Harnessing the power of "favorites" lists for recommendation systems

• Main Author: Khezrzadeh, Maryam
• Other Authors: Thomo, Alex
• Language: English; en
• Published: 2010
• Subjects: Recommendation system; Association analysis; Amazon; Frequency; Bayesian rating; Collaborative filtering; CIRC; UVic Subject Index::Sciences and Engineering::Applied Sciences::Computer science
• Online Access: http://hdl.handle.net/1828/2047

This thesis proposes a novel 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 consider two different ways to calculate the strength of item-item associations: frequency of co-occurrence, and sum of Bayesian ratings (SBR) of all lists containing the item pair. The latter 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 Mean Absolute Error (MAE), coverage, precision and recall.