Proceedings of the Fourth ACM International Conference on Web Search and Data Mining 2011
DOI: 10.1145/1935826.1935877
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Recommender systems with social regularization

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Cited by 1,359 publications
(948 citation statements)
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References 32 publications
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“…Keywords: recommender system, context-awareness, collaborative ranking, behavior modeling the scenarios with user explicit feedbacks [6,7], or cast the recommendation task as user adoption predictions in the scenarios without explicit feedbacks [4,9].…”
Section: Related Workmentioning
confidence: 99%
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“…Keywords: recommender system, context-awareness, collaborative ranking, behavior modeling the scenarios with user explicit feedbacks [6,7], or cast the recommendation task as user adoption predictions in the scenarios without explicit feedbacks [4,9].…”
Section: Related Workmentioning
confidence: 99%
“…Social information is utilized to better shape the user latent space typically. For example, [6] makes a recommendation by adding additional social regularization terms in MF to constrain the user latent feature vectors to be similar to his or her friends' average latent features. [12] proposes a category-specific social trust influence weight which outlines several variants of weighting friends within circles based on their inferred expertise levels.…”
Section: Related Workmentioning
confidence: 99%
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“…As such, the proximity measures by using random walks have clear physical meanings than other latent methods [27,33].…”
Section: Problem Formulationmentioning
confidence: 99%
“…(Soo, 2004) [11] Proposed a prototype design for building a personalized recommender system to recommend travel related information according to users' contextual information. (M.-H. Park, 2007) [9] Proposed a location based personalized recommender system, which can reflect users' personal preferences by modeling user contextual information through Bayesian Networks.(Hao Ma) [17] Analyse latent factor using probabilistic matrix factorization, we learn the user latent feature space and item latent feature space by employing a user social network and a user-item matrix simultaneously and seamlessly. (H. Ma, 2011) [22] Proposed a matrix factorization framework with social regularization.…”
mentioning
confidence: 99%