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dc.contributor.authorMa, Hao
dc.contributor.authorYang, Haixuan
dc.contributor.authorLyu, Michael R.
dc.contributor.authorKing, Irwin
dc.identifier.citationMa, Hao, Yang, Haixuan, Lyu, Michael R., & King, Irwin. (2008). SoRec: social recommendation using probabilistic matrix factorization. Paper presented at the Proceedings of the 17th ACM conference on Information and knowledge management, Napa Valley, California, USA, DOI: 10.1145/1458082.1458205en_US
dc.descriptionConference paperen_US
dc.description.abstractData sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system confronts. Many existing approaches to recommender systems can neither handle very large datasets nor easily deal with users who have made very few ratings or even none at all. Moreover, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the social interactions or connections among users. In view of the exponential growth of information generated by online social networks, social network analysis is becoming important for many Web applications. Following the intuition that a person's social network will affect personal behaviors on the Web, this paper proposes a factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results shows that our method performs much better than the state-of-the-art approaches, especially in the circumstance that users have made few or no ratings.en_US
dc.relation.ispartofProceeding of the 17th ACM conference on Information and knowledge managementen
dc.subjectSocial recommendationen_US
dc.titleSorec: social recommendation using probabilistic matrix factorizationen_US
dc.typeConference Paperen_US
dc.local.contactHaixuan Yang, School Of Mathematics,Statistics, & Applied Mathematics, Adb-G013, Nui Galway. 2320 Email:

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