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New Technology of Library and Information Service  2015, Vol. 31 Issue (9): 9-16    DOI: 10.11925/infotech.1003-3513.2015.09.02
Current Issue | Archive | Adv Search |
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
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Abstract  

[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.

Received: 03 April 2015      Published: 06 April 2016
:  TP393  

Cite this article:

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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.09.02     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I9/9

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