Abstract
Recommender Systems (RSs) in particular the collaborative filtering approaches have reached a high level of popularity. These approaches are designed for predicting the user’s future interests towards unrated items. However, the provided predictions should be taken with restraint because of the uncertainty pervading the real-world problems. Indeed, to not give consideration to such uncertainty may lead to unrepresentative results which can deeply affect the predictions’ accuracy as well as the user’s confidence towards the RS. In order to tackle this issue, we propose in this paper a new evidential item-based collaborative filtering approach. In our approach, we involve the belief function theory tools as well as the Evidential K-Nearest Neighbors (EKNN) classifier to deal with the uncertain aspect of items’ recommendation ignored by the classical methods. The performance of our new recommendation approach is proved through a comparative evaluation with several traditional collaborative filtering recommenders.
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Bobadilla, J., Ortega, F., Hernando, A., Gutierrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)
Koren, Y., Sill, J.: Collaborative filtering on ordinal user feedback. In: International Joint Conference on Artificial Intelligence, pp. 3022–3026 (2013)
Zenebe, A., Norcio, A.F.: Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst. 160(1), 76–94 (2009)
Boulkrinat, S., Hadjali, A., Aissani Mokhtari, A.: Handling preferences under uncertainty in recommender systems. In: IEEE International Conference on Fuzzy Systems, pp. 2262–2269 (2014)
Yu, K., Schwaighofer, A., Tresp, V., Xu, X., Kriegel, H.-P.: Probabilistic memory-based collaborative filtering. IEEE Trans. Knowl. Data Eng. 16(1), 56–69 (2004)
Slokom, M., Ayachi, R.: Towards a new possibilistic collaborative filtering approach. In: Second International Conference on Computer Science, Computer Engineering, and Social Media, pp. 209–216 (2015)
Smets, P.: The transferable belief model for quantified belief representation. In: Smets, P. (ed.) Quantified Representation of Uncertainty and Imprecision, pp. 267–301. Springer, Heidelberg (1998)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Heidelberg (2011)
Gu, Y., Zhao, B., Hardtke, D., Sun, Y.: Learning global term weights for content-based recommender systems. In: Proceedings of the 25th International Conference on World Wide Web, pp. 391–400 (2016)
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)
Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72079-9_12
Zheng, Y., Ouyang, Y., Rong, W., Xiong, Z.: Multi-faceted distrust aware recommendation. In: Zhang, S., Wirsing, M., Zhang, Z. (eds.) KSEM 2015. LNCS (LNAI), vol. 9403, pp. 435–446. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25159-2_39
Zhao, Z.D., Shang, M.S.: User-based collaborative-filtering recommendation algorithms on hadoop. In: Third International Conference on Knowledge Discovery and Data Mining, pp. 478–481 (2010)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web, pp. 285–295 (2001)
Dempster, A.P.: A generalization of bayesian inference. J. R. Stat. Soc. Ser. B (Methodol.) 30, 205–247 (1968)
Shafer, G.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)
Denoeux, T.: A K-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25(5), 804–813 (1995)
Su, X., Khoshgoftaar, T.M.: Collaborative filtering for multi-class data using bayesian networks. Int. J. Artif. Intell. Tools 17(01), 71–85 (2008)
Sanchez, J., Serradilla, F., Martinez, E., Bobadilla, J.: Choice of metrics used in collaborative filtering and their impact on recommender systems. In: IEEE International Conference on Digital Ecosystems and Technologies, pp. 432–436 (2008)
Bobadilla, J., Hernando, A., Ortega, F., Bernal, J.: A framework for collaborative filtering recommender systems. Expert Syst. Appl. 38(12), 14609–14623 (2011)
Pennock, D.M., Horvitz, E., Lawrence, S., Giles, C.L.: Collaborative filtering by personality diagnosis: a hybrid memory-and model-based approach. In: The Conference on Uncertainty in Artificial Intelligence, pp. 473–480 (2000)
Bennett, J., Lanning, S.: The netflix prize. In: KDD Cup and Workshop, vol. 2007, p. 35 (2007)
Elouedi, Z., Mellouli, K., Smets, P.: Assessing sensor reliability for multisensor data fusion within the transferable belief model. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(1), 782–787 (2004)
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Abdelkhalek, R., Boukhris, I., Elouedi, Z. (2016). Evidential Item-Based Collaborative Filtering. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_49
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