Top-N recommendation with novel rank approximation

Zhao Kang, Qiang Cheng

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

5 Scopus citations

Abstract

The importance of accurate recommender systems has been widely recognized by academia and industry. However, the recommendation quality is still rather low. Recently, a linear sparse and low-rank representation of the user-item matrix has been applied to produce Top-N recommendations. This approach uses the nuclear norm as a convex relaxation for the rank function and has achieved better recommendation accuracy than the state-of-the-art methods. In the past several years, solving rank minimization problems by leveraging nonconvex relaxations has received increasing attention. Some empirical results demonstrate that it can provide a better approximation to original problems than convex relaxation. In this paper, we propose a novel rank approximation to enhance the performance of Top-N recommendation systems, where the approximation error is controllable. Experimental results on real data show that the proposed rank approximation improves the Top-N recommendation accuracy substantially.

Original languageEnglish
Title of host publication16th SIAM International Conference on Data Mining 2016, SDM 2016
EditorsSanjay Chawla Venkatasubramanian, Wagner Meira
Pages126-134
Number of pages9
ISBN (Electronic)9781510828117
DOIs
StatePublished - 2016
Event16th SIAM International Conference on Data Mining 2016, SDM 2016 - Miami, United States
Duration: May 5 2016May 7 2016

Publication series

Name16th SIAM International Conference on Data Mining 2016, SDM 2016

Conference

Conference16th SIAM International Conference on Data Mining 2016, SDM 2016
Country/TerritoryUnited States
CityMiami
Period5/5/165/7/16

Bibliographical note

Publisher Copyright:
Copyright © by SIAM.

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Fingerprint

Dive into the research topics of 'Top-N recommendation with novel rank approximation'. Together they form a unique fingerprint.

Cite this