GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
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We propose a novel collaborative recommendation approach to take advantage of the information available in user-created lists. Our approach assumes associations among any two items appearing in a list together. We calculate sum of Bayesian ratings (SBR) of all lists containing an item pair as the strength of item-item associations in that pair. SBR takes into consideration not only the number of lists the items have co-appeared in, but also the quality of the lists. We collected a data set of user ratings for books along with Listmania lists on Amazon.com using Amazon Web Services (AWS). Our method shows superior performance to existing user-based and item-based collaborative filtering approaches according to the resulted MAE, coverage and F-measure.