Categories and Subject Descriptors
- ISBN: 9781450300995
We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically re- lies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the partic- ular item. Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals’ preferences for past events, combined collabo- ratively with other peoples’ likes and dislikes. We examine the topic of unseen item recommendation through a user study of academic (scientific) talk recommendation, where we aim to correctly estimate a ranking function for each user, predicting which talks would be of most interest to them. Then by decomposing user parameters into shared and in- dividual dimensions, we induce a similarity metric between users based on the degree to which they share these dimen- sions. We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation. Finally, to further reduce the need for ex- plicit user feedback, we suggest an active learning approach for eliciting feedback and a method for incorporating avail- able implicit user cues.