Abstract
Recommendation systems are widely adopted in e-commerce businesses for helping customers locate products they would like to purchase. In an earlier work, we introduced a recommendation system, termed Yoda, which employs a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy for large-scale Web-based applications. To reduce the complexity of the hybrid approach, Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows the real-time aggregation based on confidence values of an active user to pre-defined sets of recommendations. In this paper, we extend Yoda to include more recommendation sets. The recommendation sets can be obtained from different sources, such as human experts, web navigation patterns, and clusters of user evaluations.More over, the extended Yoda can learn the confidence values automatically by utilizing implicit users' relevance feedback through web navigations using genetic algorithms (GA). Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%). The experimental results also indicate that the retrieval accuracy is significantly increased by using the GA-based learning mechanism.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
M. Balabanovi, “An adaptive web page recommendation service,” in Proceedings of Autonomous Agents, 1997, pp. 378–385.
M. Balabanovi and Y. Shoham, “Fab, content-based, collaborative recommendation, Communications of the ACM, vol. 40, no. 3, pp. 66–72, 1997.
J. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998, pp. 43–52.
R. Fagin, “Combining fuzzy information from multiple systems,” in Proceedings of Fifteenth ACM Symposyum on Principles of Database Systems, 1996.
D.E. Goldberg, Genetic Algorithms in Search, Optimisation, and Machine Learning, Addison-Wesley: Wokingham, England, 1989.
N. Good, J. Schafer, J. Konstan, J. Borchers, B. Sarwar, J. Herlocker, and J. Riedl, “Combining collaborative filtering with personal agents for better recommendations,” in Proceedings of the 1999 Conference of the American Association of Artifical Intelligence, 1999, pp. 439–446.
J. Holland, Adaption in Natural and Artificial Systems, University of Michigan Press: Ann Arbor, Michigan.
A. Hunter, Sugal Programming manual. http://www.trajan-software.demon.co.uk/sugal.htm, 1995.
B. Kitts, D. Freed, and M. Vrieze. “Cross-sell, a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities,” in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000, pp. 437–446.
E. Knorr, R. Ng, and V. Tucakov, “Distance-based outliers: Algorithms and applications,” The VLDB Journal, vol. 8, no. 3, pp. 237–253, 2000.
D. Knuth, “Seminumerical algorithm,” The Art of Computer Programming vol. 2, 1997.
A. Kohrs and B. Merialdo, “Using category-based collaborative filtering in the active WebMuseum,” in Proceedings of IEEE International Conference on Multimedia and Expo, 2000, vol. 1, pp. 351–354.
J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl, “Applying collaborative filtering to usenet news,” Communications of the ACM, no. 4, p. 3, 1997.
W. Lam, S. Mukhopadhyay, J. Mostafa, and M. Palakal, “Detection of shifts in user interests for personalized information filtering,” in Proceedings of the 19th Int'l ACM-SIGIR Conf on Research and Development in Information Retrieval, 1996, pp. 317–325.
H. Lieberman, N. Dyke, and A. Vivacqua, “Let's Browse, a collaborative Browsing Agent,” Knowledge-Based Systems, vol. 12, pp. 427–431, 1999.
B. Mobasher, R. Cooley, and J. Srivastava, “Automatic personalization based on Web usage mining,” Communications of the ACM, vol. 43, no. 8, pp. 142–151, 2000.
B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, “Web data mining: Effective personalization based on association rule discovery from web usage data,” in Proceeding of the Third International Workshop on Web Information and Data Management, 2001.
A. Moukas, “Amalthea: Information discovery and filtering using a multiagent evolving ecosystem,” in Proceedings of 1st Int. Conf. on The Practical Applications of Intelligent Agents and MultiAgent Technology (PAAM), 1996.
M. Pazzani and D. Billsus, “Learning and revising user profiles: The indentification of interesting web sites,” Machine Learning, vol. 27, pp. 313–331, 1997.
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens, an open architecture for collaborative filtering of netnews,” in Proceedings of ACM conference on Cumputer-Supported CooperativeWork, 1994, pp. 175–186.
Y. Rui, T. Huang, M. Ortega, and S. Mehrotra, “Relevance feedback:Apower tool for interactive content-based image retrieval,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 644–655, 1998.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Application of dimensionality reduction in recommender system—A case study,” in Proceedings of ACM WebKDD 2000 Web Mining for e-Commerce Workshop, 2000.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Analysis of recommendation algorithms for e-Commerce,” in Proceedings of ACM e-Commerce 2000 Conference, 2000.
C. Shahabi, F. Banaei-Kashani, Y.-S. Chen, and D. McLeod, “Yoda: An accurate and scalable web-based recommendation system,” in Proceedings of Sixth International Conference on Cooperative Information Systems (CoopIS 2001), 2001.
C. Shahabi, F. Banaei-Kashani, J. Faruque, and A. Faisal, “Feature matrices: A model for efficient and anonymous web usage mining,” in Proceedings of EC-Web, 2001.
C. Shahabi, F. Banaei-Kashani, and J. Faruque, “A reliable, efficient, and scalable system for web usage data acquisition,” in WebKDD'01 Workshop in Conjunction with the ACM-SIGKDD, 2001.
C. Shahabi, A.M. Zarkesh, J. Adibi, and V. Shah, “Knowledge discovery from users web page navigation,” in Proceedings of the IEEE RIDE97 Workshop, 1997.
U. Shardanand and P. Maes, “Social information filtering, algorithm for automating word of mouth,” in Proceedings on Human Factors in Computing Systems, 1995, pp. 210–217.
B. Sheth and P. Maes, “Evolving agents for personalized information filtering,” in Proceedings of the Ninth IEEE Conference on Artificial Intelligence for Applications, 1993.
A. Tan and C. Teo, “Learning user profiles for personalized information dissemination,” in Proceedings of Int'l Joint Conf. on Neural Network, 1998, pp. 183–188.
L. Wu, C. Faloutsos, K. Sycara, and T. Payne, “FALCON: Feedback adaptive loop for content-based retrieval,” in Proceedings of Int'l. Conf. on Very Large Data Bases, 2000.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Shahabi, C., Chen, YS. An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback. Distributed and Parallel Databases 14, 173–192 (2003). https://doi.org/10.1023/A:1024888710505
Issue Date:
DOI: https://doi.org/10.1023/A:1024888710505