Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
The effect of correlation coefficients on communities of recommenders
Proceedings of the 2008 ACM symposium on Applied computing
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
Impact of data characteristics on recommender systems performance
ACM Transactions on Management Information Systems (TMIS)
Cross-domain collaborative filtering over time
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A simple but effective method to incorporate trusted neighbors in recommender systems
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Knowledge-Based Systems
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Collaborative filtering, a widely-used user-centric recommendation technique, predicts an item's rating by aggregating its ratings from similar users. User similarity is usually calculated by cosine similarity or Pearson correlation coefficient. However, both of them consider only the direction of rating vectors, and suffer from a range of drawbacks. To solve these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. Further, our principled method reduces correlation due to chance. Experimental results on six real-world data sets show that our method achieves superior accuracy.