Abstract
Recommendations are crucial for the success of large websites. While there are many ways to determine recommendations, the relative quality of these recommenders depends on many factors and is largely unknown. We present the architecture and implementation of AWESOME (Adaptive website recommendations), a data warehouse-based recommendation system. It allows the coordinated use of a large number of recommenders to automatically generate website recommendations. Recommendations are dynamically selected by efficient rule-based approaches utilizing continuously measured user feedback on presented recommendations. AWESOME supports a completely automatic generation and optimization of selection rules to minimize website administration overhead and quickly adapt to changing situations. We propose a classification of recommenders and use AWESOME to comparatively evaluate the relative quality of several recommenders for a sample website. Furthermore, we propose and evaluate several rule-based schemes for dynamically selecting the most promising recommendations. In particular, we investigate two-step selection approaches that first determine the most promising recommenders and then apply their recommendations for the current situation. We also evaluate one-step schemes that try to directly determine the most promising recommendations.
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Jameson, A., Konstan, J., Riedl, J.: AI techniques for personalized recommendation. In: Tutorial at 18th National Conference on Artificial Intelligence (AAAI) (2002)
Koutri, M., Daskalaki, S., Avouris, N.: Adaptive interaction with web sites: an overview of methods and techniques. In: Proceedings of the 4th International Workshop on Computer Science and Information Technologies (CSIT) (2002)
Linden, G., Smith, B., York, J.: Amazon.com Recommendations: item-to-item collaborative filtering. IEEE Distribut. Syst. Online 4(1) (2003)
Perkowitz, M., Etzioni, O.: Adaptive web sites: an AI challenge. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence. Morgan Kaufmann (1997)
Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowledge Inform. Syst. 1(1) (1999)
Tan, P., Kumar, V.: Modeling of web robot navigational patterns. In: Proceedings of the ACM WebKDD Workshop (2000)
Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A framework for the evaluation of session reconstruction heuristics in web usage analysis. Special issue on mining web-based data for e-business applications. INFORMS J. Comput. 15(2) (2003)
Kimball, R., Merz, R.: The Data Webhouse Toolkit – Building Web-Enabled Data Warehouse. Wiley Computer Publishing, New York (2000)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4) (2002)
Schafer, J.B., Konstan, J.A., Riedl, J.: Electronic commerce recommender applications. J. Data Min. Knowledge Discov. 5(1/2) (2001)
Terveen, L., Hill, W.: Human–computer collaboration in recommender systems. In: Carroll, J. (ed.) Human Computer Interaction in the New Millenium. Addison-Wesley, New York (2001)
Kushmerick, N., McKee, J., Toolan, F.: Toward zero-input personalization: referrer-based page recommendation. In: Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-based Systems (2000)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Min. Knowledge Discov. 6(1) (2002)
Cosley, D., Lawrence, S., Pennock, D.M.: REFEREE: an open framework for practical testing of recommender systems using ResearchIndex. In: Proceedings of the 28th VLDB Conference (2002)
Gray, J., Bosworth, A., Layman, A., Pirahesh, H.: Data cube: a relational aggregation operator generalizing groupby, cross-tab, and sub-total. In: Proceedings of the 12th EEE International Conference on Data Engineering (ICDE) (1995)
Witten, I.H., Frank, E.: Data Mining. Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann (2000)
Golovin, N., Rahm, E.: Reinforcement learning architecture for web recommendations. In: Proceedings of the International Conference on Information Technology (ITCC), Vol. 1, Las Vegas (2004)
Golovin, N., Rahm, E.: Automatic optimization of web recommendations using feedback and ontology graphs. In: Proceedings of the International Conference on Web Engineering (ICWE), Sydney, LNCS 3579, Springer Verlag (2005), submitted for publication
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA (1998)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-T.: Web usage mining: discovery and applications of usage patterns from web Data. SIGKDD Explor. 1(2) (2000)
Schafer, J.B., Konstan, J.A., Riedl, J.: Meta-recommendation systems: user-controlled integration of diverse recommendations. In: Proceedings of the 11th International Conference on Information and Knowledge Management (CIKM) (2002)
Mobasher, B., Nakagawa, M.: A hybrid web personalization model based on site connectivity. In: Proceedings of the ACM WebKDD Workshop (2003)
Shahabi, C., Chen, Y.: An adaptive recommendation system without explicit acquisition of user relevance feedback. Distribut. Parallel Databases 14(2) (2003)
Lim, M., Kim, J.: An adaptive recommendation system with a coordinator agent. In: Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development (2001)
Kim, K., Carroll, J.M., Rosson, M.B.: An empirical study of web personalization assistants: supporting end-users in web information systems. In: Proceedings of the IEEE 2002 Symposium on Human Centric Computing Languages and Environments (2002)
Geyer-Schulz, A., Hahsler, M.: Evaluation of recommender algorithms for an internet information broker based on simple association rules and on the repeat-buying theory. In: Proceedings of the ACM WebKDD Workshop (2002)
Heer, J., Chi, E.H.: Separating the swarm: categorization methods for user sessions on the web. In: Proceedings of the Conference on Human Factors in Computing Systems (2002)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of ACM E-Commerce (2000)
Hayes, C., Massa, P., Avesani, P., Cunningham, P.: An on-line evaluation framework for recommender systems. In: Proceedings of Workshop on Personalization and Recommendation in E-Commerce (2002)
Shahabi, C., Banaei-Kashani, F., Faruque, J.: A reliable, efficient, and scalable system for web usage data acquisition. In: Proceedings of the ACM WebKDD Workshop (2001)
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Thor, A., Golovin, N. & Rahm, E. Adaptive website recommendations with AWESOME. The VLDB Journal 14, 357–372 (2005). https://doi.org/10.1007/s00778-005-0160-x
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DOI: https://doi.org/10.1007/s00778-005-0160-x