CoFiSet: Collaborative filtering via learning pairwise preferences over item-sets

Weike Pan, Li CHEN

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

38 Citations (Scopus)

Abstract

Collaborative filtering aims to make use of users' feedbacks to improve the recommendation performance, which has been deployed in various industry recommender systems. Some recent works have switched from exploiting explicit feedbacks of numerical ratings to implicit feedbacks like browsing and shopping records, since such data are more abundant and easier to collect. One fundamental challenge of leveraging implicit feedbacks is the lack of negative feedbacks, because there are only some observed relatively "positive" feedbacks, making it difficult to learn a prediction model. Previous works address this challenge via proposing some pointwise or pairwise preference assumptions on items. However, such assumptions with respect to items may not always hold, for example, a user may dislike a bought item or like an item not bought yet. In this paper, we propose a new and relaxed assumption of pairwise preferences over item-sets, which defines a user's preference on a set of items (item-set) instead of on a single item. The relaxed assumption can give us more accurate pairwise preference relationships. With this assumption, we further develop a general algorithm called CoFiSet (collaborative filtering via learning pairwise preferences over item-sets). Experimental results show that CoFiSet performs better than several state-of-the-art methods on various ranking-oriented evaluation metrics on two real-world data sets. Furthermore, CoFiSet is very efficient as shown by both the time complexity and CPU time.

Original languageEnglish
Title of host publicationProceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
EditorsJoydeep Ghosh, Zoran Obradovic, Jennifer Dy, Zhi-Hua Zhou, Chandrika Kamath, Srinivasan Parthasarathy
PublisherSiam Society
Pages180-188
Number of pages9
ISBN (Electronic)9781611972627
DOIs
Publication statusPublished - 2013
EventSIAM International Conference on Data Mining, SDM 2013 - Austin, United States
Duration: 2 May 20134 May 2013

Publication series

NameProceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

Conference

ConferenceSIAM International Conference on Data Mining, SDM 2013
Country/TerritoryUnited States
CityAustin
Period2/05/134/05/13

Scopus Subject Areas

  • Computer Science Applications
  • Software
  • Theoretical Computer Science
  • Information Systems
  • Signal Processing

User-Defined Keywords

  • Collaborative filtering
  • Implicit feedbacks
  • Pairwise preferences over item-sets

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