de conteúdos e de relacionamento deste com interesses e necessidades dos usuários. Um dos problemas ainda observados nestes sistemas está relacionado com a fragilidade de algumas abordagens de classificação e relacionamento de conteúdo que se baseiam principalmente em aspectos sintáticos dos conteúdos tratados. Os sistemas de recomendação baseados em conhecimento buscam mitigar este problema a partir da incorporação de elementos semânticos nos processos de indexação e relacionamento dos materiais. Apesar de bons resultados observados, ainda são identificadas necessidades de investigação, tanto nas atividades de classificação dos conteúdos, como na representação e tratamento dos relacionamentos entre conteúdos e possíveis interessados. Este trabalho busca colaborar com o desenvolvimento nesta área a partir da proposta de um sistema de recomendação baseado em conhecimento e voltado para a recomendação de materiais educacionais em um contexto de pequenos grupos de estudantes. O diferencial deste sistema se dá através de um processo de incorporação da semântica associada com os assuntos tratados e também com a utilização de aspectos semânticos para representar as necessidades e relacionamentos originados pelos usuários do sistema. O principal diferencial deste sistema está localizado na utilização de um algoritmo de recomendação híbrido, no qual tanto aspectos sintáticos como semânticos são empregados. Para avaliar o sistema de recomendação proposto, foi realizada a sua prototipação e teste em um ambiente controlado.
The Recommendation systems are already established as tools that support users to overcome the difficulties caused by the excessive volume of content available in digital format and was designed to conduct automated the content classification tasks and relationship of this with wins users. One of the problems observed in these systems is related to the weakness of some classification approaches and content relationship rely mainly on methodical aspects of the discussed subjects. Recommendation systems based on knowledge seek to mitigate this problem from the incorporation of semantic elements in the indexing processes and material relationship. Despite good results observed, research needs are also identified, both used to classify content activities, such as the representation and treatment of relationships between content and potential stakeholders. This paper seeks to contribute to the development in this area from the proposal for a recommendation system based on knowledge and facing the recommendation of educational materials in a context of small groups of students. The spread of this system is through a semantics of the merger process associated with these types of concerns and also with the use of semantic aspects to represent the needs and relationships originated by system users. The main distinguishing feature of this system is located in the use of a hybrid recommendation algorithm in which both syntactic and semantic aspects are employed. To evaluate the proposed recommendation system, it is due for prototyping and testing in a controlled environment.

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Vieira, Priscilla Kelly Machado. "Recomendação semântica de conteúdo em ambientes de convergência digital." Universidade Federal da Paraíba, 2013. http://tede.biblioteca.ufpb.br:8080/handle/tede/6109.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
The emerging scenario of interactive Digital TV (iDTV) is promoting the increase of interactivity in the communication process and also in audiovisual production, thus rising the number of channels and resources available to the user. This reality makes the task of finding the desired content becoming a costly and possibly ineffective action. The incorporation of recommender systems in the iDTV environment is emerging as a possible solution to this problem. This work aims to propose a hybrid approach to content recommendation in iDTV, based on data mining techniques, integrated the concepts of the Semantic Web, allowing structuring and standardization of data and consequent possibility of sharing information, providing semantics and automated reasoning. For the proposed service is considered the Brazilian Digital TV System and the middleware Ginga. A prototype has been developed and carried out experiments with NetFlix database using the measuring accuracy for evaluation. There was obtained an average accuracy of 30% using only mining technique. Including semantic rules obtained average accuracy of 35%.
Com o advento da TV Digital interativa (TVDi), nota-se o aumento de interatividade no processo de comunicação além do incremento das produções audiovisuais, elevando o número de canais e recursos disponíveis para o usuário. Esta realidade faz da tarefa de encontrar o conteúdo desejado uma ação onerosa e possivelmente ineficaz. A incorporação de sistemas de recomendação no ambiente TVDi emerge como uma possível solução para este problema. Este trabalho tem como objetivo propor uma abordagem híbrida para recomendação de conteúdo em TVDi, baseada em técnicas de Mineração de Dados, integradas a conceitos da Web Semântica, permitindo a estruturação e padronização dos dados e consequente possibilidade do compartilhamento de informações, provendo semântica e raciocínio automático. Para o serviço proposto é considerado o Sistema Brasileiro de TV Digital e o middleware Ginga. Foi desenvolvido um protótipo e realizado experimentos com a base de dados do NetFlix, utilizando a métrica de precisão para avaliação. Obteve-se uma precisão média de 30%, utilizando apenas a técnica de mineração. Acoplando-se com as regras semânticas obteve-se precisão média de 35%.

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Saia, Roberto. "Similarity and diversity: two sides of the same coin in the evaluation of data streams." Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266878.

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The Information Systems represent the primary instrument of growth for the companies that operate in the so-called e-commerce environment. The data streams generated by the users that interact with their websites are the primary source to define the user behavioral models. Some main examples of services integrated in these websites are the Recommender Systems, where these models are exploited in order to generate recommendations of items of potential interest to users, the User Segmentation Systems, where the models are used in order to group the users on the basis of their preferences, and the Fraud Detection Systems, where these models are exploited to determine the legitimacy of a financial transaction. Even though in literature diversity and similarity are considered as two sides of the same coin, almost all the approaches take into account them in a mutually exclusive manner, rather than jointly. The aim of this thesis is to demonstrate how the consideration of both sides of this coin is instead essential to overcome some well-known problems that affict the state-of-the-art approaches used to implement these services, improving their performance. Its contributions are the following: with regard to the recommender systems, the detection of the diversity in a user profile is used to discard incoherent items, improving the accuracy, while the exploitation of the similarity of the predicted items is used to re-rank the recommendations, improving their effectiveness; with regard to the user segmentation systems, the detection of the diversity overcomes the problem of the non-reliability of data source, while the exploitation of the similarity reduces the problems of understandability and triviality of the obtained segments; lastly, concerning the fraud detection systems, the joint use of both diversity and similarity in the evaluation of a new transaction overcomes the problems of the data scarcity, and those of the non-stationary and unbalanced class distribution.

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Lisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.

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Représenter l'information décrivant la musique est une activité complexe, qui implique différentes sous-tâches. Ce manuscrit de thèse porte principalement sur la musique classique et étudie comment représenter et exploiter ses informations. L'objectif principal est l'étude de stratégies de représentation et de découverte des connaissances appliquées à la musique classique, dans des domaines tels que la production de base de connaissances, la prédiction de métadonnées et les systèmes de recommandation. Nous proposons une architecture pour la gestion des métadonnées de musique à l'aide des technologies du Web Sémantique. Nous introduisons une ontologie spécialisée et un ensemble de vocabulaires contrôlés pour les différents concepts spécifiques à la musique. Ensuite, nous présentons une approche de conversion des données, afin d’aller au-delà de la pratique bibliothécaire actuellement utilisée, en s’appuyant sur des règles de mapping et sur l’interconnexion avec des vocabulaires contrôlés. Enfin, nous montrons comment ces données peuvent être exploitées. En particulier, nous étudions des approches basées sur des plongements calculés sur des métadonnées structurées, des titres et de la musique symbolique pour classer et recommander de la musique. Plusieurs applications de démonstration ont été réalisées pour tester les approches et les ressources précédentes
Representing the information about music is a complex activity that involves different sub-tasks. This thesis manuscript mostly focuses on classical music, researching how to represent and exploit its information. The main goal is the investigation of strategies of knowledge representation and discovery applied to classical music, involving subjects such as Knowledge-Base population, metadata prediction, and recommender systems. We propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialised ontology and a set of controlled vocabularies for the different concepts specific to music. Then, we present an approach for converting data, in order to go beyond the librarian practice currently in use, relying on mapping rules and interlinking with controlled vocabularies. Finally, we show how these data can be exploited. In particular, we study approaches based on embeddings computed on structured metadata, titles, and symbolic music for ranking and recommending music. Several demo applications have been realised for testing the previous approaches and resources

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Lemdani, Roza. "Système hybride d'adaptation dans les systèmes de recommandation." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC050/document.

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Les systèmes de recommandation sont des outils servant à suggérer aux utilisateurs des items pouvant les intéresser. De tels systèmes requièrent la définition d'un algorithme prenant en compte le domaine d'application. Cet algorithme est ensuite exécuté pour chaque utilisateur du système afin de lui générer des recommandations, et ce, sans prendre en compte ses particularités et ses besoins spécifiques.L'objet de cette thèse consiste à proposer une nouvelle approche de recommandation hybride combinant plusieurs algorithmes de recommandation afin d'obtenir une recommandation plus précise. De plus, l'approche proposée repose sur la structure de l'ontologie donnée en entrée du système, ce qui la rend réutilisable, facilement adaptable et applicable à tous les domaines (musique, publications scientifiques, films, etc.).Nous nous sommes également intéressées à la détection du type de recommandations auxquelles l'utilisateur répond le mieux afin d'adapter le processus de recommandation à chaque catégorie d'utilisateur et d'obtenir des recommandations plus ciblées. Notre approche de recommandation permet également d'expliquer les recommandations obtenues, ce qui permet d'augmenter la confiance de l'utilisateur vis-à-vis du système en lui prouvant que ses recommandations lui sont personnellement destinées et de lui donner la possibilité de corriger les explications, ce qui améliore la connaissance de l'utilisateur par le système et aide à écarter les futures recommandations non pertinentes.Le système de recommandation défini a été expérimenté hors-ligne à l'aide d'une validation croisée sur le dataset de MovieLens et en ligne avec de vrais utilisateurs. Les résultats obtenus sont très satisfaisants
Recommender systems are tools used to present users with items that might interest them. Such systems use algorithms that rely on the domain application. These algorithms are then executed for each user in order to find the most relevant recommendations for him, without taking into account his specific needs.In this thesis, we define a hybrid recommender system which combines several recommendation algorithms in order to obtain more accurate recommendations. Moreover, the defined approach relies on the structure of the input ontology, which makes the framework reusable, adaptable and domain-independent (music, research papers, films, etc.).We also had an interest in detecting in which kind of recommendations a user responds better in order to adapt the recommendation process to each user category and obtain more targeted recommendations. Finally, our approach can explain each recommendation, which increases the user confidence in the system by proving him that the recommendations are adapted to him. We also allow the user to correct the explanations in order to help the system to get a better understanding of him and avoid non accurate recommendations in the future.Our recommender system has been experimented online with real users and offline by performing a cross-validation on the MovieLens dataset. The results of the experimentation are very satisfying so far

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Mariano, Roberval Gomes. "DESENVOLVIMENTO DE UMA FAMÍLIA DE SISTEMAS DE RECOMENDAÇÕES BASEADOS NA TECNOLOGIA DA WEB SEMÂNTICA E SEU REUSO NA RECOMENDAÇÃO DE INSTRUMENTOS JURÍDICO-TRIBUTÁRIOS." Universidade Federal do Maranhão, 2008. http://tedebc.ufma.br:8080/jspui/handle/tede/400.

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Made available in DSpace on 2016-08-17T14:53:01Z (GMT). No. of bitstreams: 1 Roberval Gomes Mariano.pdf: 3806410 bytes, checksum: 98c37c22e17816b87c3a646527ac2c4e (MD5) Previous issue date: 2008-12-05
The huge amount of data available on the Web and its dynamic nature create a demand for information filtering applications such as recommender systems. The lack of semantic structure of data available on the Web constitutes a barrier for increasing the effectiveness of such applications family. This work discusses the analysis, design, implementation and evaluation of Semantic Web based hybrid filtering agents. Such agents were integrated in ONTOSERS, an application family for the development of recommender systems based on the Semantic Web technology. The implemented agents were evaluated and their results were compared with the results of collaborative and content-based filtering agents. The hybrid filtering techniques presented better results than the other approaches in the conducted experiments. The tested hybrid filtering approaches were the weighted and switched ones. The explicit feedback was used to validate the recommendations, presenting a better correlation with the hybrid filtering techniques. The developed agents were also evaluated through the reuse of the ONTOSERS systems family, a multi-agent recommender system in the Brazilian tributary domain.
A grande quantidade de dados disponíveis na Web e a sua natureza dinâmica criam uma demanda por aplicações de filtragem de informação, tais como os sistemas de recomendação. A falta de estruturação semântica dos dados disponíveis na Web é uma barreira para a melhoria da efetividade desta família de aplicações. Este trabalho apresenta a análise, projeto, implementação e avaliação de agentes de filtragem híbrida baseados na tecnologia da Web Semântica. Estes agentes foram integrados na ONTOSERS, uma família de aplicações para o desenvolvimento de sistemas de recomendações baseados na tecnologia da Web Semântica. Os agentes implementados foram testados e tiveram seus resultados comparados com os resultados de agentes utilizando filtragem colaborativa e baseada em conteúdo. As técnicas de filtragem híbrida apresentaram resultados melhores do que os obtidos com as outras técnicas nos experimentos realizados. As técnicas de filtragem híbrida testadas foram a ponderada e a alternada. O feedback explícito foi utilizado para validar as recomendações, apresentando uma melhor correlação com as técnicas de filtragem híbrida. Os agentes desenvolvidos foram ainda avaliados através do reuso da família de sistemas ONTOSERS na construção do INFOTRIB, um sistema multiagente de recomendações no domínio tributário brasileiro.

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Bogdanov, Dmitry. "From music similarity to music recommendation : computational approaches based on audio features and metadata." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123776.

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Aquest treball es centra en el modelatge d'usuari per la recomanació musical i desenvolupa algoritmes per la comprensió automàtica i visualització de preferències musicals. Primer, es proposa un model d'usuari construït a partir d'un conjunt de peces musicals. En segon lloc, s'estudien mètodes d’estimació de similitud musical, treballant exclusivament en el contingut d'àudio. Es proposen noves mètriques basades en la informació tímbrica, temporal, tonal i semàntica. En tercer lloc, es proposen diversos mètodes de recomanació musical que utilitzen aquestes mètriques i que milloren amb un filtratge addicional basat en metadades. També es proposa un mètode senzill basat en metadades editorials. En quart lloc, es presenten els predictors de preferència rellevants a nivell acústic i semàntic. Finalment, es presenta un mètode de visualització de preferències que millora l'experiència d'usuari en sistemes de recomanació.
In this work we focus on user modeling for music recommendation and develop algorithms for computational understanding and visualization of music preferences. Firstly, we propose a user model starting from an explicit set of music tracks provided by the user as evidence of his/her preferences. Secondly, we study approaches to music similarity, working solely on audio content and propose a number of novel measures working with timbral, temporal, tonal, and semantic information about music. Thirdly, we propose distance-based and probabilistic recommendation approaches working with explicitly given preference examples. We employ content-based music similarity measures and propose filtering by metadata to improve results of purely content-based recommenders. Moreover, we propose a lightweight approach working exclusively on editorial metadata. Fourthly, we demonstrate important predictors of preference from both acoustical and semantic perspectives. Finally, we demonstrate a preference visualization approach which allows to enhance user experience in recommender systems.

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Patel, Namrata. "Mise en œuvre des préférences dans des problèmes de décision." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT286/document.

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Il y a une forte croissance, à nos jours, de «services» intelligents proposés aux clients sur les plates-formes de commerce électronique, destinés à une assistance personnalisée. L'étude de préférences a suscité un grand intérêt dans ce contexte, grâce à leur utilisation dans la résolution de problèmes liés à la prise de décision. En effet, la recherche sur les préférences en intelligence artificielle (IA) propose différentes manières d'aborder ce problème : de l'acquisition des préférences à leur représentation formelle et, éventuellement, à leur gestion suivant plusieurs méthodes de raisonnement. Dans cette thèse, nous adressons la problématique de la mise en œuvre de préférences comparatives pour l'aide à la décision par le développement d'un système interactif «intelligent» de recommandations personnalisées. Nous suivons une tendance récente, et le concevons sur une base de considérations psychologiques, linguistiques et personnelles. Nous contribuons ainsi aux domaines suivants de préférences en IA : (1) leur acquisition, (2) leur représentation, et (3) leur mise en œuvre. Nous examinons d'abord un goulot d'étranglement dans l'acquisition de préférences et proposons une méthode d'acquisition de préférences exprimées en langage naturel (LN), qui permet leur représentation formelle en tant que préférences comparatives. Nous étudions ensuite les aspects théoriques de la représentation et du raisonnement avec les préférences comparatives pour aide à la décision. Finalement, nous décrivons notre outil de recommandations qui utilise : (1) une base de données de produits qualifiée par une analyse de critiques d'utilisateurs, (2) une approche interactive pour guider les utilisateurs à exprimer leurs préférences comparatives, et (3) un moteur de raisonnement qui manipule ces préférences afin de proposer une recommandation basée sur les préférences de l'utilisateur
Intelligent ‘services’ are increasingly used on e-commerce platforms to provide assistance to customers. In this context, preferences have gained rapid interest for their utility in solving problems related with decision making. Research on preferences in AI has shed light on various ways of tackling this problem, ranging from the acquisition of preferences to their formal representation and eventually their proper manipulation. Following a recent trend of stepping back and looking at decision-support systems from the user’s point of view, i.e. designing them on the basis of psychological, linguistic and personal considerations, we take up the task of developing an “intelligent” tool which uses comparative preference statements for personalised decision support. We tackle and contribute to different branches of research on preferences in AI: (1) their acquisition (2) their formal representation and manipulation (3) their implementation. We first address a bottleneck in preference acquisition by proposing a method of acquiring user preferences, expressed in natural language (NL), which favours their formal representation and further manipulation. We then focus on the theoretical aspects of handling comparative preference statements for decision support. We finally describe our tool for product recommendation that uses: (1) a review-based analysis to generate a product database, (2) an interactive preference elicitation unit to guide users to express their preferences, and (3) a reasoning engine that manipulates comparative preference statements to generate a preference-based ordering on outcomes as recommendations

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"CodeReco - A Semantic Java Method Recommender." Master's thesis, 2017. http://hdl.handle.net/2286/R.I.44982.

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abstract: The increasing volume and complexity of software systems and the growing demand of programming skills calls for efficient information retrieval techniques from source code documents. Programming related information seeking is often challenging for users facing constraints in knowledge and experience. Source code documents contain multi-faceted semi-structured text, having different levels of semantic information like syntax, blueprints, interfaces, flow graphs, dependencies and design patterns. Matching user queries optimally across these levels is a major challenge for information retrieval systems. Code recommendations can help information seeking and retrieval by pro-actively sampling similar examples based on the users context. These recommendations can be beneficial in improving learning via examples or improving code quality by sampling best practices or alternative implementations. In this thesis, an attempt is made to help programming related information seeking processes via pro-active code recommendations, and information retrieval processes by extracting structural-semantic information from source code. I present CodeReco, a system that recommends semantically similar Java method samples. Conventional code recommendations found in integrated development environments are primarily driven by syntactical compliance and auto-completion, whereas CodeReco is driven by similarities in use of language and structure-semantics. Methods are transformed to a vector space model and a novel metric of similarity is designed. Features in this vector space are categorized as belonging to types signature, structure, concept and language for user personalization. Offline tests show that CodeReco recommendations cover broader programming concepts and have higher conceptual similarity with their samples. A user study was conducted where users rated Java method recommendations that helped them icomplete two programming problems. 61.5% users were positive that real time method recommendations are helpful, and 50% reported this would reduce time spent in web searches. The empirical utility of CodeReco’s similarity metric on those problems was compared with a purely language based similarity metric (baseline). Baseline received higher ratings from novices, arguably due to lack of structure-semantics in their samples while seeking recommendations.
Dissertation/Thesis
Masters Thesis Computer Science 2017

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Yeh, Li Kai, and 葉力愷. "Applying Semantic Web and DBPEDIA to Recommender System." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107CGU05396033%22.&searchmode=basic.

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碩士
長庚大學
資訊管理學系
107
Recommender system plays a big role in the society these days. In the Market it has a pivotal position. Academically, it has been a popular topic for researchers. There are more and more usable data because of the rapid growth of technology and the integrity of the corporation’s data. The customers can also rate projects or give some subjective feedback which also makes more usable data. How these data can be used is the most difficult subject. Recommender system is one of the method that can wisely use these usable data. Recommender system has a wide range of applications such as movies, music, news, travels and so on. This widely property has made recommender system so popular no matter in researches or businesses. To make the data more completed we brought in the concept of semantic web while extracting movies’ features from the linked open data(LOD) which is DBPEDIA. Finally, we build a model with similarity analyze and to predict the precision as 0.71. In this study we will focus on a movie recommender system based on a dataset from Movielens.

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Chou, Ming-Han, and 周明翰. "A Personal Movie Recommender based on Latent Semantic Analysis." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/24217428291127950190.

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碩士
國立屏東科技大學
資訊管理系所
101
With the rapid development of information technology and Internet, there are more and more information disseminated and transmitted on the Internet. It is convenient to Internet users but it also caused information overloading problem. Therefore, the information retrieval and information filtering technologies have attracted much attention. Latent Semantic Analysis (LSA) develops a sematic space for the data set. By the singular value decomposition and dimension reduction calculation, we can get the real meaning of words in the content of the articles. LSA is deemed as an effective tool to solve the information overloading problem. In this research, we developed a personal movie recommender based on LSA and personal preferences. We have conducted a prototype system and an experiment to evaluate the performance. The results of the experiment show that our system has much better performance than the recommender designed based on random recommendation.

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Dewabharata, Anindhita, and Anindhita Dewabharata. "A Design of Semantic-based Recommender System for Medical Tourism." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/99416214431752574139.

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碩士
國立臺灣科技大學
工業管理系
100
Medical tourism has been growing very rapidly in recent years. This trend causing the information about medical tourism destination will increase significantly. The information of medial tourism has been found online started from the demographic spread of the potential medical tourists and medical destination. However, the growth of information available on the web nowadays has led to information overload, hampering the user's ability to distinguish relevant information from irrelevant. This condition restricts people use information resource effectively. Due to this fact, recommender systems have gained momentum as an efficient tool to reduce the complexity when searching for relevant information. Personalization capabilities are undoubtedly valuable for recommender system to match the user's preference against all available medical tourism resources. In designing a recommendation system, it is important to consider about construction of the main design decisions and it can be constrained by the environment of the recommender which is influence them. The recommender system is designed by using the technology of the semantic web to model the domain knowledge and as a content-based recommendation technique. Finally, a design of recommender system for medical tourism has been proposed in this research. The system will generate recommendation of medical tourism resources all in one package to users.

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Huang, Jin-ruei, and 黃進瑞. "Combining latent semantic analysis and learning vector quantization to construct hybrid filtering recommender." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/55516339392716802687.

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碩士
國立雲林科技大學
資訊管理系碩士班
101
Content-based filtering and collaborative filtering are often used techniques in recommendation system. The former method analyzes product attribute from users by using similarity to make recommendation. The latter method analyzes rating records from users by using similarity to make recommendation. Since the number of users and products are increasing as time goes on, many studies tend to add more product attributes to analyze similarity. While ignoring the content description from products, the prediction errors will be increased. In recent years, some studies have proposed Latent Semantic Analysis (LSA), which can analyze keywords relevance from different documents. In this paper, we applied a sequential combination of architecture in a hybrid filtering recommendation system and used MovieLens as our dataset, and classified the dataset based on their attributes. Then we applied LSA to analyze the film plots, and cluster these films to build rating matrices from the user rating records. Finally, we used Learning Vector Quantization (LVQ) to build our framework. The prediction results showed that hybrid recommendation system provides promising personalized recommendation. Our experiment gains 80.1% of precision, and 82.1% of recall.

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Anelli, Vito Walter. "Knowledge-Enabled Recommender Systems in the Linked Data Era." Doctoral thesis, 2020. http://hdl.handle.net/11589/191260.

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I sistemi di raccomandazione sono in generale scarsamente conosciuti. Tuttavia, sono praticamente onnipresenti. Sono loro che, in un mondo che ci sommerge di informazione rilevante ed irrilevante, fanno la differenza. Processano cataloghi di migliaia, o milioni, di elementi per restituirci l'informazione personalizzata e rilevante che cerchiamo. Senza di loro, saremmo come naufraghi in un oceano di informazioni che tentano di berlo tutto, sorso dopo sorso. D'altro canto, sussurrano continuamente all'orecchio di leggere notizie, guardare film, ascoltare canzoni. Che siano dei grilli parlanti o dei lucignolo, alla fine è nostra responsabilità definirlo. Allo stesso tempo, il web si sta evolvendo, fornendoci una informazione ricca e semantica. Il cosiddetto Semantic Web, infatti, ci consente di alimentare i sistemi di raccomandazione con conoscenza di elevata qualità. Questa conoscenza consente ai sistemi di raccomandazione di comprendere il dominio, fornire spiegazioni, migliorare la qualità delle raccomandazioni. In questo percorso di ricerca, abbiamo affrontato i diversi aspetti della raccomandazione ed i diversi modi in cui la conoscenza semantica può risultare utile. Ci siamo inizialmente focalizzati sulla fattorizzazione di matrici, una tecnica di raccomandazione, ed abbiamo proposto diversi metodi per inglobare conoscenza in essa. Fattorizzazione di feature, diffusione della rilevanza su grafo e Factorization Machine interpretabili sono solo alcuni esempi in tal senso. Abbiamo sviluppato un sistema di raccomandazione che sfrutta le prefenze condizionali pair-wise per abbassare la barriera tra l'umano e la macchina. Abbiamo affrontato il problema della conoscenza semi-strutturata, proponendo modelli che considerano la diversificazione delle raccomandazioni nel tempo, la popolarità personalizzata e la dissimilarità. Infine, ci siamo concentrati sulla valutazione dei sistemi di raccomandazione, proponendo nuove tecniche per impostare gli iperparametri di un modello ed abbiamo definito una nuova nozione di fairness. Ci auguriamo che vi godiate il viaggio.
Recommender Systems are unfamiliar to ordinary people. However, they are almost everywhere. In a world that overwhelms us with relevant and irrelevant information, they make the difference. They process catalogs from thousands to millions of items to return us only the relevant and personalized information. Otherwise, we would be as castaways in the ocean of information that try to drink it all. On the other side, they are constantly whispering in our ear, suggesting to enjoy news, movies, songs. If they are Jiminy or Lamp-Wick, it is our responsibility. At the same time, the Web is evolving, providing us rich and semantic information. The so-called Semantic Web lets us feed Recommender Systems with high-quality knowledge. This knowledge lets Recommender Systems understand the domain, provide explanations, improve the quality of recommendations. In this research journey, we have faced different aspects of the recommendation and the multiple ways semantic knowledge can be beneficial. We have first focused on Matrix Factorization, a recommendation technique, and we have proposed several ways to exploit knowledge. Feature Factorization, Graph Spreading Relevance, and interpretable Factorization Machines are a few examples. We have developed a recommender that takes into account conditional pair-wise preferences to lower the human-machine barrier. We have also faced the semi-structured knowledge, proposing models that consider temporal diversification, personalized popularity, and dissimilarity. Finally, we have focused on Recommender Systems evaluation, proposing new techniques for tuning hyperparameters, and a new notion of fairness. We hope you will enjoy the journey.

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TOMEO, Paolo. "Beyond Accuracy in Recommender Systems under the Linked Data lens." Doctoral thesis, 2017. http://hdl.handle.net/11589/98558.

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I Sistemi di Raccomandazione sono diventanti strumenti fondamentali per aiutare gli utenti nella ricerca di cosa è rilevante per loro in situazioni dove il sovraccarico informativo rende tale task difficile o quasi impossibile. I Sistemi di Raccomandazione sono progettati per suggerire oggetti sconosciuti a utenti in modo personalizzato, raccomandando gli oggetti che sono più probabilmente di interesse per gli utenti. Mentre nuovi algoritmi e approcci sono stati proposti negli anni principalmente con l'obiettivo di migliorare l'accuratezza delle raccomandazioni, recentemente è stato evidenziato che l'accuratezza predittiva non è sufficiente a garantire una user experience soddisfacente. È stata posta l'attenzione su altri importanti fattori di qualità come la diversità e novità delle raccomandazioni, e su altri problemi in questo campo, come il problema degli utenti nella fasi di cold-start. Allo stesso tempo, il Web si è evoluto da uno spazio di informazione globale di documenti collegati a un Web dei Dati. L'iniziativa Linked Data nata al fine di fornire una serie standardizzata di best practices per la pubblicazione e il collegamento strutturato di dati sul Web, ha avuto un ruolo fondamentale nello sviluppo del Web dei Dati. Dati semantici presenti nei Linked Data consentono la progettazione di una nuova generazione di applicazioni e servizi basati sulla conoscenza. Questa tesi indaga su una serie di linee di ricerca nel campo dei Sistemi di Raccomandazione che utilizzano i Linked Data, con un focus sui diversi fattori di qualità delle raccomandazioni, e non solo sulla precisione. In particolare, proponiamo nuovi metodi per personalizzare la diversificazione delle liste di raccomandazioni considerando diverse attributi descrittivi degli oggetti, e un nuovo metodo per sfruttare le informazioni temporali nella diversificazione di tipo intent-aware. Inoltre, indaghiamo l'uso di dati semantici e di informazione cross-domain per affrontare il problema degli utenti in fase cold-start. Infine, mettiamo a confronto diverse metriche di similarità semantica e database Linked Data per valutare le loro prestazioni nei sistemi di raccomandazione content-based. I risultati sperimentali, mostrati e discussi in questa tesi, sostengono la validità dei nostri contributi e delle analisi.
Recommender Systems have become fundamental tools in helping users to find what is relevant for them in situations where information overload makes such task hard or even impossible. Recommender Systems are designed to suggest unknown items to the users in a personalized way, recommending those items that are most likely of interest to the users. While new algorithms and approaches have been proposed over the years mainly devoted to maximizing recommendation accuracy, recently it has been recognized that the predictive accuracy is not enough to guarantee satisfying user experience. Attention has been paid to other important quality factors such as diversity and novelty of the recommendations, and to further issues in this area, for instance the user cold start problem. At the same time, the Web has evolved from a global information space of linked documents to a Web of Data. The Linked Data initiative born in order to provide a standardized set of best practices for publishing and connecting structured data on the Web, has played a fundamental role in the development of the Web of Data. Semantic data in the Linked Data sources enable the design of new generation of knowledge-driven applications and services. This thesis investigates a set of research lines in the field of Recommender Systems using Linked Data with a focus on different quality dimensions of recommendations, besides accuracy. Specifically, we propose new methods for personalizing the diversification of list of recommendations over different item dimensions, and a new method for exploiting temporal information in intent-aware diversification. Moreover, we investigate the use of semantic data and cross-domain information for tackling the user cold-start problem. Finally, we compare different semantic similarity metrics and Linked Data sources to assess their performance in feeding content-based recommender systems. Experimental results, showed and discussed in this thesis, support the validity of our contributions and analyses.

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Benlizidia, Sihem. "LORESA : un système de recommandation d'objets d'apprentissage basé sur les annotations sémantiques." Thèse, 2007. http://hdl.handle.net/1866/7234.

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Torres, Diego. "Co-evolución entre la Web Social y la Web Semántica." Tesis, 2014. http://hdl.handle.net/10915/41223.

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La Web Social y la Web Semántica han impactado en la forma en que la creación de conocimiento se ha llevado a cabo en la Web. La Web Social promociona la participación de los usuarios para crear y editar contenido y conocimiento en la Web. La proliferación de contenido y la necesidad de tener una administración automatizada de esta información disparó la aparición de la Web Semántica. Actualmente, la Web Social y la Web Semántica conviven y comparten un mismo tema: un mejor manejo del conocimiento. Sin embargo, la mayoría de la información en la Web Social no es parte de la Web Semántica, y la información de la Web Semántica no es utilizada para mejorar a la Web Social. Esta tesis presenta un enfoque innovador para estimular una co-evolución entre la Web Semántica y la Web Social: las fuerzas que impulsan la Web Social y las herramientas que llevan a cabo la Web Semántica trabajando en conjunto con el fin de tener beneficios mutuos. En este trabajo afirmamos que la co-evolución entre la Web Social y la Web Semántica mejorará la generación de información semántica en la Web Semántica, y mejorará la producción de conocimiento en la Web Social. Esto invita a responder las siguientes preguntas: ¿Cómo puede incluirse la generación de datos semánticos en las actividades de los usuarios de la Web Social? ¿Como puede definirse la semántica de un recurso web en un entorno social? ¿Cómo puede inyectarse en la Web Social las nuevas piezas de información extraídas de la Web Semántica? ¿Poseen las comunidades de la Web Social convenciones generales que deban ser respetadas? Con el fin de mejorar la Web Semántica con las fuerzas de la Web Social, en este trabajo se proponen dos enfoques de Social Semantic Tagging: P-Swooki que permite a usuarios de una wiki semántica gestionar anotaciones semánticas permitiendo completar el proceso de construcción de conocimiento, y Semdrops que permite a los usuarios describir en forma semántica cualquier recurso de la Web tanto en un espacio de conocimiento personal como en un espacio compartido. Además, con el fin de mejorar el contenido de la Web Social, proponemos BlueFinder: un sistema de recomendación que detecta y recomienda la mejor manera de representar en un sitio de la Web Social, información que es extraída de la Web Semántica. En particular, BlueFinder recomienda la manera de representar una propiedad semántica de DBpedia en Wikipedia, respetando las convenciones de la comunidad de usuarios de Wikipedia.
Tesis realizada en co-tutela con la Universidad de Nantes (Francia). Director de tesis por la Universidad de Nantes: Pascal Molli; co-director de tesis por la Universidad de Nantes: Hala Skaf-Molli. Grado alcanzado por la Universidad de Nantes: Docteur de l'Université de Nantes.

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Bellini, Vito. "Semantics-Aware Autoencoder." Doctoral thesis, 2020. http://hdl.handle.net/11589/191073.

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Recommender Systems are widely adopted nowadays in many services, such as e-commerce websites, content streaming platforms for both music, videos, or just news. They aim to help users to find what they look for by filtering only the relevant content to them in a personalized fashion since every user has its tastes. Over the years, several algorithms have been developed to solve the recommendation problem. Very recently, we assisted in the rise of Deep Learning, which had been able to outperform many state-of-the-art machine learning algorithms. On the other hand, even though deep learning is very effective for this problem, it is hard to explain as the model is not interpretable. In this thesis, we present SemAuto, a novel deep learning interpretable architecture that can explain its outputs, and that can be used to generate an explanation for the provided recommendation. We evaluated our semantics-aware approach with respect to other state-of-the-art algorithms to prove the recommendation's accuracy effectiveness. Furthermore, we performed an extensive A/B test with real users to evaluate the explanation we generate.

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Liang, Dawen. "Understanding Music Semantics and User Behavior with Probabilistic Latent Variable Models." Thesis, 2016. https://doi.org/10.7916/D8TH8MZP.

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Bayesian probabilistic modeling provides a powerful framework for building flexible models to incorporate latent structures through likelihood model and prior. When we specify a model, we make certain assumptions about the underlying data-generating process with respect to these latent structures. For example, the latent Dirichlet allocation (LDA) model assumes that when generating a document, we first select a latent topic and then select a word that often appears in the selected topic. We can uncover the latent structures conditioned on the observed data via posterior inference. In this dissertation, we apply the tools of probabilistic latent variable models and try to understand complex real-world data about music semantics and user behavior. We first look into the problem of automatic music tagging -- inferring the semantic tags (e.g., "jazz'', "piano'', "happy'', etc.) from the audio features. We treat music tagging as a matrix completion problem and apply the Poisson matrix factorization model jointly on the vector-quantized audio features and a "bag-of-tags'' representation. This approach exploits the shared latent structure between semantic tags and acoustic codewords. We present experimental results on the Million Song Dataset for both annotation and retrieval tasks, illustrating the steady improvement in performance as more data is used. We then move to the intersection between music semantics and user behavior: music recommendation. The leading performance in music recommendation is achieved by collaborative filtering methods which exploit the similarity patterns in user's listening history. We address the fundamental cold-start problem of collaborative filtering: it cannot recommend new songs that no one has listened to. We train a neural network on semantic tagging information as a content model and use it as a prior in a collaborative filtering model. The proposed system is evaluated on the Million Song Dataset and shows comparably better result than the collaborative filtering approaches, in addition to the favorable performance in the cold-start case. Finally, we focus on general recommender systems. We examine two different types of data: implicit and explicit feedback, and introduce the notion of user exposure (whether or not a user is exposed to an item) as part of the data-generating process, which is latent for implicit data and observed for explicit data. For implicit data, we propose a probabilistic matrix factorization model and infer the user exposure from data. In the language of causal analysis (Imbens and Rubin, 2015), user exposure has close connection to the assignment mechanism. We leverage this connection more directly for explicit data and develop a causal inference approach to recommender systems. We demonstrate that causal inference for recommender systems leads to improved generalization to new data. Exact posterior inference is generally intractable for latent variables models. Throughout this thesis, we will design specific inference procedure to tractably analyze the large-scale data encountered under each scenario.

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Dissertations / Theses on the topic 'Semantic Recommender'

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