Abstract
In case-based reasoning (CBR) approaches to product recommendation, descriptions of the available products are stored in a case library and retrieved in response to a query representing the user’s requirements. We present an approach to recovery from the retrieval failures that often occur when the user’s requirements are treated as constraints that must be satisfied. Failure to retrieve a matching case triggers a recovery process in which the user is invited to select from a recovery set of relaxations (or sub-queries) of her query that are guaranteed to succeed. The suggested relaxations are ranked according to a simple measure of recovery cost defined in terms of the importance weights assigned to the query attributes. The recovery set for an unsuccessful query also serves as a guide to continued exploration of the product space when none of the cases initially recommended by the system is acceptable to the user
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bergmann R., Breen S., Göker M., Manago M., Wess S. (1999). Developing Industrial Case-Based Reasoning Applications: The INRECA Methodology. Springer-Verlag, Berlin, Heidelberg
Bridge D. (2002). Towards Conversational Recommender Systems: A Dialogue Grammar Approach. In: Aha D. W (eds). Proceedings of the ECCBR-02 Workshop on Mixed-Initiative Case-Based Reasoning. Robert Gordon University, Aberdeen Scotland, pp. 9–22
Bridge D., Ferguson A. (2002). An Expressive Query Language for Product Recommender Systems. Artificial Intelligence Review 18:269–307
Burke R. (2002). Interactive Critiquing for Catalog Navigation in E-Commerce. Artificial Intelligence Review 18:245–267
Burkhard H.-D. (1998). Extending Some Concepts of CBR – Foundations of Case Retrieval Nets. In: Lenz M., Bartsch-Spörl B., Burkhard H.-D., Wess S (eds). Case-Based Reasoning Technology. Springer-Verlag, Berlin Heidelberg, pp. 17–50
Corella F., Kaplan S.J., Wiederhold G., Yesil L. (1984). Cooperative Responses to Boolean Queries, In Proceedings of the First International Conference on Data Engineering, 77–85
Gaasterland T., Godfrey P., Minker J. (1992). An Overview of Cooperative Answering. Journal of Intelligent Information Systems 1:123–157
Godfrey P. (1997). Minimisation in Cooperative Response to Failing Database Queries. International Journal of Cooperative Information Systems 6:95–149
Godfrey P. (1998). Relaxation in Web Search: A New Paradigm for Search by Boolean Queries. http://www.cs.yorku.ca/~godfrey/papers/web98/
Göker M.H., Thompson C.A. (2000). Personalized Conversational Case-Based Recommendation. In: Blanzieri E., Portinale L (eds). Advances in Case-Based Reasoning. Springer-Verlag, Berlin Heidelberg, pp. 99–111
Janas J.M. (1979). How to Not Say Nil – Improving Answers to Failing Queries in Data Base Systems. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, 429–434
Kaplan S.J. (1982). Cooperative Responses from a Portable Natural Language Query System. Artificial Intelligence 19:165–187
McGinty L., Smyth B. (2003). On the Role of Diversity in Conversational Recommender Systems. In: Ashley K. D., Bridge D.G (eds). Case-Based Reasoning Research and Development. Springer-Verlag, Berlin Heidelberg, pp. 276–290
McSherry D. (2002). Recommendation Engineering. In Proceedings of the 15th European Conference on Artificial Intelligence pp. 86–90
McSherry D. (2003a). Coverage-Optimized Retrieval. In Proceedings of the 18th International Joint Conference on Artificial Intelligence, 1349–1350
McSherry D. (2003b). Explanation of Retrieval Mismatches in Recommender System Dialogues. In: McGinty L. (eds). Workshop Proceedings, 5th International Conference on Case-Based Reasoning. University of Science and Technology, Norwegian , pp. 191–199
McSherry D. (2003c). Similarity and Compromise. In: Ashley K.D., Bridge D.G (eds). Case-Based Reasoning Research and Development. Springer-Verlag, Berlin Heidelberg, pp. 291–305
McSherry D. (2004a). Balancing User Satisfaction and Cognitive Load in Coverage-Optimised Retrieval. Knowledge-Based Systems 17:113–119
McSherry D. (2004b). Incremental Relaxation of Unsuccessful Queries. In: González Calero P., Funk P (eds). Advances in Case-Based Reasoning. Springer-Verlag, Berlin Heidelberg, pp. 331–345
Reilly J., McCarthy K., McGinty L. Smyth B. (2004). Dynamic Critiquing. In: González Calero P., Funk P (eds). Advances in Case-Based Reasoning. Springer-Verlag, Berlin Heidelberg, pp. 763–777
Ricci F., Arslan B., Mirzadeh N., Venturini A. (2002). ITR: A Case-Based Travel Advisory System. In: Craw S., Preece A (eds). Advances in Case-Based Reasoning. Springer-Verlag, Berlin Heidelberg, pp. 613–627
Sperner E. (1928). Ein Satz über Untermengen einer Endlichen Menge. Mathematische Zeitschrift 27:544–548
Stahl A. (2002). Defining Similarity Measures: Top-Down vs Bottom-Up. In: Craw S., Preece A (eds). Advances in Case-Based Reasoning. Springer-Verlag, Berlin Heidelberg, pp. 406–420
Thompson C.A., Göker M.H., Langley P. (2004). A Personalized System for Conversational Recommendations. Journal of Artificial Intelligence Research 21:393–428
Wilke W., Lenz M., Wess S. (1998). Intelligent Sales Support with CBR. In: Lenz M., Bartsch-Spörl B., Burkhard H.-D., Wess S. (eds). Case-Based Reasoning Technology. Springer-Verlag, Berlin Heidelberg, pp. 91–113
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mcsherry, D. Retrieval Failure and Recovery in Recommender Systems. Artif Intell Rev 24, 319–338 (2005). https://doi.org/10.1007/s10462-005-9000-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-005-9000-z