Wuhan University Journal of Natural Sciences

, Volume 23, Issue 1, pp 9–16 | Cite as

A framework for personalized adaptive user interest prediction based on topic model and forgetting mechanism

Complex Science Management
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Abstract

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.

Keywords

user interest user interest presentation user interest prediction topic model forgetting mechanism 

CLC number

TP 393 

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Copyright information

© Wuhan University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information ManagementWuhan UniversityHubeiChina
  2. 2.School of Media and CommunicationWuhan Textile UniversityHubeiChina

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