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dc.contributor.authorAbdi, Mohamed Husseinen
dc.contributor.authorOkeyo, Georgeen
dc.contributor.authorMwangi, Ronald Waweruen
dc.date.accessioned2018-12-11T14:58:11Z
dc.date.available2018-12-11T14:58:11Z
dc.date.issued2018-01-24
dc.identifier.citationAbdi, M.H., Okeyo, G., Mwangi, R.W. (2018) Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey. Computer and Information Science, 11(2),en
dc.identifier.issn1913-8989
dc.identifier.urihttp://hdl.handle.net/2086/17333
dc.descriptionopen access articleen
dc.description.abstractCollaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.en
dc.language.isoenen
dc.publisherCanadian Center of Science and Educationen
dc.subjectCollaborative filteringen
dc.subjectContext- awareen
dc.subjectMatrix factorizationen
dc.subjectPerformance improvemenen
dc.subjectAccuracy of predictionsen
dc.subjectQuality of recommendationsen
dc.titleMatrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Surveyen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.5539/cis.v11n2p1
dc.researchgroupCyber Technology Institute (CTI)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC BYen
dc.date.acceptance2018-01-24en
dc.exception.ref2021codes252cen


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