| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 104 === The flourishing of the Internet has increasingly promoted the rise of new types of online news websites with e-commerce portals. Combining online news websites with e-commerce can attract more users and create more benefit, which is also an important trend of online worlds. The great amount of information provided by news websites is becoming even more complicated. Therefore, it is an important issue for online news websites to deploy appropriate online recommendation methods that can raise the users’ click-through rates and loyalty.
In our research, we conducted cross-domain and diversity analysis of user preferences to develop novel online product recommendation methods, and evaluate online recommendation results. Accordingly, by cross-domain analysis on news browsing and product purchasing, we developed methods based on Matrix Factorization and Latent Dirichlet Allocation to predict the user preferences for products. Our experimental result shows that our proposed approach can improve the cold-start problem and enhance the click-through rate of products.
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