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by Emiko Fukuda, Yoshio Kamijo, Ai Takeuchi, Michiharu Masui, Yukihiko Funaki
()

Abstract

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.

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Readership Statistics

18 Readers on Mendeley
by Discipline
 
89% Computer Science
 
6% Social Sciences
 
6% Engineering
by Academic Status
 
28% Student > Master
 
17% Researcher
 
17% Student > Ph. D. Student
by Country
 
6% France
 
6% Australia

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