Exploiting various implicit feedback for collaborative filtering

Byoungju Yang, Sangkeun Lee, Sungchan Park, Sang-goo Lee
2012 Proceedings of the 21st international conference companion on World Wide Web - WWW '12 Companion  
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 naïve 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
more » ... ove recommendation accuracy. 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.
doi:10.1145/2187980.2188166 dblp:conf/www/YangLPL12 fatcat:lvcuovjyybhsbe6t5fc5db55rq