A Framework for Personalized Adaptive User Interest Prediction Based on Topic Model and Forgetting Mechanism
GUI Sisi1, LU Wei1, ZHOU Pengcheng1, ZHENG Zhan21. School of Information Management, Wuhan University, Wuhan 430072, Hubei, China; 2. School of Media and Communication, Wuhan Textile University, Wuhan 430073, Hubei, China
User interest is not static and changes dynamically. In the scenario of a search engine, this paper presents a personalized adaptive user interest prediction framework. It represents user interest as a topic distribution, captures every change of user interest in the history, and uses the changes to predict future individual user interest dynamically. More specifically, it first uses a personalized user interest representation model to infer user interest from queries in the user’s history data using a topic model; then it presents a personalized user interest prediction model to capture the dynamic changes of user interest and to predict future user interest by leveraging the query submission time in the history data. Compared with the Interest Degree Multi-Stage Quantization Model, experiment results on an AOL Search Query Log query log show that our framework is more stable and effective in user interest prediction.
Key words:user interest; user interest presentation; user interest prediction; topic model; forgetting mechanism
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