Personalized News Recommendation by Topic Modeling for Extracting User Profiles

• Main Authors: Wu, En-Ping, 吳恩平
• Other Authors: Liu, Duen-Ren
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/77556023355296993465

碩士 === 國立交通大學 === 資訊管理研究所 === 104 === Personalized recommendation systems have become a critical service for helping users to find items which are suitable and useful for them. However, in accordance with the change of conditions, a user’s reading interests may change over time. Hence, for online news reading, it is important to recommend articles that match each user’s dynamic preferences. Moreover, most of the existing methods obtain information only from the news read by users such as news contents, categories, and keywords. Instead of just focusing on news information, the sweepstakes-participating records were also taken to be our source data to find out users’ potential interests. A personalized news recommendation method is presented in this paper. Our source data were obtained from the website: NIUS news (www.niusnews.com), a female-oriented news website that provides news in Chinese. In order to make accurate recommendations, we adopted Latent Dirichlet Allocation (LDA), one of the topic modeling techniques to process both news contents and the descriptions of sweepstakes-items. The values of topic distributions were then used to build the user profile. Finally, by measuring the similarity between the profile of the target user and the candidate items, a personalized news recommendation is provided. The proposed method composed of topic modeling techniques, content-based filtering, and the ideals of collaborative filtering. We mainly focus on sweepstakes-item selection preference to evaluate our recommendation performance.