Дисертації з теми "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.
Повний текст джерелаMENDONCA, 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.
Повний текст джерелаRecommender 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.
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.
Повний текст джерелаWe 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.
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.
Повний текст джерелаEsta 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.
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.
Повний текст джерелаJoseph, 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.
Повний текст джерелаAkther, Aysha. "Social Tag-based Community Recommendation Using Latent Semantic Analysis." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23238.
Повний текст джерелаFIGUEROA, MARTINEZ CRISTHIAN NICOLAS. "Recommender Systems based on Linked Data." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2669963.
Повний текст джерелаPALUMBO, ENRICO. "Knowledge Graph Embeddings for Recommender Systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850588.
Повний текст джерелаSundaramurthy, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаMartínez, García Miriam. "Enhancing the ELECTRE decision support method with semantic data." Doctoral thesis, Universitat Rovira i Virgili, 2018. http://hdl.handle.net/10803/665102.
Повний текст джерелаTomar 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.
Li, Siying. "Context-aware recommender system for system of information systems." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.
Повний текст джерелаWorking 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
VAGLIANO, IACOPO. "Content Recommendation Through Linked Data." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2670692.
Повний текст джерелаLully, 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.
Повний текст джерелаThis 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
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.
Повний текст джерелаCurrently, 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.
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.
Повний текст джерелаWerner, 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.
Повний текст джерелаEffective 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
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.
Повний текст джерелаOur 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
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.
Повний текст джерелаMade available in DSpace on 2015-10-21T12:12:38Z (GMT). No. of bitstreams: 1 Marcos de Meira Góis_.pdf: 1916593 bytes, checksum: e5b2eae456a204d1173418cd2ed3480f (MD5) Previous issue date: 2015-08-04
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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.
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.
Повний текст джерелаCoordenação 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%.
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.
Повний текст джерелаLisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.
Повний текст джерелаRepresenting 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
Lemdani, Roza. "Système hybride d'adaptation dans les systèmes de recommandation." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC050/document.
Повний текст джерелаRecommender 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
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.
Повний текст джерелаThe 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.
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.
Повний текст джерелаIn 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.
Patel, Namrata. "Mise en œuvre des préférences dans des problèmes de décision." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT286/document.
Повний текст джерелаIntelligent ‘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
"CodeReco - A Semantic Java Method Recommender." Master's thesis, 2017. http://hdl.handle.net/2286/R.I.44982.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Science 2017
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.
Повний текст джерела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.
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.
Повний текст джерела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.
Dewabharata, Anindhita, and Anindhita Dewabharata. "A Design of Semantic-based Recommender System for Medical Tourism." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/99416214431752574139.
Повний текст джерела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.
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.
Повний текст джерела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.
Anelli, Vito Walter. "Knowledge-Enabled Recommender Systems in the Linked Data Era." Doctoral thesis, 2020. http://hdl.handle.net/11589/191260.
Повний текст джерелаRecommender 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.
TOMEO, Paolo. "Beyond Accuracy in Recommender Systems under the Linked Data lens." Doctoral thesis, 2017. http://hdl.handle.net/11589/98558.
Повний текст джерелаRecommender 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.
Benlizidia, Sihem. "LORESA : un système de recommandation d'objets d'apprentissage basé sur les annotations sémantiques." Thèse, 2007. http://hdl.handle.net/1866/7234.
Повний текст джерелаTorres, Diego. "Co-evolución entre la Web Social y la Web Semántica." Tesis, 2014. http://hdl.handle.net/10915/41223.
Повний текст джерелаTesis 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.
Bellini, Vito. "Semantics-Aware Autoencoder." Doctoral thesis, 2020. http://hdl.handle.net/11589/191073.
Повний текст джерелаLiang, Dawen. "Understanding Music Semantics and User Behavior with Probabilistic Latent Variable Models." Thesis, 2016. https://doi.org/10.7916/D8TH8MZP.
Повний текст джерела