Abstract
Recommender systems can be viewed as prediction systems where we can predict the ratings which represent users’ interest in the corresponding item. Typically, items having the highest predicted ratings will be recommended to the users. But users do not know how certain these predictions are. Therefore, it is important to associate a confidence measure to the predictions which tells users how certain the system is in making the predictions. Many different approaches have been proposed to estimate confidence of predictions made by recommender systems. But none of them provide guarantee on the error rate of these predictions. Conformal Prediction is a framework that produces predictions with a guaranteed error rate. In this paper, we propose a conformal prediction algorithm with item-based collaborative filtering as the underlying algorithm which is a simple and widely used algorithm in commercial applications. We propose different nonconformity measures and empirically determine the best nonconformity measure. We empirically prove validity and efficiency of proposed algorithm. Experimental results demonstrate that the predictive performance of conformal prediction algorithm is very close to its underlying algorithm with little uncertainty along with the measures of confidence and credibility.
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References
Adomavicius, G., Kamireddy, S., Kwon, Y.: Towards more confident recommendations:improving recommender systems using filtering approach based onrating variance. In: Proceedings of (WITS 2007) (2007)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of WWW, pp. 285–295 (2001)
Mazurowski, M.A.: Estimating confidence of individual rating predictions in collaborative filtering recommender systems. Expert Syst. Appl. 40(10), 3847–3857 (2013)
Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9, 371–421 (2008)
Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer, New York (2005)
Proedrou, K., Nouretdinov, I., Vovk, V., Gammerman, A.J.: Transductive confidence machines for pattern recognition. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, p. 381. Springer, Heidelberg (2002)
McNee, S.M., Lam, S.K., Guetzlaff, C., Konstan, J.A., Riedl, J.: Confidence displays and training in recommender systems. In: International Conference on Human-Computer Interaction (2003)
McLaughlin, M.R., Herlocker, J.L.: A collaborative filtering algorithm and evaluation metric that accurately model the user experience. SIGIR 2004, 329–336 (2004)
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer, New York (2011)
Koren, Y., Sill, J.: Ordrec: an ordinal model for predicting personalized item rating distributions. In: RecSys, pp. 117–124 (2011)
Vovk, V., Fedorova, V., Nouretdinov, I., Gammerman, A.: Criteria of Efficiency for Conformal Prediction. Technical report (2014)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Lemire, D., Maclachlan, A.: Slope One Predictors for Online Rating-Based Collaborative Filtering. CoRR abs/cs/0702144 (2007)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.W.: Recommending and evaluating choices in a virtual community of use. In: Proceedings of ACM CHI95 Conference on Human Factors in Computing Systems, pp. 194–201 (1995)
Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009)
Johansson, U., Bostrom, H., Lofstrom, T.: Conformal prediction using decisiontrees. In: IEEE 13th International Conference on Data Mining, pp. 330–339 (2013)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)
Saunders, C., Gammerman, A., Vovk, V.: Transduction with confidence and credibility. In: Proceedings of IJCAI 1999, pp. 722–726 (1999)
Johansson, U., Bostrom, H., Lofstrom, T.: Conformal prediction using decision trees. In: IEEE 13th International Conference on Data Mining, pp. 330–339 (2013)
Papadopoulos, H., Vovk, V., Gammerman, A.: Conformal prediction with neural networks. In: 19th IEEE ICTAI 2007, pp. 388–395 (2007)
Breese, J. S., Heckerman, D., and Kadie, C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on UAI 1998, pp. 43–52 (1998)
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Himabindu, T.V.R., Padmanabhan, V., Pujari, A.K., Sattar, A. (2016). Prediction with Confidence in Item Based Collaborative Filtering. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_11
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DOI: https://doi.org/10.1007/978-3-319-42911-3_11
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