Exploiting various implicit feedback for collaborative filtering

Byoungju Yang, Sangkeun Lee, Sungchan Park, Sang Goo Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

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 the previous approaches, aggregating item usage count into ratings. Copyright is held by the author/owner(s).

Original languageEnglish
Title of host publicationWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
Pages639-640
Number of pages2
DOIs
StatePublished - 2012
Externally publishedYes
Event21st Annual Conference on World Wide Web, WWW'12 - Lyon, France
Duration: Apr 16 2012Apr 20 2012

Publication series

NameWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion

Conference

Conference21st Annual Conference on World Wide Web, WWW'12
Country/TerritoryFrance
CityLyon
Period04/16/1204/20/12

Keywords

  • Implicit feedback
  • Rating function
  • Recommender system
  • User behavior

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