Browsing by Author "Jian, Ciao-Ting"

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Integrating collaborative topic modeling and diversity for movie recommendations during news browsing
(1970-01-01)
Liu, Duen-Ren
;
Chou, Yun-Cheng
;
Jian, Ciao-Ting
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
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.
Online Recommendation based on Collaborative Topic Modeling and Item Diversity
(2018-01-01)
Liu, Duen-Ren
;
Chou, Yun-Cheng
;
Jian, Ciao-Ting
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
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.