We propose a personalized mobile search engine (PMSE) 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, PMSE classifies these concepts into content concepts and location concepts. In addition, users’ locations (positioned by GPS) are used to supplement the location concepts in PMSE.
User preferences are organized in an ontology-based, multifacet 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 relevances to the user’s need, four entropies are introduced to balance the weights between the content and location facets. Based on the client-server model, we also present a detailed architecture and design for implementation of PMSE.
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 PMSE server. Moreover, we address the privacy issue by restricting the information in the user profile exposed to the PMSE server with two privacy parameters. Experimental results show that PMSE significantly improves the precision comparing to the baseline. PMSE A Personalized Mobile Search Engine
Most of the previous work assumed that all concepts are of the same type. Observing the need for different types of concepts, we present in this paper a personalized mobile search engine (PMSE) which represents different types of concepts in different ontologies. In particular, recognizing the importance of location information in mobile search, we separate concepts into location concepts and content concepts. To incorporate context information revealed by user mobility, we also take into account the visited physical locations of users in the PMSE. Since this information can be conveniently obtained by GPS devices, it is hence referred to as GPS locations. GPS locations play an important role in mobile web search.
In this paper, we propose a realistic design for PMSE by adopting the metasearch approach which relies on one of the commercial search engines, such as Google, Yahoo, or Bing, to perform an actual search. The client is responsible for receiving the user’s requests, submitting the requests to the PMSE server, displaying the returned results, and collecting his/her click through in order to derive his/her personal preferences. The PMSE server, on the other hand, is responsible for handling heavy tasks such as forwarding the requests to a commercial search engine, as well as training and reranking of search results before they are returned to the client. The user profiles for specific users are stored on the PMSE clients, thus preserving privacy to the users. PMSE has been prototyped with PMSE clients on the Google Android platform and the PMSE server on a PC server to validate the proposed ideas.