Context-Based Adaptive Personalized Web Search for Improving Information Retrieval Effectiveness

Current Web search/metasearch engines are built to serve all users, independent of the special needs of any individual user in different situations. Personalization of web search is to carry out retrieval for each user incorporating his/her interests. In this paper, we propose a novel adaptive personalized approach based on context to adapting search results according to each user's need in different situations for relevant information with little user effort. After the process of the context-based adaptive personalized search is analyzed, three key technologies to implement this process are presented in details, which are semantic indexing for Web resources, modeling and acquiring user context and semantic similarity matching between Web resources and user context. Experimental results indicate that the adaptive personalized search system is adopted by most of users and our approach to personalize Web search is both effective.