Collective Matrix Factorization Using Tag Embedding for Effective Recommender System

Hanbyul Bang, Jee Hyong Lee

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

5 Scopus citations

Abstract

Many people communicate each other through online community, SNS as Instagram, Facebook, etc. Most of these services annotate on their clips or pictures by using tags, which contain some information and can describe their contents. In this paper, we propose a new recommender system using word embedding with tag information and collective matrix factorization technique. By vectorizing tags that users annotated, we make user-tag matrix by merging tag vectors and factorize it together with user-item matrix. We show that this method effectively works through experiments.

Original languageEnglish
Title of host publicationProceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages846-850
Number of pages5
ISBN (Electronic)9781467390415
DOIs
StatePublished - 28 Dec 2016
Externally publishedYes
Event8th Joint International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016 - Sapporo, Hokkaido, Japan
Duration: 25 Aug 201628 Aug 2016

Publication series

NameProceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016

Conference

Conference8th Joint International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016
Country/TerritoryJapan
CitySapporo, Hokkaido
Period25/08/1628/08/16

Keywords

  • collaborative filtering
  • collective matrix factorization
  • recommender system
  • tag
  • word embedding

Fingerprint

Dive into the research topics of 'Collective Matrix Factorization Using Tag Embedding for Effective Recommender System'. Together they form a unique fingerprint.

Cite this