Hengshu Zhu
Enhong Chen
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Abstract
Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.
- Rakesh Agrawal and Ramakrishnan Srikant. 1994. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94). Morgan Kaufmann, San Francisco, CA, 487--499. Google Scholar
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- Leif Azzopardi, Mark Girolami, and Keith van Risjbergen. 2003. Investigating the relationship between language model perplexity and IR precision-recall measures. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’03). ACM, New York, NY, 369--370. DOI: http://dx.doi.org/10.1145/860435.860505 Google ScholarDigital Library
- Roland Bader, Eugen Neufeld, Wolfgang Woerndl, and Vivian Prinz. 2011. Context-aware POI recommendations in an automotive scenario using multi-criteria decision making methods. In Proceedings of the 2011 Workshop on Context-Awareness in Retrieval and Recommendation (CaRR’11). ACM, New York, NY, 23--30. DOI: http://dx.doi.org/10.1145/1961634.1961640 Google ScholarDigital Library
- Tengfei Bao, Happia Cao, Enhong Chen, Jilei Tian, and Hui Xiong. 2010. An unsupervised approach to modeling personalized contexts of mobile users. In Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM’10). IEEE Computer Society, Washington, DC, 38--47. DOI: http://dx.doi.org/10.1109/ICDM.2010.16 Google ScholarDigital Library
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Lantent Dirichlet allocation. Journal of Machine Learning Research 3 (2003), 993--1022. Google ScholarDigital Library
- Huanhuan Cao, Tengfei Bao, Qiang Yang, Enhong Chen, and Jilei Tian. 2010. An effective approach for mining mobile user habits. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10). ACM, New York, NY, 1677--1680. DOI: http://dx.doi.org/10.1145/1871437.1871702 Google ScholarDigital Library
- Nathan Eagle, Aaron Clauset, and John A. Quinn. 2009. Location segmentation, inference and prediction for anticipatory computing. In AAAI Spring Symposium on Technosocial Predictive Analytics.Google Scholar
- Andreas Emrich, Alexandra Chapko, and Dirk Werth. 2009. Context-aware recommendations on mobile services: The m:Ciudad approach. In Proceedings of the 4th European Conference on Smart Sensing and Context (EuroSSC’09). Springer-Verlag, Berlin, 107--120. Google ScholarDigital Library
- Yong Ge, Qi Liu, Hui Xiong, Alexander Tuzhilin, and Jian Chen. 2011. Cost-aware travel tour recommendation. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11). ACM, New York, NY, 983--991. DOI: http://dx.doi.org/10.1145/2020408.2020568 Google ScholarDigital Library
- Thomas L. Griffiths and Mark Steyvers. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America 101, suppl1 (2004), 5228--5235.Google ScholarCross Ref
- Gregor Heinrich. 2005. Parameter Estimation for text Analysis. Technical report, University of Lipzig (2005).Google Scholar
- Kyoung Jae Kim, Hyunchul Ahn, and Sangwon Jeong. 2010. Context-aware recommender systems using data mining techniques. Journal of World Academy of Science, Engineering and Technology 64 (2010), 357--362.Google Scholar
- Minsuk Kahng, Sangkeun Lee, and Sang-goo Lee. 2011. Ranking in context-aware recommender systems. In Proceedings of the 20th International Conference Companion on World Wide Web (WWW’11). ACM, New York, NY, 65--66. DOI: http://dx.doi.org/10.1145/1963192.1963226 Google ScholarDigital Library
- Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. 2010. Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys’10). ACM, New York, NY, 79--86. DOI: http://dx.doi.org/10.1145/1864708.1864727 Google ScholarDigital Library
- Jennifer R. Kwapisz, Gary M. Weiss, and Samuel A. Moore. 2011. Activity recognition using cell phone accelerometers. SIGKDD Explorer Newsletter 12, 2 (2011), 74--82. Google ScholarDigital Library
- Dongjoo Lee, Sung Park, Minsuk Kahng, Sangkeun Lee, and Sang-goo Lee. 2010. Exploiting contextual information from event logs for personalized recommendation. Computer and Information Science 121--139.Google Scholar
- Xueying Li, Huanhuan Cao, Hui Xiong, Enhong Chen, and Jilei Tian. 2012. BP-Growth: Searching strategies for efficient behavior pattern mining. In Proceedings of 13th IEEE International Conference on Mobile Data Management (MDM’12). Google ScholarDigital Library
- Qi Liu, Yong Ge, Zhongmou Li, Enhong Chen, and Hui Xiong. 2011. Personalized travel package recommendation. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM’11). IEEE Computer Society, Washington, DC, 407--416. DOI: http://dx.doi.org/10.1109/ICDM.2011.118 Google ScholarDigital Library
- Qi Liu, Haiping Ma, Enhong Chen, and Hui Xiong. 2013. A survey of context-aware mobile recommendations. International Journal of Information Technology and Decision Making 12, 1 (2013), 139--172. DOI: http://dx.doi.org/10.1142/S0219622013500077Google ScholarCross Ref
- Moon-Hee Park, Jin-Hyuk Hong, and Sung-Bae Cho. 2007. Location-based recommendation system using bayesian user s preference model in mobile devices. In Ubiquitous Intelligence and Computing, 4th International Conference, UIC 2007, Hong Kong, China, July 11-13, 2007, Proceedings (Lecture Notes in Computer Science), Jadwiga Indulska, Jianhua Ma, Laurence Tianruo Yang, Theo Ungerer, and Jiannong Cao (Eds.), Vol. 4611. Springer, 1130--1139. DOI: http://dx.doi.org/10.1007/978-3-540-73549-6_110 Google ScholarDigital Library
- Ivana Podnar, Manfred Hauswirth, and Mehdi Jazayeri. 2002. Mobile push: Delivering content to mobile users. In Proceedings of the 22nd International Conference on Distributed Computing Systems (ICDCSW’02). IEEE Computer Society, Washington, DC, 563--570. Google ScholarDigital Library
- Mikkel N. Schmidt. 2009. Linearly constrained Bayesian matrix factorization for blind source separation. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems. 1624--1632.Google Scholar
- Mikkel N. Schmidt, Ole Winther, and Lars Kai Hansen. 2009. Bayesian non-negative matrix factorization. In Proceedings of 2009 International Conference on Independent Component Analysis and Signal Separation. 540--547. Google ScholarDigital Library
- Timothy Sohn, Kevin A. Li, Gunny Lee, Ian Smith, James Scott, and William G. Griswold. 2005. Place-Its: A study of location-based reminders on mobile phones. In Proceedings of the 5th International Conference on Ubiquitous Computing (Ubicomp’05). 232--250. Google ScholarDigital Library
- Hung-Wen Tung and Von-Wun Soo. 2004. A personalized restaurant recommender agent for mobile e-service. In Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE’04). IEEE Computer Society, Washington, DC, 259--262. Google ScholarDigital Library
- Wolfgang Woerndl, Christian Schueller, and Rolf Wojtech. 2007. A hybrid recommender system for context-aware recommendations of mobile applications. In Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop (ICDEW’07). IEEE Computer Society, Washington, DC, 871--878. DOI: http://dx.doi.org/10.1109/ICDEW.2007.4401078 Google ScholarDigital Library
- Kuifei Yu, Baoxian Zhang, Hengshu Zhu, Huanhuan Cao, and Jilei Tian. 2012. Towards personalized context-aware recommendation by mining context logs through topic models. In Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I (PAKDD’12). Springer-Verlag, Berlin, 431--443. DOI: http://dx.doi.org/10.1007/978-3-642-30217-6_36 Google ScholarDigital Library
- Vincent Wenchen Zheng, Bin Cao, Yu Zheng, Xing Xie, and Qiang Yang. 2010. Collaborative filtering meets mobile recommendation: A user-centered approach. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI’10). 236--241.Google ScholarDigital Library
- Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International Conference on World Wide Web (WWW’09). ACM, New York, NY, 791--800. DOI: http://dx.doi.org/10.1145/1526709.1526816 Google ScholarDigital Library
- Hengshu Zhu, Huanhuan Cao, Enhong Chen, Hui Xiong, and Jilei Tian. 2012a. Exploiting enriched contextual information for mobile app classification. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12). ACM, New York, NY, 1617--1621. DOI: http://dx.doi.org/10.1145/2396761.2398484 Google ScholarDigital Library
- Hengshu Zhu, Enhong Chen, Kuifei Yu, Huanhuan Cao, Hui Xiong, and Jilei Tian. 2012b. Mining personal context-aware preferences for mobile users. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining (ICDM’12). IEEE Computer Society, Washington, DC, 1212--1217. DOI: http://dx.doi.org/10.1109/ICDM.2012.31 Google ScholarDigital Library
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- Mining Mobile User Preferences for Personalized Context-Aware Recommendation
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