Bradley N. Miller
Joseph A. Konstan
John Riedl
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Abstract
Recommender systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not portable. They can only run on large computers connected to the Internet. A second limitation is that they require the user to trust the owner of the recommender with personal preference data. Personal recommenders hold the promise of delivering high quality recommendations on palmtop computers, even when disconnected from the Internet. Further, they can protect the user's privacy by storing personal information locally, or by sharing it in encrypted form. In this article we present the new PocketLens collaborative filtering algorithm along with five peer-to-peer architectures for finding neighbors. We evaluate the architectures and algorithms in a series of offline experiments. These experiments show that Pocketlens can run on connected servers, on usually connected workstations, or on occasionally connected portable devices, and produce recommendations that are as good as the best published algorithms to date.
- Ackerman, M. S., Cranor, L. F., and Reagle, J. 1999. Privacy in e-commerce: Examining user scenarios and privacy preferences. In ACM Conference on Electronic Commerce. 1--8.]] Google Scholar
- Aggarwal, C., Wolf, J., Wu, K., and Yu, P. 1999. Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 201--212.]] Google Scholar
- Anderson, D. P., Cobb, J., Korpela, E., Lebofsky, M., and Werthimer, D. 2002. Seti@home: An experiment in public-resource computing. Comm. ACM 45, 11 (November), 56--61.]] Google Scholar
- AP. 2002. New shopping technology could breed supermarket class system. San Jose Mercury News (November 10).]]Google Scholar
- Billsus, D. and Pazzani, M. J. 1998. Learning collaborative information filters. In Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, 46--54.]] Google Scholar
- Bouch, A., Kuchinsky, A., and Bhatti, N. 2002. Quality is in the eye of the beholder: Meeting users' requirements for internet quality of service. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systms. ACM Press, 297--304.]] Google Scholar
- Breese, J. S., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98). 43--52.]] Google Scholar
- Canny, J. 2002a. Collaborative filtering with privacy. In IEEE Symposium on Security and Privacy. 45--57.]] Google Scholar
- Canny, J. 2002b. Collaborative filtering with privacy: To appear. In IEEE Conference on Security and Privacy.]] Google Scholar
- Canny, J. 2002c. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.]] Google Scholar
- Chavez, A. and Maes, P. 1996. Kasbah: An agent marketplace for buying and selling goods. In the First International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology (PAAM'96). Practical Application Company, London, UK, 75--90.]]Google Scholar
- Chen, Y., Edler, J., Goldberg, A., Gottlieb, A., Sobti, S., and Yianilos, P. 1999. A prototype implementation of archival intermemory. In Proceedings of the Fourth ACM International Conference on Digital Libraries.]] Google Scholar
- Clarke, I., Hong, T. W., Miller, S. G., Sandberg, O., and Wiley, B. 2002. Protecting free expression online with freenet. IEEE Internet Computing. 6, 1 (January), 40--49.]] Google Scholar
- Claypool, M., Le, P., Waseda, M., and Brown, D. 2001. Implicit interest indicators. In Proceedings of the ACM Intelligent User Interfaces Conference (IUI).]] Google Scholar
- Clymer, A. 2003. Troops risk identity theft after burglary. New York Times (January 12).]]Google Scholar
- Cramer, R., Gennaro, R., and Schoenmakers, B. 1997. A secure and optimally efficient multi-authority election scheme. Lecture Notes in Computer Science 1233, 103--118.]] Google Scholar
- Dabek, F., Kaashoek, M. F., Karger, D., Morris, R., and Stoica, I. 2001. Wide-area cooperative storage with CFS. In Proceedings of the 18th ACM Symposium on Operating Systems Principles (SOSP '01). Chateau Lake Louise, Banff, Canada.]] Google Scholar
- Devore, J. L. 1995. Probability and Statistics for Engineering and the Sciences, fourth edition Duxbury Press.]]Google Scholar
- Dingledine, R., Freedman, M. J., and Molnar, D. 2001. The free haven project: Distributed anonymous storage service. Lecture Notes in Computer Science 2009.]] Google Scholar
- Druschel, P. and Rowstron, A. 2001. PAST: A large-scale, persistent peer-to-peer storage utility. In Proceedings of HOTOS. 75--80.]] Google Scholar
- Foner, L. 1999. Political artifacts and personal privacy: The yenta multi-agent distributed matchmaking system. Ph.D. thesis, Massachusetts Institute of Technology.]] Google Scholar
- Foner, L. N. 1996. A multi-agent referral system for matchmaking. Proceedings of the Second International Conference on Multi-Agent System (ICMAS-96).]]Google Scholar
- Freedman, M. J. and Morris, R. 2002. Tarzan: A peer-to-peer anonymizing network layer. In Proceedings of the 9th ACM Conference on Computer and Communications Security (CCS 2002). Washington, D.C.]] Google Scholar
- Friedman, E. and Resnick, P. 1999. The social cost of cheap pseudonyms. Journal of Economics and Management Strategy, 10, 2 (August), 173--199.]]Google Scholar
- Gnutella. Gnutella website http://gnutella.wego.com.]]Google Scholar
- Goecks, J. and Shavlik, J. 2002. Learning users' interests by unobtrusively observing their normal behavior. In Proceedings of the ACM Intelligent User Interfaces Conference (IUI).]] Google Scholar
- Goldschlag, D., Reed, M., and Syverson, P. 1999. Onion routing for anonymous and private internet connections. Comm. ACM 42, 2 (February), 39--41.]] Google Scholar
- Good, N., Schafer, B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., and Riedl, J. 1999. Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the 1999 Conference of the American Association of Artificial Intelligence (AAAI-99).]] Google Scholar
- Hansell, S. 2002. Privacy policy on web shifts as profits ebb. New York Times (April 11).]]Google Scholar
- Herlocker, J., Konstan, J., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 1999 Conference on Research and Development in Information Retrieval (SIGIR-99).]] Google Scholar
- Jovanovic, M. A. 2001. Modelling large peer-to-peer networks and a case study of gnutella. M.S. thesis, University of Cincinnati.]]Google Scholar
- Karger, D., Lehman, E., Leighton, T., Levine, M., Lewin, D., and Panigrahy, R. 1997. Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the world wide web. In ACM Symposium on Theory of Computing. 654--663.]] Google Scholar
- Karypis, G. 2001. Evaluation of item-based top-n recommendation algorithms. In Proceedings of the 10th Conference of Information and Knowledge Management.]] Google Scholar
- Kubiatowicz, J., Bindel, D., Chen, Y., Eaton, P., Geels, D., Gummadi, R., Rhea, S., Weatherspoon, H., Weimer, W., Wells, C., and Zhao, B. 2000. Oceanstore: An architecture for global-scale persistent storage. In Proceedings of ACM ASPLOS. ACM.]] Google Scholar
- Lyman, P. and Varian, H. 2000. How much information? http://www.sims.berkeley.edu/how-much-info.]]Google Scholar
- Maes, P. 1994. Agents that reduce work and information overload. Comm. ACM 37, 7 (July), 30--40.]] Google Scholar
- Maes, P. and Kozierok, R. 1993. Learning interface agents. In Proceedings of AAAI-93 and IAAI-93. AAAI Press; Menlo Park, CA, USA.]]Google Scholar
- Miller, B., Riedl, J., and Konstan, J. 2002. From Usenet to CoWebs: Interacting with Social Information Systems. Springer Verlag, Chapter Experiences in Applying Collaborative Filtering to a Social Information System.]]Google Scholar
- Minar, N. 1998. Designing an ecology of distributed agents. M.S. thesis, MIT.]]Google Scholar
- Minar, N., Gray, M., Roup, O., Krikorian, R., and Maes, P. 1999. Hive: Distributed agents for networking things. In First International Symposium on Agent Systems and Applications (ASA'99)/Third International Symposium on Mobile Agents (MA'99). Palm Springs, CA, USA.]] Google Scholar
- Mobasher, B., Cooley, R., and Srivastava, J. 2000. Automatic personalization based on Web usage mining. Comm. ACM 43, 8, 142--151.]] Google Scholar
- Mobasher, B., Dai, H., and Tao, M. 2002. Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery 6, 61--82.]] Google Scholar
- Morita, M. and Shinoda, Y. 1994. Information filtering based on user behavior analysis and best match text retrieval. In Proceedings of the 17th International Conference on Research and Development in Information Retrieval. SIGIR 94, W. Croft and C. van Rijsbergen, Eds. Springer-Verlag; Berlin, Germany, 48.]] Google Scholar
- Moukas, A. and Zacharia, G. 1997. Evolving a multi-agent information filtering solution in amalthaea. In Proceedings of Autonomous Agents 97.]] Google Scholar
- Neumann, P. G. 1995. Computer Related Risks. Addison Wesley.]] Google Scholar
- Pedersen, T. 1991. A threshold cryptosystem without a trusted party. In Advances in Cryptology - EUROCRYPT'91 Lecture Notes in Computer Science, D. W. Davies, Ed. Vol. 547. Springer-Verlag, 522--526.]]Google Scholar
- Ratnasamy, S., Francis, P., Handley, M., Karp, R., and Shenker, S. 2001. A scalable content addressable network. In Proceedings of ACM SIGCOMM 2001.]] Google Scholar
- Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P., and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of CSCW 1994. ACM SIG Computer Supported Cooperative Work.]] Google Scholar
- Ripeanu, M., Foster, I., and Iamnitchi, A. 2002. Mapping the gnutella network: Properties of large-scale peer-to-peer systems and implications for system design. IEEE Internet Comput. J. 6, 1.]] Google Scholar
- Rowstron, A. and Druschel, P. 2001a. Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems. Lecture Notes in Computer Science 2218.]] Google Scholar
- Rowstron, A. I. T. and Druschel, P. 2001b. Storage management and caching in PAST, a large-scale, persistent peer-to-peer storage utility. In Symposium on Operating Systems Principles. 188--201.]] Google Scholar
- Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., and Riedl, J. 1998. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In Proceedings of the 1998 Conference on Computer Supported Cooperative Work.]] Google Scholar
- Sarwar, B., Konstan, J., and Riedl, J. 2001. Internet Commerce and Software Agents: Cases, Technologies, and Opportunities. Idea Group, Chapter Distributed Recommender Systems: New Opportunities for Internet Commerce, Idea Group Publishing, Hershey, PA.]]Google Scholar
- Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. 2000a. Analysis of recommender algorithms for e-commerce. In ACM E-Commerce 2000. 158--167.]] Google Scholar
- Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. 2000b. Application of dimensionality reduction in recommender system---a case study. In ACM WebKDD 2000 Web Mining for E-Commerce Workshop.]]Google Scholar
- Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference (WWW10). Hong Kong.]] Google Scholar
- Shardanand, U. and Maes, P. 1995. Social information filtering: Algorithms for automating 'word of mouth'. In Human Factors in Computing Systems CHI '95 Conference Proceedings. 210--217.]] Google Scholar
- Sheth, B. and Maes, P. 1993. Evolving agents for personalized information filtering. In Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications. IEEE Computer Society Press; Los Alamitos, CA, USA.]]Google Scholar
- Shirkey, C. 2001. Peer-to-Peer, Harnessing the Power of Disruptive Technologies. O'Reilly and Associates, Chapter Listening to Napster.]]Google Scholar
- Stoica, I., Morris, R., Karger, D., Kaashoek, F., and Balakrishnan, H. 2001. Chord: A scalable Peer-To-Peer lookup service for internet applications. In Proceedings of the 2001 ACM SIGCOMM Conference. 149--160.]] Google Scholar
- Terveen, L., Hill, W., Amento, B., McDonald, D., and Creter, J. 1997. Phoaks: A system for sharing recommendations. Comm. ACM 40, 3 (Mar.), 59--62.]] Google Scholar
- Ungar, L. and Foster, D. 1998. Clustering methods for collaborative filtering. In Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park California.]]Google Scholar
- Waldman, M. and Mazi, D. 2001. Tangler: a censorship-resistant publishing system based on document entanglements. In ACM Conference on Computer and Communications Security. 126--135.]] Google Scholar
- Waldman, M., Rubin, A. D., and Cranor, L. F. 2000. Publius: A robust, tamper-evident, censorship-resistant, web publishing system. In Proceedings of the 9th USENIX Security Symposium. 59--72.]] Google Scholar
- Whelan, C. 2002. How consumers can strike back if their identity has been stolen. Wall Street Journal (August 21).]]Google Scholar
- Wingfield, N. and Pereira, J. December, 2002. Amazon uses faux suggestions to promote new clothing store. Wall Street Journal (December 4).]]Google Scholar
Index Terms
- PocketLens: Toward a personal recommender system
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