Improving a recommender system by collective matrix factorization with tag information

Bu Sung Kim, Heera Kim, Jaedong Lee, Jee Hyong Lee

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

13 Scopus citations

Abstract

Collaborative filtering (CF) is the most widely used method of recommender systems. However, it is hard to give users reliable recommendation when there is little information about users. This is the sparsity problem of CF. In this paper, we propose a collective matrix factorization method using tag information to solve the sparsity problem. With tag information, we construct a user-tag matrix that represents users' preferences about tags. Using the user-tag matrix, we convert sparse user-item matrix into dense user-item matrix. In our method, the collective matrix factorization has the role of transferring information between the user-item matrix and user-tag matrix. We experimentally show that our method generates more precise prediction than general CF suffering from the sparsity problem.

Original languageEnglish
Title of host publication2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages980-984
Number of pages5
ISBN (Electronic)9781479959556
DOIs
StatePublished - 18 Feb 2014
Event2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 - Kitakyushu, Japan
Duration: 3 Dec 20146 Dec 2014

Publication series

Name2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014

Conference

Conference2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
Country/TerritoryJapan
CityKitakyushu
Period3/12/146/12/14

Keywords

  • collaborative filtering
  • collective matrix factorization
  • recommender system
  • sparsity problem
  • tag information

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