Similarity vs. Diversity
Case-based reasoning systems usually accept the conventional similarity assumption during retrieval, preferring to retrieve a set of cases that are maximally similar to the target problem. While we accept that this works well in many domains, we suggest that in others it is misplaced. In particular, we argue that often diversity can be as important as similarity. This is especially true in case-based recommender systems. In this paper we propose and evaluate strategies for improving retrieval diversity in CBR systems without compromising similarity or efficiency.
KeywordsRecommender System Greedy Algorithm Retrieval Strategy Target Problem Greedy Selection
Unable to display preview. Download preview PDF.
- 1.Aamodt, A. and Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1):39–52, 1994.Google Scholar
- 2.Bergmann, R., Richter, M., Schmitt, S., Stahl, A. and Vollrath, I.: Utility-SOriented Matching: A New Research Direction for Case-Based Reasoning. In: Proceedings of the German Workshop on Case-Based Reasoning, 2001.Google Scholar
- 4.Burke, R.: A case-based approach to collaborative filtering. In: Proceedings of the 5th European Workshop on Case-Based Reasoning. Springer-Verlag, 2000.Google Scholar
- 5.Faltings, B.: Probabilistic Indexing for Case-Based Prediction. In: Proceedings of the 2nd International Conference on Case-Based Reasoning, pages 611–622. Springer-Verlag, 1997.Google Scholar
- 6.S. Fox, S. and Leake, D. B.: Using Introspective Reasoning to Refine Indexing. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 391–397. Morgan Kaufmann, 1995.Google Scholar
- 7.Kolodner, J.: Judging which is the “best” case for a case-based reasoner. In: Proceedings of the Second Workshop on Case-Based Reasoning, pages 77–81. Morgan Kaufmann, 1989.Google Scholar
- 8.Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, 1993.Google Scholar
- 10.Leake, D. B.: Case-Based Reasoning: Experiences,Lessons and Future Directions. AAAI/MIT Press, 1996.Google Scholar
- 11.Leake, D. B.: Constructive Similarity Assessment: Using Stored Cases to Define New Situations. In: Proceedings of the 14th Annual Conference of the Cognitive Science Society, pages 313–318. Lawrence Earlbaum Associates, 1992.Google Scholar
- 14.Smyth, B.: and McKenna, E.: Incremental Footprint-Based Retrieval. In: Proceedings of the 21st SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, pages 89–101. Springer Verlag, 2000.Google Scholar