Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Set-based model: a new approach for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Communications of the ACM
Personalized web search by mapping user queries to categories
Proceedings of the eleventh international conference on Information and knowledge management
Enhancing the Set-Based Model Using Proximity Information
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Combining document representations for known-item search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A personalized search engine based on web-snippet hierarchical clustering
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Interest-based personalized search
ACM Transactions on Information Systems (TOIS)
An iterative implicit feedback approach to personalized search
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
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There are many kinds of personalizing approaches in the area of web information retrieval. But it is still unclear whether personalization is consistently effective on different queries for different users, and under different search contexts. In this paper, we study this problem and propose a personalized search approach that can easily extend a conventional search engine. We present an intelligent relevance-evaluation framework for user Intention-based personalized search based on web-mining and machine learning approaches. Users can navigate through the prior user's intention by their own needs. This is especially useful for polysemous and poor queries. By analyzing the results, we reveal that there is an unique representation of intention under different queries, contexts and users. Furthermore, we reveal that this knowledge is very important in improving retrieval performance by filtering the results, recommending a new query, and distinguishing user's characteristics. Proposed personalized search engine HPS(Hierachical Phrase Search) can provide more predictive information by clearly identifying the searcher's intention at query-time.