Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Community gravity: measuring bidirectional effects by trust and rating on online social networks
Proceedings of the 18th international conference on World wide web
Trust based recommender system for the semantic web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
mTrust: discerning multi-faceted trust in a connected world
Proceedings of the fifth ACM international conference on Web search and data mining
Learning personal + social latent factor model for social recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring social influence for recommendation: a generative model approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Social contextual recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
We study the problem of social recommendation incorporating topic mining and social trust analysis. Different from other works related to social recommendation, we merge topic mining and social trust analysis techniques into recommender systems for finding topics from the tags of the items and estimating the topic-specific social trust. We propose a probabilistic matrix factorization (TTMF) algorithm and try to enhance the recommendation accuracy by utilizing the estimated topic-specific social trust relations. Moreover, TTMF is also convenient to solve the item cold start problem by inferring the feature (topic) of new items from their tags. Experiments are conducted on three different data sets. The results validate the effectiveness of our method for improving recommendation performance and its applicability to solve the cold start problem.