Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2012/poster/244> ?p ?o. }
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- 244 creator byoungju-yang.
- 244 creator sang-goo-lee.
- 244 creator sangkeun-lee.
- 244 creator sungchan-park.
- 244 type InProceedings.
- 244 label "Exploiting Various Implicit Feedback for Collaborative Filtering".
- 244 sameAs 244.
- 244 abstract "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 improve 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 theprevious approaches, aggregating item usage count into ratings.".
- 244 hasAuthorList authorList.
- 244 isPartOf proceedings.
- 244 isPartOf proceedings.
- 244 keyword "Implicit feedback".
- 244 keyword "Rating function".
- 244 keyword "Recommender systems".
- 244 keyword "User behavior".
- 244 title "Exploiting Various Implicit Feedback for Collaborative Filtering".