| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 105 === With the rapid development of the Internet and the rise of new types of news websites with e-commerce portals, more and more users obtain specific topics online information. Successfully information recommendation to users by analyzing users’ browsing behaviors and preferences in the web-based platform can attract more users and enhance the information flow of platform, which is an important trend of the current online worlds. However, information provided by news websites is exploding and becoming more complicated. Therefore, it is an indispensable part of IT technology for e-commerce platforms to deploy appropriate online recommendation methods to improve the users’ click-through rates.
In this research, we conduct cross-domain and diversity analysis of user preferences to develop novel online movie recommendation methods and evaluated online recommendation results. Specifically, association rule mining is conducted on user browsing news and moves to find the latent associations between news and movies. A novel online recommendation approach is proposed to predict user preferences for movies based on Latent Dirichlet Allocation, Collaborative Topic Modeling and the diversity of recommendations. The experimental results show that the proposed approach can improve the cold-start problem and enhance the click-through rate of movies.
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