Issue No. 04 - April (2013 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.23
Dik Lun Lee , Hong Kong University of Science and Technology, Hong Kong
Kenneth Wai-Ting Leung , Hong Kong University of Science and Technology, Hong Kong
Wang-Chien Lee , The Pennsylvania State University, University Park
We propose a personalized mobile search engine (PMSE) that captures the users' preferences in the form of concepts by mining their clickthrough 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. The 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. In our design, the client collects and stores locally the clickthrough data to protect privacy, whereas heavy tasks such as concept extraction, training, and reranking 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. We prototype PMSE on the Google Android platform. Experimental results show that PMSE significantly improves the precision comparing to the baseline.
Ontologies, Servers, Entropy, Vectors, Privacy, Mobile communication, Search engines, user profiling, Clickthrough data, concept, location search, mobile search engine, ontology, personalization
Dik Lun Lee, Kenneth Wai-Ting Leung, Wang-Chien Lee, "PMSE: A Personalized Mobile Search Engine", IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 820-834, April 2013, doi:10.1109/TKDE.2012.23