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Retracted

This article has been retracted as the peer review of the article has been compromised, due to a conflict of interest between the handling editor and one of the authors.

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References

  1. Z. Xu, H.-Y. Chen, and J. Yu, “Generating personalized web search using semantic context,” The Scientific World Journal, vol. 2015, Article ID 462782, 10 pages, 2015.
The Scientific World Journal
Volume 2015, Article ID 462782, 10 pages
http://dx.doi.org/10.1155/2015/462782
Research Article

Generating Personalized Web Search Using Semantic Context

1The Third Research Institute, Ministry of Public Security, Shanghai 201142, China
2Tsinghua University, Beijing 100081, China
3East China University of Political Science and Law, Shanghai 201142, China
4Shanghai University, Shanghai 201142, China

Received 19 May 2014; Accepted 18 August 2014

Academic Editor: Xiangfeng Luo

Copyright © 2015 Zheng Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The “one size fits the all” criticism of search engines is that when queries are submitted, the same results are returned to different users. In order to solve this problem, personalized search is proposed, since it can provide different search results based upon the preferences of users. However, existing methods concentrate more on the long-term and independent user profile, and thus reduce the effectiveness of personalized search. In this paper, the method 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. Experimental results fully confirm that our approach can successfully represent user context according to individual user information needs.