Top-N recommender system via matrix completion

Zhao Kang, Chong Peng, Qiang Cheng

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

93 Scopus citations

Abstract

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.

Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
Pages179-185
Number of pages7
ISBN (Electronic)9781577357605
StatePublished - 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States
CityPhoenix
Period2/12/162/17/16

Bibliographical note

Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

ASJC Scopus subject areas

  • Artificial Intelligence

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