Mobile Search Engine Personalization Enhanced with Recommendation System with an Impact of Affect Analysis

As the amount of web information grows rapidly, we propose a personalized search engine with an affect analysis. In this paper, we propose a new web search personalization which recommends the user with the additional information about the content they searched. It gives the user knowledge about the content which they never knew. The user preferences are organized in ontology based, multifacet user profiles, which are used to adapt a personalized ranking function for rank adaptation of future results. To recommend the users with additional information we have to analyse the other user’s click through data. Based on the client - server model, we also present a detailed architecture and design for implementation of personalized mobile search engine with an impact of affect analysis. We prototype PMSE on the google android platform. Experimental results show that PMSE significantly improves the precision comparing to the baseline.