Abstract
Collaborative filtering (CF) is typically used for recommending those items to a user which other like-minded users preferred in the past. User-based collaborative filtering (UbCF) and item-based collaborative filtering (IbCF) are two types of CF with a common objective of estimating target user’s rating for the target item. This paper explores different ways of combining predictions from UbCF and IbCF with an aim of minimizing overall prediction error. In this paper, we propose an approach for combining predictions from UbCF and IbCF through multiple linear regression (MLR) and support vector regression (SVR). Results of the proposed approach are compared with the results of other fusion approaches. The comparison demonstrates the superiority of the proposed approach. All the tests are performed on a large publically available dataset.
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References
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)
Aggarwal, C.C.: Recommender Systems. Springer, Berlin (2016)
Barragáns-MartÃnez, A.B., Costa-Montenegro, E., Burguillo, J.C., Rey-López, M., Mikic-Fonte, F.A., Peleteiro, A.: A hybrid content-based and item-based collaborative filtering approach to recommend tv programs enhanced with singular value decomposition. Inf. Sci. 180(22), 4290–4311 (2010)
Cantador, I., Brusilovsky, P., Kuflik, T.: Second workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In: RecSys. pp. 387–388 (2011)
De Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Combining content-based and collaborative recommendations: a hybrid approach based on bayesian networks. Int. J. Approx. Reason. 51(7), 785–799 (2010)
Liu, Q., Xiong, Y., Huang, W.: Combining user-based and item-based models for collaborative filtering using stacked regression. Chin. J. Electron. 23(4), 712–717 (2014)
Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., Riedl, J.: Movielens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 263–266. ACM (2003)
Patel, R., Thakkar, P., Kotecha, K.: Enhancing movie recommender system. In: International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN pp. 0976–6499 (2014)
Rich, E.: User modeling via stereotypes. Cogn. Sci. 3(4), 329–354 (1979)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)
Thakkar, P., Varma, K., Ukani, V.: Outcome fusion-based approaches for user-based and item-based collaborative filtering. In: International Conference on Information and Communication Technology for Intelligent Systems, pp. 127–135. Springer (2017)
Wang, J., De Vries, A.P., Reinders, M.J.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501–508. ACM (2006)
Yagnik, S., Thakkar, P., Kotecha, K.: Recommending tags for new resources in social bookmarking system. Int. J. Data Min. Knowl. Manag. Process 4(1), 19 (2014)
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Thakkar, P., Varma, K., Ukani, V., Mankad, S., Tanwar, S. (2019). Combining User-Based and Item-Based Collaborative Filtering Using Machine Learning. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-13-1747-7_17
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DOI: https://doi.org/10.1007/978-981-13-1747-7_17
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