Improving Case-Based Recommendation
A Collaborative Filtering Approach

Derry O Sullivan, David Wilson, and Barry Smyth

Smart Media Institute
University College Dublin
{dermot.osullivan,david.wilson,barry.smyth}@ucd.ie



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.
References

1.	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)
2.	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) 7787
3.	Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating word
of mouth. In: Proceedings of ACM CHI95 Conference on Human Factors in Computing
Systems. (1995) 210217
4.	Smyth, B., Cotter, P.: Personalized electronic programme guides. Artificial Intelligence
Magazine 21 (2001)
5.	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 Artificial Intelligence
(AAAI-99). (1999) 439446
6.	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)
7.	Balabanovic, M., Shoham, Y: Fab: Content-based, collaborative recommendation. Communications 
of the ACM 40 (1997) 6672
8.	Wilson, D.C., Leake, D.B.: Maintaining case-based reasoners: Dimensions and directions.
Computational Intelligence 17 (2001)
9.	Watson, I.: CBR is a methodology not a technology. Knowledge Based Systems 12 (1999)
303308
10.	OSullivan, D., Wilson, D., Smyth, B.: Using collaborative filtering data in case-based recommendation. 
In: Proceedings of the 15th International FLAIRS Conference. (2002) To
Appear.
11.	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)
431440
12.	Hipp, J., Nakhaeizadeh, U.G.: Mining association rules: Deriving a superior algorithm by
analyzing todays approaches. In: Proceedings of the 4th European Symposium on Principles
of Data Mining and Knowledge Discovery. (2000)
13.	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) 307328
14.	Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommender 
algorithms. In: Proceedings of the Tenth International World Wide Web Conference.
(2001)
15.	Donald, K.M.: (Use of the fischlar video library system)
16.	Charu Aggarwal, Zheng Sun, P.S.Y.: Finding profile association rules (1998)
