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Academic literature on the topic 'Semantic Recommender'

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Published: 14 March 2023

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Journal articles on the topic "Semantic Recommender"

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Hendrik, Khamidudin Azzakiy, and Aditya Budi Utomo. "Semantic Hybrid Recommender System." Advanced Science Letters 21, no. 10 (October 1, 2015): 3363–66. http://dx.doi.org/10.1166/asl.2015.6499.

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Sulieman, Dalia, Maria Malek, Hubert Kadima, and Dominique Laurent. "Toward Social-Semantic Recommender Systems." International Journal of Information Systems and Social Change 7, no. 1 (January 2016): 1–30. http://dx.doi.org/10.4018/ijissc.2016010101.

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In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.
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Fraihat, Salam, and Qusai Shambour. "A Framework of Semantic Recommender System for e-Learning." Journal of Software 10, no. 3 (March 2015): 317–30. http://dx.doi.org/10.17706/jsw.10.3.317-330.

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Cantador, Iván, Pablo Castells, and Alejandro Bellogín. "An Enhanced Semantic Layer for Hybrid Recommender Systems." International Journal on Semantic Web and Information Systems 7, no. 1 (January 2011): 44–78. http://dx.doi.org/10.4018/jswis.2011010103.

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Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources.
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Hawashin, Bilal, Shadi Alzubi, Tarek Kanan, and Ayman Mansour. "An efficient semantic recommender method forArabic text." Electronic Library 37, no. 2 (April 1, 2019): 263–80. http://dx.doi.org/10.1108/el-12-2018-0245.

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PurposeThis paper aims to propose a new efficient semantic recommender method for Arabic content.Design/methodology/approachThree semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature.FindingsExperiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time.Originality/valueAlthough many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.
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Jaglan, G., and S. K. Malik. "Blending Semantic Web with Recommender Systems." International Journal of Computer Sciences and Engineering 6, no. 5 (May 31, 2018): 523–31. http://dx.doi.org/10.26438/ijcse/v6i5.523531.

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Vellino, Andre. "Recommending research articles using citation data." Library Hi Tech 33, no. 4 (November 16, 2015): 597–609. http://dx.doi.org/10.1108/lht-06-2015-0063.

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Purpose – The purpose of this paper is to present an empirical comparison between the recommendations generated by a citation-based recommender for research articles in a digital library with those produced by a user-based recommender (ExLibris “bX”). Design/methodology/approach – For these computer experiments 9,453 articles were randomly selected from among 6.6 M articles in a digital library as starting points for generating recommendations. The same seed articles were used to generate recommendations in both recommender systems and the resulting recommendations were compared according to the “semantic distance” between the seed articles and the recommended ones, the coverage of the recommendations and the spread in publication dates between the seed and the resulting recommendations. Findings – Out of the 9,453 test runs, the recommendation coverage was 30 per cent for the user-based recommender vs 24 per cent for the citation-based one. Only 12 per cent of seed articles produced recommendations with both recommenders and none of the recommended articles were the same. Both recommenders yielded recommendations with about the same semantic distance between the seed article and the recommended articles. The average differences between the publication dates of the recommended articles and the seed articles is dramatically greater for the citation-based recommender (+7.6 years) compared with the forward-looking user-based recommender. Originality/value – This paper reports on the only known empirical comparison between the Ex Librix “bX” recommendation system and a citation-based collaborative recommendation system. It extends prior preliminary findings with a larger data set and with an analysis of the publication dates of recommendations for each system.
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Durao, Frederico, and Peter Dolog. "Semantic Grounding Strategies for Tagbased Recommender Systems." International journal of Web & Semantic Technology 2, no. 4 (October 30, 2011): 67–79. http://dx.doi.org/10.5121/ijwest.2011.2405.

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García-Sánchez, Francisco, Ricardo Colomo-Palacios, and Rafael Valencia-García. "A social-semantic recommender system for advertisements." Information Processing & Management 57, no. 2 (March 2020): 102153. http://dx.doi.org/10.1016/j.ipm.2019.102153.

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Shu, Qiong, He Ping Chen, and Jin Guang Gu. "Semantic Reasoning-Based Chinese Recipe Recommender System." Advanced Materials Research 718-720 (July 2013): 1998–2004. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1998.

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For the blank of the recommender system for chinese recipes, this paper uses OWLS-WSDL to build a semantic reasoning-based chinese recipe recommender system. This system through the tool of Protégé to establish the ontology of chinese recipes and then add rules for ontology reasoning. On this basis bring out a catering algorithm, using Euclidean distance and Jaccard to calculate the similarity between the dishes. According to the similarity as well as user preference, provides a quick means of siding dishes for users.
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Dissertations / Theses on the topic "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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Book chapters on the topic "Semantic Recommender"

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Ziegler, Cai-Nicolas. "Semantic Web Recommender Systems." In Current Trends in Database Technology - EDBT 2004 Workshops, 78–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30192-9_8.

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Gedikli, Fatih, and Dietmar Jannach. "Recommender Systems, Semantic-Based." In Encyclopedia of Social Network Analysis and Mining, 1–11. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_116-1.

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Gedikli, Fatih, and Dietmar Jannach. "Recommender Systems, Semantic-Based." In Encyclopedia of Social Network Analysis and Mining, 1501–10. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_116.

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Gedikli, Fatih, and Dietmar Jannach. "Recommender Systems, Semantic-Based." In Encyclopedia of Social Network Analysis and Mining, 2137–47. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_116.

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Konstan, Joseph A., and John T. Riedl. "Recommender Systems for the Web." In Visualizing the Semantic Web, 151–67. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-3737-5_10.

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Maccatrozzo, Valentina, Davide Ceolin, Lora Aroyo, and Paul Groth. "A Semantic Pattern-Based Recommender." In Communications in Computer and Information Science, 182–87. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12024-9_24.

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Mirizzi, Roberto, Tommaso Di Noia, Eugenio Di Sciascio, and Azzurra Ragone. "A Recommender System for Linked Data." In Semantic Search over the Web, 311–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25008-8_12.

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Baba-Hamed, Latifa, and Magloire Namber. "Diversity in a Semantic Recommender System." In Advances in Intelligent Systems and Computing, 297–306. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01863-8_32.

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Thanh-Tai, Huynh, Huu-Hoa Nguyen, and Nguyen Thai-Nghe. "A Semantic Approach in Recommender Systems." In Future Data and Security Engineering, 331–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48057-2_23.

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Anelli, Vito Walter, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, and Felice Antonio Merra. "SAShA: Semantic-Aware Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs." In The Semantic Web, 307–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49461-2_18.

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Conference papers on the topic "Semantic Recommender"

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Wang, Rui-Qin, and Fan-Sheng Kong. "Semantic-Enhanced Personalized Recommender System." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370858.

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Neirynck, Thomas. "Semantic road networks for recommender systems." In the International Working Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2254556.2254691.

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Ge, Mouzhi, and Fabio Persia. "Research Challenges in Multimedia Recommender Systems." In 2017 IEEE 11th International Conference on Semantic Computing (ICSC). IEEE, 2017. http://dx.doi.org/10.1109/icsc.2017.31.

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Lémdani, Roza, Géraldine Polaillon, Nacéra Bennacer, and Yolaine Bourda. "A semantic similarity measure for recommender systems." In the 7th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2063518.2063545.

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Zaremarjal, Ashkan Yeganeh, and Derya Yiltas-Kaplan. "Semantic Collaborative Filtering Recommender System Using CNNs." In 2021 8th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE, 2021. http://dx.doi.org/10.1109/iceee52452.2021.9415931.

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Bafna, Prafulla, Shailaja Shirwaikar, and Dhanya Pramod. "Semantic Clustering Driven Approaches to Recommender Systems." In ACM COMPUTE '16: Ninth Annual ACM India Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2998476.2998487.

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Faralli, Stefano, Giovanni Stilo, and Paola Velardi. "A Semantic Recommender for Micro-blog Users." In 2015 IEEE International Congress on Big Data (BigData Congress). IEEE, 2015. http://dx.doi.org/10.1109/bigdatacongress.2015.18.

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Rakow, Christian, Andreas Lommatzsch, and Till Plumbaum. "Topical Semantic Recommendations for Auteur Films." In RecSys '16: Tenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2959100.2959110.

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Hou, Min, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, and Qi Liu. "Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/650.

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Abstract:
In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). Nevertheless, most previous fashion recommendation models comprehend the clothing images with a global content representation and lack detailed understanding of users' semantic preferences, which usually leads to inferior recommendation performance. To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS). Specifically, we first introduce a fine-grained interpretable semantic space. We then develop a Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA) module to project users and items into this space, respectively. With SAERS, we are capable of not only providing cloth recommendations for users, but also explaining the reason why we recommend the cloth through intuitive visual attribute semantic highlights in a personalized manner. Extensive experiments conducted on real-world datasets clearly demonstrate the effectiveness of our approach compared with the state-of-the-art methods.
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Vogiatzis, Dimitrios, and Nicolas Tsapatsoulis. "Modeling User Networks in Recommender Systems." In 2008 Third International Workshop on Semantic Media Adaptation and Personalization (SMAP). IEEE, 2008. http://dx.doi.org/10.1109/smap.2008.35.

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