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Issue No.04 - April (2013 vol.25)
pp: 820-834
Kenneth Wai-Ting Leung , Hong Kong University of Science and Technology, Hong Kong
Dik Lun Lee , 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
Kenneth Wai-Ting Leung, Dik Lun Lee, Wang-Chien Lee, "PMSE: A Personalized Mobile Search Engine", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 4, pp. 820-834, April 2013, doi:10.1109/TKDE.2012.23
[1] Appendix, appendix.pdf, 2012.
[2] Nat'l geospatial, http:/, 2012.
[3] $svm^{light}$ , http:/, 2012.
[4] World gazetteer, http:/, 2012.
[5] E. Agichtein, E. Brill, and S. Dumais, "Improving Web Search Ranking by Incorporating User Behavior Information," Proc. 29th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2006.
[6] E. Agichtein, E. Brill, S. Dumais, and R. Ragno, "Learning User Interaction Models for Predicting Web Search Result Preferences," Proc. Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2006.
[7] Y.-Y. Chen, T. Suel, and A. Markowetz, "Efficient Query Processing in Geographic Web Search Engines," Proc. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2006.
[8] K.W. Church, W. Gale, P. Hanks, and D. Hindle, "Using Statistics in Lexical Analysis," Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon, Psychology Press, 1991.
[9] Q. Gan, J. Attenberg, A. Markowetz, and T. Suel, "Analysis of Geographic Queries in a Search Engine Log," Proc. First Int'l Workshop Location and the Web (LocWeb), 2008.
[10] T. Joachims, "Optimizing Search Engines Using Clickthrough Data," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2002.
[11] K.W.-T. Leung, D.L. Lee, and W.-C. Lee, "Personalized Web Search with Location Preferences," Proc. IEEE Int'l Conf. Data Mining (ICDE), 2010.
[12] K.W.-T. Leung, W. Ng, and D.L. Lee, "Personalized Concept-Based Clustering of Search Engine Queries," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 11, pp. 1505-1518, Nov. 2008.
[13] H. Li, Z. Li, W.-C. Lee, and D.L. Lee, "A Probabilistic Topic-Based Ranking Framework for Location-Sensitive Domain Information Retrieval," Proc. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2009.
[14] B. Liu, W.S. Lee, P.S. Yu, and X. Li, "Partially Supervised Classification of Text Documents," Proc. Int'l Conf. Machine Learning (ICML), 2002.
[15] W. Ng, L. Deng, and D.L. Lee, "Mining User Preference Using Spy Voting for Search Engine Personalization," ACM Trans. Internet Technology, vol. 7, no. 4,article 19, 2007.
[16] J.Y.-H. Pong, R.C.-W. Kwok, R.Y.-K. Lau, J.-X. Hao, and P.C.-C. Wong, "A Comparative Study of Two Automatic Document Classification Methods in a Library Setting," J. Information Science, vol. 34, no. 2, pp. 213-230, 2008.
[17] C.E. Shannon, "Prediction and Entropy of Printed English," Bell Systems Technical J., vol. 30, pp. 50-64, 1951.
[18] Q. Tan, X. Chai, W. Ng, and D. Lee, "Applying Co-Training to Clickthrough Data for Search Engine Adaptation," Proc. Int'l Conf. Database Systems for Advanced Applications (DASFAA), 2004.
[19] J. Teevan, M.R. Morris, and S. Bush, "Discovering and Using Groups to Improve Personalized Search," Proc. ACM Int'l Conf. Web Search and Data Mining (WSDM), 2009.
[20] E. Voorhees and D. Harman, TREC Experiment and Evaluation in Information Retrieval. MIT Press, 2005.
[21] Y. Xu, K. Wang, B. Zhang, and Z. Chen, "Privacy-Enhancing Personalized Web Search," Proc. Int'l Conf. World Wide Web (WWW), 2007.
[22] S. Yokoji, "Kokono Search: A Location Based Search Engine," Proc. Int'l Conf. World Wide Web (WWW), 2001.
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