Harnessing the power of "favorites" lists for recommendation systems

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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information Filtering General Terms Algorithms, Performance, Experimentation Keywords Recommender Systems, Collaborative Fil...

• Type: Conference
• Year: 2009
• Where: RECSYS
• Authors: Maryam Khezrzadeh, Alex Thomo, William W. Wadge
• Keywords: Approach Assumes Associations, Bayesian Rating, Collaborative Recommendation Approach, Control Systems, RECSYS 2009