On the reuse of past optimal queries
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic feedback using past queries: social searching?
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 6th international conference on Intelligent user interfaces
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Modern Information Retrieval
Similarity spreading: a unified framework for similarity calculation of interrelated objects
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Further Experiments on Collaborative Ranking in Community-Based Web Search
Artificial Intelligence Review
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A live-user evaluation of collaborative web search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A graph-based profile similarity calculation method for collaborative information retrieval
Proceedings of the 2008 ACM symposium on Applied computing
Personalized information access through flexible and interoperable profiles
WISE'07 Proceedings of the 2007 international conference on Web information systems engineering
Graph-based profile similarity calculation method and evaluation
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
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As the volume of information augments, the importance of the Information Retrieval (IR) increases. Collaborative Information Retrieval (CIR) is one of the popular social-based IR approaches. A CIR system registers the previous user interactions to response to the subsequent user queries more efficiently. But the goals and the characteristics of two users may be different; so when they send the same query to a CIR system, they may be interested in two different lists of documents. In this paper we deal with the personalization problem in the CIR systems by constructing a profile for each user. We propose three new approaches to calculate the user profile similarity that we will employ in our personalized CIR algorithm.