Abstract
Recommender systems are highly sensitive to cases of false-positives, that is, recommendations made which have proved not to be relevant. These situations often lead to a loss of trust in the system by the users; therefore, every improvement in the recommendation quality measures is important. Recommender systems which admit an extensive set of values in the votes (usually those which admit more than 5 stars to rate an item) cannot be assessed adequately using precision as a recommendation quality measure; this is due to the fact that the division of the possible values of the votes into just two sets, relevant (true-positive) and not-relevant (false-positive), proves to be too poor and involves the accumulation of values in the not-relevant set. In order to establish a balanced quality measure it is necessary to have access to detailed information on how the cases of false-positives are distributed. This paper provides the mathematical formalism which defines the precision quality measure in recommender systems and its generalization to extended-precision.
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References
Konstan, J.A., Miller, B.N., Riedl, J.: PocketLens: toward a personal recommender system. ACM Trans. on Inf. Syst. 22(3), 437–476 (2004)
Antonopoulus, N., Salter, J.: Cinema screen recommender agent: combining collaborative and content-based filtering. IEEE Intell. Syst., 35–41 (2006)
Li, P., Yamada, S.: A movie recommender system based on inductive learning. In: IEEE Conf. on Cybern. and Intell. Syst., vol. 1, pp. 318–323 (2004)
Jinghua, H., Kangning, W., Shaohong, F.: A survey of e-commerce recommender systems. In: Int. Conf. on Service Syst. and Service Management, pp. 1–5 (2007)
Denis, H.: Managing collaborative learning processes, e-learning applications. In: 29th Int. Conf. on Inf. Technol. Interfaces, pp. 345–350 (2007)
Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative Filtering adapted to Recommender Systems of e-learning. Knowl. Based Syst. 22, 261–265 (2009)
Lang, K.: NewsWeeder: Learning to filter netnews. In: 12th Int. Conf. on Machine Learning, Tahoe City, CA (1995)
Krulwich, B.: Lifestyle Finder: Intelligent user profiling using large-scale demographic data. Artificial Intell. Magazine 18(2), 37–45 (1997)
Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowl. and Data Eng. 17(6), 734–749 (2005)
Herlocker, J.L., Konstan, J.A., Riedl, J.T., Terveen, L.G.: Evaluating collaborative filtering recommender systems. ACM Transactions on Inf. Syst. 22(1), 5–53 (2004)
Gao, L.Q., Li, C.: Hybrid personalizad recommended model based on genetic algorithm. In: Int. Conf. on Wireless Commun. Netw. and Mob. Computing, pp. 9215–9218 (2008)
Ho, Y., Fong, S., Yan, Z.: A hybrid ga-based collaborative filtering model for online recommenders. In: Int. Conf. on e-Business, pp. 200–203 (2007)
Al-Shamri, M.Y., Bharadwaj, K.K.: Fuzzy-genetic approach to recommender Systems based on a novel hybrid user model. Expert Syst. with Applications 35, 1386–1399 (2008)
Huang, Z., Zeng, D., and Chen, H.: A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Intelligent Systems, 68-78 (2007)
Sanchez, J.L., Serradilla, F., Martinez, E., Bobadilla, J.: Choice of metrics used in collaborative filtering and their impact on recommender systems. In: Proceedings of the IEEE International Conference on Digital Ecosystems and Technologies (DEST 2008), pp. 432–436 (2008)
Bobadilla, J., Serradilla, F., Bernal, J.: A New Collaborative Filtering Metric that Improves the Behavior of Recommender Systems. Knowl. Based Syst. 23, 520–528 (2010)
Ryan, P.B., Bridge, D.: Collaborative Recommending using Formal Concept Analysis. Knowl. Based Syst. 19(5), 309–315 (2006)
Leung, C.W., Chan, S.C., Chung, F.L.: An empirical study of a cross-level association rule mining approach to cold-start recommendations. Knowledge Based Systems 21(7), 515–529 (2008)
Hernández, F., Gaudioso, E.: Evaluation of Recommender Systems: a New Approach. Expert Syst. with Appl. (35), 790–804 (2008)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. 4(2), 133–151 (2001)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: 14th Conf. on Uncertain. in Artif. Intell., pp. 43–52. Morgan Kaufmann (1998)
Candillier, L., Meyer, F., Boullé, M.: Comparing State-of-the-art Collaborative Filtering Systems. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 548–562. Springer, Heidelberg (2007)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
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Ortega, F., Hernando, A., Bobadilla, J. (2011). Extended Precision Quality Measure for Recommender Systems. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_44
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DOI: https://doi.org/10.1007/978-3-642-25274-7_44
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