10 Jan 2011
Current IPB Center Library search engine has been developed to serve all users, independent of the special needs of any individual user. Personalized search is to carry out retrieval for each user incorporating his/her interest. This research propose a novel technique to learn user profile from user lending history to represent long-term interest and user search history to represent short-term interest. The user profiles are then used to improve retrieval effectiveness in online search. A general profile are learned from a category hierarchy. These two profiles are combined to map a user query into a set of categories which represent the user’s search intention and serve as context to disambiguate the words in the user’s query. Online search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a merging algorithm are evaluated. Experimental results indicate that this technique to personalize search is effective.
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