On using category experts for improving the performance and accuracy in recommender systems

Won Seok Hwang, Ho Jong Lee, Sang Wook Kim, Minsoo Lee

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

13 Scopus citations

Abstract

A variety of recommendation methods have been proposed to satisfy the performance and accuracy; however, it is fairly difficult to satisfy both of them because there is a trade-off between them. In this paper, we introduce the notion of category experts and propose the recommendation method by exploiting the ratings of category experts instead of those of the users similar to a target user. We also extend the method that uses both the category preference of a target user and his/her similarity to category experts. We show that our method significantly outperforms the existing methods in terms of performance and accuracy through extensive experiments with real-world data.

Original languageEnglish
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages2355-2358
Number of pages4
DOIs
StatePublished - 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: 29 Oct 20122 Nov 2012

Publication series

NameACM International Conference Proceeding Series

Conference

Conference21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/TerritoryUnited States
CityMaui, HI
Period29/10/122/11/12

Keywords

  • collaborative filtering
  • expert
  • performance evaluation
  • recommender system

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