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Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

Duen-Ren Liu (Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan)
Yun-Cheng Chou (Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan)
Ciao-Ting Jian (Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan)

Kybernetes

ISSN: 0368-492X

Article publication date: 9 January 2020

Issue publication date: 22 October 2020

Abstract

Purpose

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Design/methodology/approach

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

Findings

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Originality/value

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

Keywords

Acknowledgements

This research was supported by the Ministry of Science and Technology of Taiwan under Grant No. 105-2410-H-009-033-MY3. This research was conducted in collaboration with NIUSNEWS (www.niusnews.com/).

Citation

Liu, D.-R., Chou, Y.-C. and Jian, C.-T. (2020), "Integrating collaborative topic modeling and diversity for movie recommendations during news browsing", Kybernetes, Vol. 49 No. 11, pp. 2633-2649. https://doi.org/10.1108/K-08-2019-0578

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited