Online Recommendation Based on Collaborative Topic Modeling and Item Diversity

Duen-Ren Liu, Yun Cheng Chou, Ciao Ting Jian

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

Abstract

Online news websites provide diverse article topics, such as fashion news, entertainment and movie articles to attract more users and create more benefits. Analyzing users' browsing behaviors and preferences to provide online recommendations is an important trend for online news websites. In this research, we propose a novel online recommendation method for recommending movie articles to users when they are browsing the news. Specifically, association rule mining is conducted on user browsing news and movies to find the latent associations between news and movies. A novel online recommendation approach is proposed based on Latent Dirichlet Allocation, enhanced Collaborative Topic Modeling and the diversity of recommendations. We evaluate the proposed approach via an online evaluation on a real news website. The online evaluation results show that our proposed approach can enhance the click-through rate for recommending movie articles and alleviate the cold-start issue.

Original languageEnglish
Title of host publicationProceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7-12
Number of pages6
ISBN (Electronic)9781538674475
DOIs
StatePublished - 8 Jul 2018
Event7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018 - Yonago, Japan
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018

Conference

Conference7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
CountryJapan
CityYonago
Period8/07/1813/07/18

Keywords

  • Collaborative Topic Modeling
  • Diversity
  • Latent Topic Model
  • Online Recommendation
  • Recommendation

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