Research on User Traits Predicting Based on LDA Topic Model
WANG Yajing1, GUO Qiang1, DENG Chunyan1, LIN Qingxuan1, LIU Jianguo2,3
1. Research Center for Complex Systems Science, University of Shanghai for Science & Technology, Shanghai 200093, China; 2. Institute of Accounting and Finance,Shanghai University of Finance and Economics, Shanghai 200433, China; 3. Institute of Sina WRD Big Data, Shanghai 210204, China
Abstract：User traits can be effectively predicted by singular value decomposition and Logistic Regression through online user’s ‘Like’ information. However, this method cannot predict new users’ traits. To slove the problem, this paper proposes an online user traits predicting method based on LDA topic model. Firstly, the method extracted the Weibo user’s ‘Like’ text topic through LDA model. Then it predicted new user traits based on topic. Finally, the result is compared to the traditional method based on singular value decomposition. The results showed that the F1 value of this method was up to 0.15, and the calculation time was shortened by 69.09% in average. Research inproves the defect that the inherent tags of the ‘Like’ informations cannot accurately reflect user preference, avoiding the disadvantage of recalculating new users and their ‘like’information in the predicting process of traditional methods, providing another feasible way for user traits analysis.
王雅静, 郭强, 邓春燕, 林青轩, 刘建国. 基于LDA主题模型的用户特征预测研究[J]. 复杂系统与复杂性科学, 2020, 17(4): 9-15.
WANG Yajing, GUO Qiang, DENG Chunyan, LIN Qingxuan, LIU Jianguo. Research on User Traits Predicting Based on LDA Topic Model. Complex Systems and Complexity Science, 2020, 17(4): 9-15.
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