Abstract
Recommender Systems use Information Filtering techniques to manage user preferences and provide the user with options, which will present greater possibility to satisfy them. Among these techniques, Content Based Filtering recommend new items by comparing them with a user profile, usually expressed as a set of items given by the user. This comparison is often performed using the k-NN method, which presents efficiency problems as the user profile grows. This paper presents an approach where each user profile is modeled by a meta-prototype and the comparison between an item and a profile is based on a suitable matching function. We show experimentally that our approach clearly outperforms the k-NN method while they presenting equal or even better prediction accuracy. The meta-prototype approach performs slightly worse than kd-tree speed up method but it exhibits a significant gain in prediction accuracy.
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References
M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining Content-Based and Collaborative Filters in an Online Newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems, August 19 1999.
Joshua Alspector, Aleksander Kolcz, and Nachimuthu Karunanithi. Comparing Feature-Based and Clique-Based User Models for Movie Selection. In Proceedings of the Third ACM Conference on Digital Libraries, pages 11–18, 1998.
Smyth, B. & Cotter, P. (1999) Surfing the Digital Wave: Generating Personalised TV Listings using Collaborative, Case-Based Recommendation. Proceedings of the 3rd International Conference on Case-Based Reasoning, Munich, Germany, 561–571.
Arya S.: Nearest Neighbor Searching and Applications, Ph.D. thesis, University of Maryland, College Park, MD, 1995.
Bentley J., “Multidimensional binary search trees used for associative searching”, Communications of the ACM, Vol. 18, pp. 509–517, 1975.
De Carvalho, F.A.T., Souza, R.M.C.M. and Verde, R. (submitted): A Modal Symbolic Pattern Classier.
Cover, T. M., and P. E. Hart (1967). Nearest Neighbor Classifiers. IEEE Transactions on Computers, 23–11, November, 1974, pp. 1179–1184.
D. R. Wilson and T. R. Martinez. Reduction techniques for exemplar-based learning algorithms. Machine Learning, 38(3):257–268, 2000.
Domingos, Pedro (1995). Rule Induction and Instance-Based Learning: A Unified Approach, to appear in The 1995 International Joint Conference on Artificial Intelligence (IJCAI-95).
Bock, H. H. and Diday, E. (2000): Analysis of Symbolic Data. Springer, Heidelberg.
Baeza, Y. and Ribeiro, N. Modern Information Retrieval.
Ichino, M. and Yaguchi, H. (1994): Generalized Minkowsky Metrics for Mixed Feature Type Data Analysis. IEEE Transactions system, Man and Cybernetics, 24, 698–708
Verde, R., De Carvalho, F.A.T. and Lechevallier, Y. (2000): A Dynamical Clustering Algorithm for Symbolic Data, in 25th Annual Conference of the Germany Classification Society, Munich (Germany), 59–72
McJones, P. (1997). EachMovie collaborative filtering data set. DEC Systems Research Center. http://www.research.digital.com/SRC/eachmovie/
Herlocker, Jonathan Lee. Understanding and Improving Automated Collaborative Filtering Systems, cp 3.
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© 2002 Springer-Verlag Berlin Heidelberg
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Bezerra, B., de Carvalho, F.d.A.T., Ramalho, G.L., Zucker, J.D. (2002). Speeding up Recommender Systems with Meta-prototypes. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_22
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DOI: https://doi.org/10.1007/3-540-36127-8_22
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