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dc.contributor.authorZhang, Chuxu
dc.contributor.authorYu, Lu
dc.contributor.authorWang, Yan
dc.contributor.authorShah, Chirag
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2017-06-19T09:21:45Z
dc.date.available2017-06-19T09:21:45Z
dc.date.issued2017-06-09
dc.identifier.citationZhang C, Yu L, Wang Y, Shah C, Zhang X (2017) Collaborative User Network Embedding for Social Recommender Systems. Proceedings of the 2017 SIAM International Conference on Data Mining: 381–389. Available: http://dx.doi.org/10.1137/1.9781611974973.43.
dc.identifier.doi10.1137/1.9781611974973.43
dc.identifier.urihttp://hdl.handle.net/10754/625053
dc.description.abstractTo address the issue of data sparsity and cold-start in recommender system, social information (e.g., user-user trust links) has been introduced to complement rating data for improving the performances of traditional model-based recommendation techniques such as matrix factorization (MF) and Bayesian personalized ranking (BPR). Although effective, the utilization of the explicit user-user relationships extracted directly from such social information has three main limitations. First, it is difficult to obtain explicit and reliable social links. Only a small portion of users indicate explicitly their trusted friends in recommender systems. Second, the “cold-start” users are “cold” not only on rating but also on socializing. There is no significant amount of explicit social information that can be useful for “cold-start” users. Third, an active user can be socially connected with others who have different taste/preference. Direct usage of explicit social links may mislead recommendation. To address these issues, we propose to extract implicit and reliable social information from user feedbacks and identify top-k semantic friends for each user. We incorporate the top-k semantic friends information into MF and BPR frameworks to solve the problems of ratings prediction and items ranking, respectively. The experimental results on three real-world datasets show that our proposed approaches achieve better results than the state-of-the-art MF with explicit social links (with 3.0% improvement on RMSE), and social BPR (with 9.1% improvement on AUC).
dc.description.sponsorshipResearch reported in this publication was partially supported by the US Institute of Museum and Library Services (IMLS) National Leadership Grant #LG-81-16-0025, and the King Abdullah University of Science and Technology (KAUST).
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)
dc.relation.urlhttp://epubs.siam.org/doi/10.1137/1.9781611974973.43
dc.rightsArchived with thanks to Proceedings of the 2017 SIAM International Conference on Data Mining
dc.subjectSocial Recommender Systems
dc.subjectNetwork Embedding
dc.subjectTop-k Semantic Friends
dc.subjectMatrix Factorization
dc.subjectBayesian Personalized Ranking
dc.titleCollaborative User Network Embedding for Social Recommender Systems
dc.typeBook Chapter
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalProceedings of the 2017 SIAM International Conference on Data Mining
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionRutgers University, USA.
kaust.personYu, Lu
kaust.personZhang, Xiangliang
refterms.dateFOA2018-06-13T14:40:38Z
dc.date.published-online2017-06-09
dc.date.published-print2017-06-30


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