Abstract
Data Mining, or Knowledge Discovery as it is also known, is becoming increasingly useful in a wide variety of applications. In the following paper, we look at its use in combating some of the traditional issues faced with recommender systems. We discuss our ongoing work which aims to enhance the performance of PTV, an applied recommender system working in the TV listings domain. This system currently combines the results of separate user-based collaborative and case-based components to recommend programs to users. Our extension to this idea operates on the theory of developing a case-based view of the collaborative component itself. By using data mining techniques to extract relationships between programme items, we can address the sparsity/maintenance problem. We also adopt a unique approach to recommendation ranking which combines user similarities and item similarities to provide more effective recommendation orderings. Experimental results corroborate our ideas, demonstrating the effectiveness of data mining in improving recommender systems by providing similarity knowledge to address sparsity, both at user-based recommendation level and recommendation ranking level.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 1999 Conference on Research and Development in Information Retrieval: SIGIR-99. (1999)
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM 40 (1997) 77–87
Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating “word of mouth”. In: Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems. (1995) 210–217
Smyth, B., Cotter, P.: Personalized electronic programme guides. Artificial Intelligence Magazine 21 (2001)
Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B.M., Herlocker, J.L., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the 1999 Conference of the American Association of Artifical Intelligence (AAAI-99). (1999) 439–446
Soboroff, I., Nicholas, C.: Combining content and collaboration in text filtering. In: Proceedings of the IJCAI-99 Workshop on Machine Learning for Information Filtering. (1999)
Balabanović, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM 40 (1997) 66–72
Wilson, D.C., Leake, D.B.: Maintaining case-based reasoners: Dimensions and directions. Computational Intelligence 17 (2001)
Watson, I.: CBR is a methodology not a technology. Knowledge Based Systems 12 (1999) 303–308
O’Sullivan, D., Wilson, D., Smyth, B.: Using collaborative filtering data in case-based recommendation. In: Proceedings of the 15th International FLAIRS Conference. (2002) To Appear.
Fox, S., Leake, D.: Learning to refine indexing by introspective reasoning. In: Proceedings of First International Conference on Case-Based Reasoning, Berlin, Springer Verlag (1995) 431–440
Hipp, J., Nakhaeizadeh, U.G.: Mining association rules: Deriving a superior algorithm by analyzing today’s approaches. In: Proceedings of the 4th European Symposium on Principles of Data Mining and Knowledge Discovery. (2000)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R., eds.: Advances in Knowledge Discovery and Data Mining. AAAI Press (1996) 307–328
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recom-mender algorithms. In: Proceedings of the Tenth International World Wide Web Conference. (2001)
Donald, K.M.: (Use of the fischlar video library system)
Charu Aggarwal, Zheng Sun, P.S.Y.: Finding profile association rules (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sullivan, D.O., Wilson, D., Smyth, B. (2002). Improving Case-Based Recommendation. In: Craw, S., Preece, A. (eds) Advances in Case-Based Reasoning. ECCBR 2002. Lecture Notes in Computer Science(), vol 2416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46119-1_21
Download citation
DOI: https://doi.org/10.1007/3-540-46119-1_21
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44109-0
Online ISBN: 978-3-540-46119-7
eBook Packages: Springer Book Archive