Abstract
We present a Context Ultra-Sensitive Approach based on two-step Recommender systems (CUSA-2-step-Rec). Our approach relies on a committee of profile-specific neural networks. This approach provides recommendations that are accurate and fast to train because only the URLs relevant to a specific profile are used to define the architecture of each network. Similar to the task of completing the missing pieces of a puzzle, each neural network is trained to predict the missing URLs of several complete ground-truth sessions from a given profile, given as input several incomplete subsessions. We compare the proposed approach with collaborative filtering showing that our approach achieves higher coverage and precision while being faster, and requiring lower main memory at recommendation time. While most recommenders are inherently context sensitive, our approach is context ultra-sensitive because a different recommendation model is designed for each profile separately.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Perkowitz, M., Etzioni, O.: Adaptive web sites: Automatically learning for user access pattern. In: Proc. 6th int. WWW conference (1997)
Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern discovery on the World Wide Web. In: Proc. IEEE Intl. Conf. Tools with AI, Newport Beach, CA, pp. 558–567 (1997)
Nasraoui, R.K., Joshi, A.: Mining Web Access Logs Using a Relational Clustering Algorithm Based on a Robust Estimator. In: 8th International World Wide Web Conference, Toronto, pp. 40–41 (1999)
Nasraoui, Krishnapuram, R., Frigui, H., Joshi, A.: Extracting Web User Profiles Using Relational Competitive Fuzzy Clustering. International Journal on Artificial Intelligence Tools 9(4), 509–526 (2000)
Nasraoui, Krishnapuram, R.: A Novel Approach to Unsupervised Robust Clustering using Genetic Niching. In: Proc. of the 9th IEEE International Conf. on Fuzzy Systems, San Antonio, TX, May 2000, pp. 170–175 (2000)
Nasraoui, Krishnapuram, R.: A New Evolutionary Approach to Web Usage and Context Sensitive Associations Mining. International Journal on Computational Intelligence and Applications - Special Issue on Internet Intelligent Systems 2(3), 339–348 (2002)
Pazzani, M., Billsus, D.: Learning and revising User Profiles: The identification of Interesting Web Sites. Machine Learning, Arlington 27, 313–331 (1997)
Kraft, D.H., Chen., J., Martin-Bautista, M.J., Vila, M.A.: Textual Information Retrieval with User Profiles Using Fuzzy Clustering and Inferencing. In: Szczepaniak, P.S., Segovia, J., Kacprzyk, J., Zadeh, L.A. (eds.) Intelligent Exploration of the Web. Physica-Verlag, Heidelberg (2002)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalizaton based on association rule discovery from Web usage data. In: ACM Workshop on Web information and data management, Atlanta, GA (November 2001)
Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1975)
Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Prentice Hall, Englewood Cliffs (1995)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, Santiago, Chile, pp. 487–499 (1994)
Linden, G., Smith, B., York, J.: Amazon.com Recommendations Item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Breese, J., Heckerman, H., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. 14th Conf. Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Schafer, J.B., Konstan, J., Reidel, J.: Recommender Systems in E-Commerce. In: Proc. ACM Conf. E-commerce, pp. 158–166 (1999)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1(2), 1–12 (2000)
Zaiane, M.X., Han, J.: Discovering web access patterns and trends by applying OLAP and data mining technology on web logs. In: Advances in Digital Libraries, Santa Barbara, CA, pp. 19–29 (1998)
Spiliopoulou, M., Faulstich, L.C.: WUM: A Web utilization Miner. In: Proceedings of EDBT workshop WebDB 1998, Valencia, Spain (1999)
Borges, J., Levene, M.: Data Mining of User Navigation Patterns. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS, vol. 1836, pp. 92–112. Springer, Heidelberg (2000)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, New York (1994)
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nasraoui, O., Pavuluri, M. (2006). Complete This Puzzle: A Connectionist Approach to Accurate Web Recommendations Based on a Committee of Predictors. In: Mobasher, B., Nasraoui, O., Liu, B., Masand, B. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2004. Lecture Notes in Computer Science(), vol 3932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11899402_4
Download citation
DOI: https://doi.org/10.1007/11899402_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47127-1
Online ISBN: 978-3-540-47128-8
eBook Packages: Computer ScienceComputer Science (R0)