User Interest Prediction Combing Topic Model and Multi-time Function
Gui Sisi1, Lu Wei1,2, Huang Shihao1, Zhou Pengcheng1
1 School of Information Management, Wuhan University, Wuhan 430072, China;
2 Center for the Studies of Information Resources, Wuhan University, Wuhan 430072, China
[Objective] User interest is not static and it changes dynamically as time goes by, this paper proposes a user interest prediction model based on topic model and multi-time function. [Methods] Generate user interests by topic model, and calculate the weights of each user interest at every time point by applying multi-time function in order to predict user interest at next time point. [Results] Compared with memory-based user profile model and multi-step user profile model, cosine similarity and Kullback-Leibler divergence of the experimental results on search engine log data provided by Sogou Lab show that this model can predict user interests more effectively. [Limitations] The proposed method is only tested on search engine log data provided by Sogou Lab, and it need further examination on other data sets. [Conclusions] It is more effective to take every time point of user history data into consideration for user interest prediction.
桂思思, 陆伟, 黄诗豪, 周鹏程. 融合主题模型及多时间节点函数的用户兴趣预测研究[J]. 现代图书情报技术, 2015, 31(9): 9-16.
Gui Sisi, Lu Wei, Huang Shihao, Zhou Pengcheng. User Interest Prediction Combing Topic Model and Multi-time Function. New Technology of Library and Information Service, 2015, 31(9): 9-16.
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