Fab: content-based, collaborative recommendation
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
The Journal of Machine Learning Research
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
FeedbackTrust: using feedback effects in trust-based recommendation systems
Proceedings of the third ACM conference on Recommender systems
Probabilistic latent preference analysis for collaborative filtering
Proceedings of the 18th ACM conference on Information and knowledge management
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Who should share what?: item-level social influence prediction for users and posts ranking
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Recommending ephemeral items at web scale
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Robust nonnegative matrix factorization using L21-norm
Proceedings of the 20th ACM international conference on Information and knowledge management
Bayesian latent variable models for collaborative item rating prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SoCo: a social network aided context-aware recommender system
Proceedings of the 22nd international conference on World Wide Web
Predicting purchase behaviors from social media
Proceedings of the 22nd international conference on World Wide Web
Recommender system by grasping individual preference and influence from other users
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Using emotional context from article for contextual music recommendation
Proceedings of the 21st ACM international conference on Multimedia
AnchorMF: towards effective event context identification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Social recommendation incorporating topic mining and social trust analysis
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Scientific articles recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Recommending branded products from social media
Proceedings of the 7th ACM conference on Recommender systems
Recommendation via user's personality and social contextual
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the first ACM conference on Online social networks
Exploiting local and global social context for recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Exponential growth of information generated by online social networks demands effective recommender systems to give useful results. Traditional techniques become unqualified because they ignore social relation data; existing social recommendation approaches consider social network structure, but social context has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users' motivation of social behaviors into social recommendation. In this paper, we investigate social recommendation on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online item adoption and recommendation. Then we propose a novel probabilistic matrix factorization method to fuse them in latent spaces. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network datasets in China. The empirical result and analysis on these two large datasets demonstrate that our method significantly outperform the existing approaches.