Online Product Recommendations based on Diversity and Latent Association Analysis on News and Products

Hsing Yu Chen, Yu Chun Lin, Duen Ren Liu*, Tzeng Feng Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Integrating news websites with product recommendation can create more benefit and is an important trend of online worlds. The information offered by the websites is becoming even more complicated. Accordingly, it is important for the websites to implement online recommendation methods that can raise the users’ click-through rates and loyalty. In this work, we proposed a novel online product recommendation approach for recommending products during news browsing. The proposed method combines online hybrid interest analysis and recommendation diversity. There are cold-start and data sparsity issues on the website. Accordingly, a hybrid of collaborative filtering and content-based approach is used to alleviate the issues. Specifically, latent association analysis is conducted on user browsing news and products to discover the latent associations between products and news. Moreover, a hybrid method is proposed based on Matrix Factorization and Latent Topic Modeling to predict user preferences for products. In addition, online interest analysis is integrated to adjust users’ online product interests according to the currently browsing news. Finally, the proposed approach combines recommendation diversity and users’ online interests to raise the chance of discovering potential user preferences on products and enhance the click through rate of online product recommendations. Online evaluations are conducted on a news website to evaluate the proposed approach. Our online experimental results indicate that the proposed approach can enhance the click-through rate of online product recommendations.

Original languageEnglish
Pages (from-to)1065-1085
Number of pages21
JournalJournal of Information Science and Engineering
Issue number5
StatePublished - Sep 2022


  • diversity
  • latent topic modeling
  • matrix factorization
  • online recommendation
  • product recommendation


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