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)
Inferring user search intention based on situation analysis of the physical world
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
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Personalized search has recently got significant attention in the web search. Accordingly, user's intention of search is very important information to retrieval information in aspect of personalized Search. Although many personalized search and strategies have been proposed, the majority of web users are difficulty in retrieval information corresponding their search intention. In this paper, we present personalized search based on User Intention through the HPVM(Hierarchical Phrase Vector Model) to solve these problems using and machine learning methodology. Users can navigate through the prior user's intention by their own needs. This is especially useful for various meaning and poor queries. By analyzing the results, we find out that there is an unique representation of user intention under different queries, contexts and users. Furthermore, we can find out that this knowledge is very important in improving personalized information retrieval performance by filtering the results, recommending a new query, and distinguishing user's characteristics. With this approach, search engines can provide more predictive information for Web searchers. Based on this approach, we developed a personalized search engine, HPS (Hierarchical Phrase Search).