Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Incremental collaborative filtering for highly-scalable recommendation algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Item-Based and User-Based Incremental Collaborative Filtering for Web Recommendations
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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The use of collaborative filtering (CF) recommenders on the Web is typically done in environments where data is constantly flowing. In this paper we propose an incremental version of item-based CF for implicit binary ratings, and compare it with a non-incremental one, as well as with an incremental user-based approach. We also study the usage of sparse matrices in these algorithms. We observe that recall and precision tend to improve when we continuously add information to the recommender model, and that the time spent for recommendation does not degrade. Time for updating the similarity matrix is relatively low and motivates the use of the item-based incremental approach.