News Recommendation Based on Collaborative Semantic Topic Models and Recommendation Adjustment

Yu Shan Liao, Jun Yi Lu, Duen Ren Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Providing news recommendations is an important trend for online news websites to attract more users and create more benefits. In this research, we propose a novel recommendation approach for recommending news articles. We propose A Collaborative Semantic Topic Model and an ensemble model to predict user preferences based on combining Matrix Factorization with articles' semantic latent topics derived from word embedding and Latent Dirichlet Allocation. The proposed ensemble model is further integrated with a recommendation adjustment mechanism to adjust users' online recommendation lists. We evaluate the proposed approach via offline experiments and online evaluation on a real news website. The experimental result demonstrates that our proposed approach can improve the recommendation quality of recommending news articles.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728128160
DOIs
StatePublished - Jul 2019
Event18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan
Duration: 7 Jul 201910 Jul 2019

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2019-July
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
CountryJapan
CityKobe
Period7/07/1910/07/19

Keywords

  • Collaborative topic model
  • Latent topic analysis
  • Recommendation
  • Recommendation adjustment

Fingerprint Dive into the research topics of 'News Recommendation Based on Collaborative Semantic Topic Models and Recommendation Adjustment'. Together they form a unique fingerprint.

  • Cite this

    Liao, Y. S., Lu, J. Y., & Liu, D. R. (2019). News Recommendation Based on Collaborative Semantic Topic Models and Recommendation Adjustment. In Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019 [8949259] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICMLC48188.2019.8949259