Preferences extraction and ranking functions in mobile search

Abstract

This paper preferences extraction and ranking functions in mobile search that captures the users' preferences in the form of concepts by mining their click through data. Due to the importance of location information in mobile search, it classifies these concepts into content concepts and location concepts. In addition, users' locations (positioned by GPS) are used to supplement the location concepts. The user preferences are organized in an ontology-based, multi-facet user profile, which are used to adapt 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. Based on the client-server model, also present a detailed architecture and design for implementation of preferences extraction and ranking functions in mobile search. In our design, the client collects and stores locally the click through data to protect privacy, whereas heavy tasks such as concept extraction, training and re-ranking are performed at the server. Moreover, here address the privacy issue by restricting the information in the user profile exposed to the server with two privacy parameters. To prototype preferences extraction and ranking functions in mobile search on the Google Android platform.