Document recommendation with implicit feedback based on matrix factorization and topic model

Chin Hui Lai, Duen-Ren Liu, Siao Rong Lin

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

Abstract

Recommender systems have been applied in many domains to solve the information-overload problem, and most of them make recommendations based on explicit data which expressed ratings in different scores. However, there are a lot of implicit data in the real world, such as users' purchase history, click history, browsing activity and so on, and it is difficult to find users' preferences based on this kind of data. In this work, we proposed a novel recommendation method, which incorporates topic model and matrix factorization. The content of documents and similar users' preferences are used to predict the negative and positive examples. The proposed approach achieves better performance than other recommender systems with implicit feedback.

Original languageEnglish
Title of host publicationProceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
EditorsArtde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages62-65
Number of pages4
ISBN (Electronic)9781538643426
DOIs
StatePublished - 22 Jun 2018
Event4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, Japan
Duration: 13 Apr 201817 Apr 2018

Publication series

NameProceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

Conference

Conference4th IEEE International Conference on Applied System Innovation, ICASI 2018
CountryJapan
CityChiba
Period13/04/1817/04/18

Keywords

  • Implicit Feedback
  • Latent Dirichlet Allocation
  • Matrix Factorization
  • Recommender System
  • Topic Modeling

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