Computer Science ›› 2016, Vol. 43 ›› Issue (2): 277-282.doi: 10.11896/j.issn.1002-137X.2016.02.058

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Building User Personalization Model Based on Short Term Memory and Forgetting Factor

CHEN Hai-yan, XU Zheng and ZHANG Hui   

  • Online:2018-12-01 Published:2018-12-01

Abstract: One criticism of search engines is that when queries are submitted,the same results are returned to different users.To address the accuratcy problem,personalized search was proposed,since it could provide different search results based upon the preferences of users.However,the existing methods concentrate more on the long-term and independent user profile,thus reducing the effectiveness of personalized search.We introduced an approach that captures the user context to provide accurate preferences of users for effectively personalized search.First,the short-term query context is generated to identify related concepts of the query.Second,the user context is generated based on the click through data of users.Finally,a forgetting factor is introduced to merge the independent user context in a user session,which maintains the evolution of user preferences.The experimental results fully confirm that our approach can successfully represent user context according to individual user information needs.

Key words: Personalized search,User model,Semantic mining,User context

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