Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Modern Information Retrieval
Collaborative information retrieval (CIR)
The New Review of Information Behaviour Research
Introduction to Algorithms
A live-user evaluation of collaborative web search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An efficient collaborative information retrieval system by incorporating the user profile
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
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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 CIR suffers from the personalization problem because 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. We have developed a personalized CIR system, called PERCIRS, to solve this problem. Selecting an efficient method to calculate the similarity between user profiles is a key factor for enhancing PERCIRS's efficiency. In this paper, we propose a new graph-based method for user profile similarity calculation. Finally, by introducing an evaluation method, we will show that this new method is more efficient than the previous methods.