A personalized search engine based on correlation clustering method

Nowadays, search engines tend to use latest technologies in enhancing the personalization of web searches, which leads to better understanding of user requirements. This paper aims to address the problem of enhancing the efficiency of search system by mining data logs. Search logs are associated with all the interactions that have been done between the user and the search engine including the query, the resulted pages and the selection. The paper also aims minimize the ambiguity within the query whilst the partitioning-based clustering aims to reduce the search space by dividing the data into groups that contain similar objects. To do so, this paper conduct several experiments to evaluate correlation clustering method. The method of this paper includes a pre-processing phase, which in turn involves tokenization, stop-words removal, and stemming. In addition, we evaluates the impact of the two similarity/distance measures (Cosine similarity and Jaccard coefficient) on the results of the correlation clustering method. Experimental results obtained are quite satisfactory in terms of the Precision, Recall and F-score. � 2005-2016 JATIT & LLS. All rights reserved.