Abstract
Collaborative filtering (CF) has become an effective way to predict useful items. It is the most widespread recommendation technique. It relies on users who share similar tastes and preferences to suggest the items that they might be interested in. Despite its simplicity and justifiability, the collaborative filtering approach experiences many problems, including sparsity, gray sheep and scalability. These problems lead to deteriorating the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user ┐X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques.
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References
Polatidis, N., Georgiadis, C.K.: A dynamic multi-level collaborative filtering method for improved recommendations. Comput. Stand. Interfaces 51, 14–21 (2017)
F. Ortega, B. Zhu, J. Bobadilla, and A. Hernando, “Knowledge-Based Systems CF4J : Collaborative filtering for Java,” Knowledge-Based Syst., vol. 0, pp. 1–6, 2018
Gomez-uribe, C.A., Hunt, N.: The Netflix recommender system_algorithms. Bus. Value Pdf 6(4) (2015)
Celma, O.: Music recommendation and discovery in the long tail. In: Citeulikeorg, pp. 252 (2008)
Callan, J., et al.: Personalisation and recommender systems in digital libraries joint NSF-EU DELOS working group report. Library (Lond) 5(May), 299–308 (2003)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2017)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Ekstrand, M.D.: Collaborative filtering recommender systems. Found. Trends® Human–Computer Interact. 4(2), 81–173 (2011)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. Constr. Build. Mater. 171, 546–557 (2007)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User Adap. Interact. 12(4), 331–370 (2002)
Fu, M., Qu, H., Moges, D., Lu, L.: Attention based collaborative filtering. Neurocomputing 311, 88–98 (2018)
Najafabadi, M.K., Mohamed, A., Onn, C.W.: An impact of time and item influencer in collaborative filtering recommendations using graph-based model. Inf. Process. Manag. 56(3), 526–540 (2019)
Vozalis, E., Margaritis, K.G.: Analysis of recommender systems algorithms. In: 6th Hellenic European Conference Computer Mathematics & its Applications (HERCMA), vol. 2003, pp. 1–14. Athens, Greece (2003)
Jawaheer, G., Szomszor, M., Kostkova, P.: Comparison of implicit and explicit feedback from an online music recommendation service, pp. 47–51 (2006)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. Gastroenterology 1, S415 (2008)
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Informatics J. 16(3), 261–273 (2015)
Tsai, C.F., Hung, C.: Cluster ensembles in collaborative filtering recommendation. Appl. Soft Comput. J. 12(4), 1417–1425 (2012)
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup Workshop, pp. 2–5 (2007)
Agrawal, S., Agrawal, J.: Survey on anomaly detection using data mining techniques. Procedia Comput. Sci. 60(1), 708–713 (2015)
Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Annual Conference on Uncertainty Artificial Intelligence, pp. 43–52 (1998)
Bhaidani, S.: Recommender system algorithms. In: Proceedings of International Conference on Weblogs Social Media ICWSM (2007, 2008)
Riedl, J., Sarwar, B., Karypis, G., Konstan, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of 10th International Conference on WorldWideWeb, pp. 285–295 (200)1
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El Fazziki, A., El Aissaoui, O., El Madani El Alami, Y., Benbrahim, M., El Allioui, Y. (2020). A Novel Collaborative Filtering Approach Based on the Opposite Neighbors’ Preferences. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_77
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DOI: https://doi.org/10.1007/978-981-15-0947-6_77
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