A Novel Survey On Personalized Mobile Search Engine

Abstract

We propose a personalized mobile search engine, PMSE that captures the users’ predilections in the form of concepts by mining their click through data. Due to the consequentiality of location information in mobile search, PMSE relegates these concepts into content concepts and location concepts. In integration, users’ locations (situated by GPS) are acclimated to supplement the location concepts in PMSE. The utilizer predilections are organized in an ontology-predicated, multi-facet utilizer profile, which are acclimated to habituate a personalized ranking function for rank adaptation of future search results. To characterize the diversity of the concepts associated with a query and their relevance’s to the users need, four entropies are introduced to balance the weights between the content and location facets. Predicated on the client-server model, we withal present a detailed architecture and design for implementation of PMSE. In our design, the client accumulates and stores locally the click through data to bulwark privacy, whereas cumbersomely hefty tasks such as concept extraction, training and re ranking are performed at the PMSE server. Moreover, we address the privacy issue by restricting the information in the utilizer profile exposed to the PMSE server with two privacy parameters. We prototype PMSE on the Google Android platform. Experimental results show that PMSE significantly amends the precision comparing to the baseline. Key words: - Search Engine, Use