Abstract
Recently we presented a novel approach to the discovery of recommendation rules from a product case base that take account of all features of a recommended product, including those with respect to which the user’s preferences are unknown. In this paper, we investigate the potential role of default preferences in the discovery of recommendation rules. As we show in the domain of digital cameras, the potential benefits include a dramatic reduction in the effective length of the discovered rules and increased coverage of queries representing the user’s personal preferences. Another important finding of the research presented is that in a recommender system that takes account of default preferences, many of the products in the case base may never be recommended.
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References
Wilke, W., Lenz, M., Wess, S.: Intelligent Sales Support with CBR. In: Lenz, M., Bartsch-Spòrl, B., Burkhard, H.-D., Wess, S. (eds.) Case-Based Reasoning Technology. Springer-Verlag, Berlin Heidelberg New York (1998) 91–113
McSherry, D., Stretch, C: Automating the Discovery of Recommendation Knowledge. Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (2005) 9–14
McSherry, D.: Incremental Nearest Neighbour with Default Preferences. Proceedings of the Sixteenth Irish Conference on Artificial Intelligence and Cognitive Science (2005) 9–18
McSherry, D.: Increasing Dialogue Efficiency in Case-Based Reasoning Without Loss of Solution Quality. Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (2003) 121–12
McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: Experiments in Dynamic Critiquing. Proceedings of the International Conference on Intelligent User Interfaces (2005) 175–182
Bergmann, R., Breen, S., Göker, M., Manago, M., Wess, S.: Developing Industrial Case-Based Reasoning Applications: The INRECA Methodology. Springer-Verlag, Berlin Heidelberg New York (1999)
McSherry, D.: Similarity and Compromise. In: Ashley, K.D., Bridge, D.G. (eds.) Case-Based Reasoning Research and Development. LNAI, Vol. 2689. Springer-Verlag, Berlin Heidelberg New York (2003) 291–305
Burke, R.: Interactive Critiquing for Catalog Navigation in E-Commerce. Artificial Intelligence Review, 18 (2002) 245–267
Linden, G., Hanks, S., Lesh, N.: Interactive Assessment of User Preference Models: The Automated Travel Assistant. Proceedings of the Sixth International Conference on User Modeling (1997) 67–78
McSherry, D.: Conversational CBR in Multi-Agent Recommendation. IJCAI-05 Workshop on Multi-Agent Information Retrieval and Recommender Systems (2005) 33–40
McSherry, D.: Automating the Discovery of Recommendation Rules. In: Bramer, M., Coenen, F., Allen, T. (eds.) Research and Development in Intelligent Systems XXI. Springer-Verlag, London (2004) 87–100
Shimazu, H.: ExpertClerk: A Conversational Case-Based Reasoning Tool for Developing Salesclerk Agents in E-Commerce Webshops. Artificial Intelligence Review 18 (2002) 223–244
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McSherry, D., Stretch, C. (2006). Recommendation Knowledge Discovery. In: Bramer, M., Coenen, F., Allen, T. (eds) Research and Development in Intelligent Systems XXII. SGAI 2005. Springer, London. https://doi.org/10.1007/978-1-84628-226-3_19
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DOI: https://doi.org/10.1007/978-1-84628-226-3_19
Publisher Name: Springer, London
Print ISBN: 978-1-84628-225-6
Online ISBN: 978-1-84628-226-3
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