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Research Article Open Access
Search engines return roughly the same results for the same query, regardless of the user’s real interest. Personalized search is an important research area that aims to resolve the ambiguity of query terms. To increase the relevance of search results, personalized search engines create user profiles to capture the users’ personal preferences and as such identify the actual goal of the input query. Since users are usually reluctant to explicitly provide their preferences due to the extra manual effort involved, recent research has focused on the automatic learning of user preferences from users’ search histories or browsed documents and the development of personalized systems based on the learned user preferences. In this project, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. User profiles which capture both the user’s positive and negative preferences. Negative preferences improve the separation of similar and dissimilar queries, which facilitates an agglomerative clustering algorithm to decide if the optimal clusters have been obtained.