Online news recommendations based on topic modeling and online interest adjustment

Duen Ren Liu*, Yu Shan Liao, Jun Yi Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Purpose: Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms. Design/methodology/approach: A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing. Findings: This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation. Originality/value: The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

Original languageEnglish
Pages (from-to)1802-1818
Number of pages17
JournalIndustrial Management and Data Systems
Issue number8
StatePublished - 9 Sep 2019


  • Collaborative topic modelling
  • Online recommendation
  • Recommendation adjustment
  • Recommendation system
  • Semantic latent topic analysis


Dive into the research topics of 'Online news recommendations based on topic modeling and online interest adjustment'. Together they form a unique fingerprint.

Cite this