Personalized Web Search For Improving Retrieval Effectiveness

Current web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of web search is to carry out retrieval for each user incorporating his/her interests.

We propose a novel technique to learn user profiles from user’s search histories. The user profiles are then used to improve retrieval effectiveness in web search. A user profile and a general profile are learned from the user’s search history and a category hierarchy respectively.

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 a context to disambiguate the words in the user’s query. Web search is conducted based on both the user query and the set of categories.

Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize web search is both effective and efficient.
Source: Binghamton University
Author: Fang Liu, Clement Yu, Weiyi Meng

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