Collaborative Filtering Based On Search Engine Logs
|DR.A.Muthu kumaravel and Mr. Kannan Subramanian
Dept. of MCA, Bharath Institute of Science and Technology, Bharath University, Chennai – 73
|Related article at Pubmed, Scholar Google|
Search engines come roughly equivalent results for an equivalent question, despite the user’s real interest. To extend the relevancy of search results, personalized search engines produce user profiles to capture the users’ personal preferences and intrinsically determine the particular goal of the input question. An honest user identification strategy is an important and elementary element in program personalization. The user identification ways area unit evaluated and compared with our antecedently projected personalized question cluster methodology. During this project, we tend to specialize in program personalization and develop many concept-based user identification strategies that area unit supported each positive and negative preferences. user profiles that capture each the user’s positive and negative preferences. Negative preferences improve the separation of comparable and dissimilar queries that facilitates associate agglomerate cluster rule to make a decision if the best clusters are obtained.