Abstract
This paper proposes that there is a substantial relative difference in the performance of information-filtering algorithms as they are applied to different datasets, and that these performance differences can be leveraged to form the basis of an Adaptive Information Filtering System. We classify five different datasets based on metrics such as sparsity, user-item ratio etc, and develop a regression function over these metrics in order to predict suitability of a particular recommendation algorithm to a new dataset, using only the aforementioned metrics. Our results show that the predicted best algorithm does perform better for the new dataset.
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References
Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proc. 15th International Conf. on Machine Learning, pp. 46–54. Morgan Kaufmann, San Francisco (1998)
Cotter, P., Smyth, B.: PTV: Intelligent personalised TV guides. In: Proceedings of the 7th Conference on Artificial Intelligence (AAAI 2000) and of the 12th Conference on Innovative Applications of Artificial Intelligence (IAAI 2000), July 30–3, pp. 957–964. AAAI Press, Menlo Park (2000)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)
Hayes, C., Cunningham, P., Clerkin, P., Grimaldi, M.: Programme-driven music radio (2002)
Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: CIKM, pp. 247–254 (2001)
Melville, P., Mooney, R., Nagarajan, R.: Content-boosted collaborative filtering (2001)
O’Donovan, J., Dunnion, J.: A comparison of collaborative recommendation algorithms over diverse data. In: Proceedings of the National Conference on Artificial Intelligence and Cognitive Science (AICS), Ireland, September 17-19, pp. 101–104 (2003)
O’Mahony, M.P., Hurley, N., Silvestre, G.C.M.: An attack on collaborative filtering. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds.) DEXA 2002. LNCS, vol. 2453, pp. 494–503. Springer, Heidelberg (2002)
Raftery, A.E., Madigan, D., Hoeting, J.A.: Bayesian model averaging for linear regression models. Journal of the American Statistical Association 92(437), 179–191 (1997)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings of ACM CSCW 1994 Conference on Computer-Supported Cooperative Work, Sharing Information and Creating Meaning, pp. 175–186 (1994)
Sarwar, B.M., Karypis, G., Konstan, J.A., Reidl, J.: Itembased collaborative filtering recommendation algorithms. In: World Wide Web, pp. 285–295 (2001)
Smyth, B., Wilson, D., O’Sullivan, D.: Improving the quality of the personalised electronic programme guide. In: Proceedings of the TV 2002 the 2nd Workshop on Personalisation in Future TV, May 2002, pp. 42–55 (2002)
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O’Donovan, J., Dunnion, J. (2004). A Framework for Evaluation of Information Filtering Techniques in an Adaptive Recommender System. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_62
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DOI: https://doi.org/10.1007/978-3-540-24630-5_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21006-1
Online ISBN: 978-3-540-24630-5
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