Exploiting Various Implicit Feedback for Collaborative Filtering


Title Exploiting Various Implicit Feedback for Collaborative Filtering
ByoungJu Yang, Sangkeun Lee, Sungchan Park, Sang-goo Lee
Year 2012 / 4
Keywords Implicit feedback, User behavior, Recommender system, Rating function
Acknowledgement SRC
Publication Type International Conference
Publication Proceedings of the 21st International Conference on World Wide Web 2012 (WWW 2012), pp. 639-640
Link url doi


So far, many researchers have worked on recommender systems using users’ implicit feedback, since it is difficult to collect explicit item preferences in most applications. Existing researches generally use a pseudo-rating matrix by adding up the number of item consumption; however, this naive approach may not capture user preferences correctly in that many other important user activities are ignored. In this paper, we show that users’ diverse implicit feedbacks can be significantly used to improve recommendation accu- racy. We classify various users’ behaviors (e.g., search item, skip, add to playlist, etc.) into positive or negative feedback groups and construct more accurate pseudo-rating matrix. Our preliminary experimental result shows significant potential of our approach. Also, we bring out a question to the previous approaches, aggregating item usage count into ratings.