Personalizing Google's Search Results using Naive Bayesian Probabilistic Model
Krishnarajanagar G. Srinivasa, Manorma Kumar, Thalari Vinay, K R Venugopal, Lalit Mohan Patnaik · ePrints@Bangalore University (Bangalore University) · 2006
Personalization is the process of presenting the right information to the right user at the right moment, by storing browsing history about the user and analysing that information. Personalization methods enables the site to target advertising, promote products, personalize news feeds, recommend documents, make appropriate advice, and target e-mail. In this paper we introduce a Naive Bayesian probabilistic model, which classifies the sites into different categories. The user profile is built dynamically on the recorded interests of the user, which are nothing, categories of the site in which user browses. The algorithms are tested on varying keywords and the results obtained were compared with the Google’s page rank system