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Issue No.02 - April-June (2009 vol.2)
pp: 140-151
Jae Kyeong Kim , Kyunghee University, Seoul
Hyea Kyeong Kim , Kyunghee University, Seoul
ABSTRACT
Personalization services in a ubiquitous computing environment—ubiquitous personalization services computing—are expected to emerge in diverse environments. Ubiquitous personalization must address limited computational power of personal devices and potential privacy issues. Such characteristics require managing and maintaining a client-side recommendation model for ubiquitous personalization. To implement the client-side recommendation model, this paper proposes Buying-net, a customer network in ubiquitous shopping spaces. Buying-net is operated in a community, called the Buying-net space, of devices, customers, and services that cooperate together to achieve common goals. The customers connect to the Buying-net space using their own devices that contain software performing tasks of learning the customers' preferences, searching for similar customers for network formation, and generating recommendation lists of items. Buying-net attempts to improve recommendation accuracy with less computational time by focusing on local relationship of customers and newly obtained information. We experimented with such customer networks in the area of multimedia content recommendation and validated that Buying-net outperformed a typical collaborative-filtering-based recommender system on accuracy as well as computational time. This shows that Buying-net has good potential to be a system for ubiquitous shopping.
INDEX TERMS
Mobile commerce, recommender systems, ubiquitous computing, ubiquitous personalization services.
CITATION
Jae Kyeong Kim, Hyea Kyeong Kim, "Personalized Recommendation over a Customer Network for Ubiquitous Shopping", IEEE Transactions on Services Computing, vol.2, no. 2, pp. 140-151, April-June 2009, doi:10.1109/TSC.2009.7
REFERENCES
[1] G. Adomavicius, Z. Huang, and A. Tuzhilin, “Personalization and Recommender Systems,” 2008 Tutorials in Operations Research: State-of-the-Art Decision Making Tools in the Information Intensive Age, Z.-L. Chen and S. Raghavan, eds., pp. 55-107, 2008.
[2] G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.
[3] L. Anderlini and I. Antonella, “Path Dependence and Learning from Neighbors,” Games and Economic Behavior, vol. 13, pp. 141-177, 1996.
[4] O. Arazy, I. Sana, B. Shapira, and N. Kumar, “Social Relationships in Recommender Systems,” Proc. 17th Ann. Workshop Information Technologies and Systems, pp. 146-151, 2007.
[5] V. Bala and S. Goyal, “Learning from Neighbours,” Rev. of Economic Studies, vol. 65, pp. 595-621, 1988.
[6] M. Balabanovic and Y. Shoham, “Fab: Content-Based Collaborative Recommendation,” Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.
[7] A. Borchers, J. Herlocker, J. Konstan, and J. Riedl, “Ganging Up on Information Overload,” Computer, vol. 31, no. 4, pp. 106-108, Apr. 1998.
[8] M. Buckland and F. Gey, “The Relationship between Recall and Precision,” J. Am. Soc. for Information Science, vol. 45, no. 1, pp. 12-19, 1994.
[9] J. Canny, “Collaborative Filtering with Privacy,” Proc. IEEE Symp. Security and Privacy, pp. 45-57, 2002.
[10] C.T. Chen and W.S. Tai, “An Information Push-Delivery System Design for Personal Information Service on the Internet,” Information Processing and Management: An Int'l J., vol. 39, no. 6, pp. 873-888, 2003.
[11] K. Cheverst, K. Mitchell, and N. Davies, “Exploring Context-Aware Information Push,” Personal and Ubiquitous Computing, vol. 6, no. 4, pp. 276-281, 2002.
[12] Y.H. Cho and J.K. Kim, “Application of Web Usage Mining and Product Taxonomy to Collaborative Recommendations in E-Commerce,” Expert Systems with Applications, vol. 26, no. 2, pp.233-246, 2004.
[13] Y.H. Cho, J.K. Kim, and S.H. Kim, “Personalized Recommender System Based on Web Usage Mining and Decision Tree Induction,” Expert Systems with Applications, vol. 23, no. 3, pp. 329-342, 2002.
[14] I. Cingil, A. Dogac, and A. Azgin, “A Broader Approach to Personalization,” Comm. ACM, vol. 43, no. 8, pp. 136-141, 2000.
[15] C. Cleverdon and M. Keen, “Factors Determining the Performance of Indexing Systems, vol. 2, Test Results,” Aslib Cranfield Research Project, Cranfield Univ., 1966.
[16] J. Goldbeck, “FilmTrust: Movie Recommendations from Semantic Web-Based Social Networks,” Proc. Consumer Comm. and Networking Conf., pp. 1314-1315, 2006.
[17] P. Han, B. Xie, F. Yang, and R. Shen, “A Scalable P2P Recommender System Based on Distributed Collaborative Filtering,” Expert Systems with Applications, vol. 27, no. 2, pp. 203-210, 2004.
[18] J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
[19] Z. Huang, “Analysis of the User Similarity Network for Distributed Recommendation,” Proc. 17th Workshop Information Technologies and Systems, 2007.
[20] T. Iwao, S. Amamiya, G. Zhong, and M. Amamiya, “Ubiquitous Computing with Service Adaptation Using Peer-to-Peer Communication Framework,” Proc. Ninth IEEE Int'l Workshop Future Trends of Distributed Computing Systems, pp. 240-248, 2003.
[21] C.Y. Kim, J.K. Lee, Y.H. Cho, and D.H. Kim, “VISCORS: A Visual-Content Recommender for the Mobile Web,” IEEE Intelligent Systems, vol. 19, no. 6, pp. 32-39, Nov./Dec. 2004.
[22] H.K. Kim, J.K. Kim, and Y.H. Cho, “A Collaborative Filtering Recommendation Methodology for Peer-to-Peer Systems,” Computer Science, vol. 3590, pp. 98-107, 2005.
[23] N. Lewis, “From Customization to Ubiquitous Personalization: Digital Identity and Ambient Network Intelligence,” Interactions, vol. 11, no. 2, pp. 48-50, 2004.
[24] P. Massa and P. Avesani, “Controversial Users Demand Local Trust Metrics: An Experimental Study on epinions.com Community,” Proc. 20th Nat'l Conf. Artificial Intelligence, pp. 121-126, 2005.
[25] J.M. Yohe, “Community Computing and the Computing Community,” Proc. 22nd Ann. ACM SIGUCCS Conf. User Services, pp.35-39, 1994.
[26] M. Pazzani, “A Framework for Collaborative, Content-Based andDemographic Filtering,” Artificial Intelligence Rev., vol. 13, nos.5/6, pp. 393-408, 1999.
[27] H. Peng, X. Bo, Y. Fan, and S. Ruimin, “A Scalable P2P Recommender System Based on Distributed Collaborative Filtering,” Expert Systems with Applications, vol. 27, no. 2, pp. 203-210, 2004.
[28] O. Potonniée, “Ubiquitous Personalization: A Smart Card Based Approach,” Proc. Fourth Gemplus Developer Conf., 2002.
[29] P. Resnick and H.R. Varian, “Recommender Systems,” Comm. ACM, vol. 40, no. 3, pp. 56-58, 1997.
[30] Y.U. Ryu, H.K. Kim, Y.H. Cho, and J.K. Kim, “Peer-Oriented Content Recommendation in a Social Network,” Proc. 16th Workshop Information Technologies and Systems, pp. 115-120, 2006.
[31] Y.U. Ryu, H.K. Kim, J.K. Kim, and Y.H. Cho, “Experimental Evaluation of Recommendation over a Customer Network,” Proc. 17th Ann. Workshop Information Technologies and Systems, pp. 158-163, 2007.
[32] B. Sarwar, G. Karypis, J.A. Konstan, and J.T. Riedl, “Analysis of Recommendation Algorithms for e-Commerce,” Proc. Second ACM Conf. Electronic Commerce, pp. 158-167, 2000.
[33] B. Sarwar, G. Karypis, J.A. Konstan, and J.T. Riedl, “Item Based Collaborative Filtering Recommendation Algorithms,” Proc. 10th Int'l World Wide Web Conf., pp. 285-295, 2001.
[34] M. Takemoto, H. Sunage, K. Tanaka, H. Matsumura, and E. Shinohara, “The Ubiquitous Service-Oriented Network (USON): An Approach for a Ubiquitous World Based on P2P Technology,” Proc. Second IEEE Int'l Conf. Peer-to-Peer Computing, pp. 17-24, 2002.
[35] A. Tveit, “Peer-to-Peer Based Recommendations for Mobile Commerce,” Proc. First Int'l Mobile Commerce Workshop, pp. 26-29, 2001.
[36] A.S. Vivacqua, C.E.R. de Mello, D.K. de Souza, J.A. de Avilar Menezes, L.C. Marques, M.S. Ferreira, and J.M. de Souza, “Time Based Activity Profiles to Recommend Partnership in a P2P Network,” Proc. 11th Int'l Conf. Computer Supported Cooperative Work in Design, pp. 582-587, 2007.
[37] J. Wang, J. Pouwelse, R.L. Lagendijk, and M.J.T. Reinders, “Distributed Collaborative Filtering for Peer-to-Peer File Sharing Systems,” Proc. Ann. 21st ACM Symp. Applied Computing, pp. 1026-1030, 2006.
[38] M. Weiser, “The Computer for the Twenty-First Century,” Scientific Am., vol. 265, no. 3, pp. 94-102, 1991.
[39] M. Weiser, “Some Computer Science Issues in Ubiquitous Computing,” Comm. ACM, vol. 36, no. 7, pp. 74-83, 1993.
[40] K.R. Wood, T. Richardson, F. Bennett, A. Harter, and A. Hopper, “Global Teleporting with Java: Toward Ubiquitous Personalized Computing,” Computer, vol. 30, no. 2, pp. 53-59, Feb. 1997.
[41] S. You, J. Choi, G. Heo, D. Choi, H. Park, H. Kim, and W. Cho, “COCOLAB: Supporting Human Life in Ubiquitous Environment by Community Computing,” Proc. First Korea-Japan Joint Workshop Ubiquitous Computing and Networking Systems, pp. 115-119, 2005.
[42] M. Zanin, P. Cano, J.M. Buldú, and C. Celma, “Complex Networks in Recommendation Systems,” Proc. Second World Scientific and Eng. Academy and Soc. Int'l Conf. Computer Eng. and Applications, pp. 120-124, 2008.
[43] L.-J. Zhang, J. Zhang, and H. Cai, Services Computing. Springer, 2007.
[44] T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Database,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 103-114, 1996.
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