Top-N recommendation on graphs

Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng

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

17 Scopus citations

Abstract

Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.

Original languageEnglish
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
Pages2101-2106
Number of pages6
ISBN (Electronic)9781450340731
DOIs
StatePublished - Oct 24 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: Oct 24 2016Oct 28 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume24-28-October-2016

Conference

Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States
CityIndianapolis
Period10/24/1610/28/16

Bibliographical note

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

Keywords

  • Collaborative filtering
  • Laplacian graph
  • Top-N recommendation

ASJC Scopus subject areas

  • General Decision Sciences
  • General Business, Management and Accounting

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