Search-Personalizer: Adaptive dynamic document clustering based search results personalization
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Abstract
Current search engines present the user a ranked list given the submitted user query. Top ranked search results generally cover few aspects .In many cases the users are interested in the main themes of search results besides the ranked list in order to have a global view. This is often achieved through clustering approaches. Personalized search studies ranking or re-ranking them based on implicit feedback and it also infer user information need based on user search engine interaction and re-rank the search results. Same as this, clustering’s of search results intuitively should also be dynamically tuned according to user search system interaction. Thus it brings interesting clustering challenges in the personalized search framework and the results should change dynamically to reflect the personalized ranking of search results. Traditional static clustering algorithms based on document similarity cannot achieve this. This paper deals how to incrementally cluster the search results and dynamically update the cluster representation based on user’s implicit feedback.
Keywords: search, personalization, clustering, hierarchical.
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