Web Search Personalization Using Semantic Similarity Measure

Web search personalization is the process of providing personalized results to the user for his query. In this paper, we present a relevance model to personalize search results which is based on query personalization. The user query is directly matched to the keywords of the user profile, and the original query is altered according to the keywords which is more likely similar or related according to the similarity measure. By finding the similarity between the user original query and user profile, a linear combination of preference space is generated at run-time to determine more accurately which pages are truly the most important with respect to the modified query. A heuristic algorithm is used to maintain the user profile based on the ongoing behavior. Our experiments prove that retrieving the search results based on query modification is effective in providing the personalized results to the user.