Abstract
Hybrid recommender systems are capable of providing better recommendations than non-hybrid ones. Our approach to hybrid recommenders is the use of prediction strategies that determine which prediction technique(s) should be used at the moment an actual prediction is required. In this paper, we determine whether case-based reasoning can provide more accurate prediction strategies than rule-based predictions strategies created manually by experts. Experiments show that case-based reasoning can indeed be used to create prediction strategies; it can even increase the accuracy of the recommender in systems where the accuracy of the used prediction techniques is highly spread.
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References
Ardissono, L., Gena, C., Torasso, P., Bellifemine, F., Chiarotto, A., Difino, A., Negro, B.: User Modeling and Recommendation Techniques for Personalized Electronic Program Guides. In: Ardissono, L., Maybury, M.T., Kobsa, A. (eds.) Personalization and User-Adaptive Interaction in Digital TV. Kluwer (to appear)
Bennett, P.N., Dumais, S.T., Horvitz, E.: Probabilistic Combination of Text Classifiers Using Reliability Indicators: Models and Results. In: Proceedings of SIGIR 2002, Tampere, Finland, pp. 207–214. ACM, New York (2002)
Billsus, D., Pazzani, M.J., Chen, J.: A Learning Agent for Wireless News Access. In: Proceedings of IUI 2000, New Orleans, USA, pp. 33–36. ACM, New York (2000)
Buczak, A.L., Zimmerman, J., Kurapati, K.: Personalization: Improving Ease-of-Use, Trust and Accuracy of a TV Show Recommender. In: Proceedings of the Workshop Personalization in Future TV 2002, Malaga, Spain, pp. 3–12 (2002)
Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
Herlocker, J.: Understanding and Improving Automated Collaborative Filtering Systems. University of Minnesota (2000)
Herlocker, J., Konstan, J.A.: Content-Independent Task-Focused Recommendation. IEEE Internet Computing 5(6), 40–47 (2001)
Mendes, E., Mosley, N., Watson, I.: A Comparison of CBR Approaches to Web Hypermedia Project Cost Estimation. In: Proceedings of WWW 2002, Honolulu, Hawaii, USA, pp. 272–280. ACM, New York (2002)
Mitchell, T.M.: Machine learning. McGraw-Hill, New York (1997)
Riesbeck, C.K., Schank, R.: Inside CBR. Lawrence Erlbaum Associates, Northvale (1989)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automated Word of Mouth. In: Proceedings of Human factors in computing systems, pp. 210–217. ACM, New York (1995)
Smyth, B., Cotter, P.: A personalised TV listings service for the digital TV age. Knowledge-Based Systems 13(2-3), 53–59 (2000)
Toyama, K., Horvitz, E.: Bayesian Modality Fusion: Probabilistic Integration of Multiple Vision Algorithms for Head Tracking. In: Proceedings of ACCV 2000, Fourth Asian Conference on Computer Vision (2000)
Van Setten, M.: Experiments with a recommendation technique that learns category interests. In: Proceedings of IADIS WWW/Internet 2002, Lisabon, Portugal, pp. 722–725 (2002)
Van Setten, M., Veenstra, M., Nijholt, A.: Prediction Strategies: Combining Prediction Techniques to Optimize Personalisation. In: Proceedings of the Workshop Personalization in Future TV 2002, Malaga, Spain, pp. 23–32 (2002)
Van Setten, M., Veenstra, M., Nijholt, A., van Dijk, B.: Prediction Strategies in a TV Recommender System: Framework and Experiments. In: Proceedings of IADIS WWW/Internet 2003, Algarve, Portugal, pp. 203–210 (2003)
Watson, I.D.: Applying CBR: Techniques for Enterprise Systems. Morgan Kaufmann Publishers Inc., San Francisco (1997)
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van Setten, M., Veenstra, M., Nijholt, A., van Dijk, B. (2004). Case-Based Reasoning as a Prediction Strategy for Hybrid Recommender Systems. In: Favela, J., Menasalvas, E., Chávez, E. (eds) Advances in Web Intelligence. AWIC 2004. Lecture Notes in Computer Science(), vol 3034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24681-7_4
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DOI: https://doi.org/10.1007/978-3-540-24681-7_4
Publisher Name: Springer, Berlin, Heidelberg
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