Personalized web search by mapping user queries to categories
Citations Over TimeTop 10% of 2002 papers
Abstract
Current web search engines are built to serve all users, independent of the 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 map a user query to a set of categories, which represent the user's search intention. This set of categories can serve as a context to disambiguate the words in the user's query. 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. Several learning and combining algorithms are evaluated and found to be effective. Among the algorithms to learn a user profile, we choose the Rocchio-based method for its simplicity, efficiency and its ability to be adaptive. Experimental results indicate that our technique to personalize web search is both effective and efficient.
Related Papers
- → Ontological User Profiles for Representing Context in Web Search(2007)42 cited
- → An improved modeling method for profile-based personalized search(2020)12 cited
- User Profile Model Based on User Search Histories(2006)
- → A Personalized Approach for Re-ranking Search Results Using User Preferences(2020)3 cited