Abstract
Collaborative Filtering (CF) is one of the most effective approaches to engineer recommendation systems. It recommends those items to user which other users with related preferences and tastes liked in the past. User-based and Item-based Collaborative Filtering (IbCF) are two flavours of collaborative filtering. Both of these methods are used to estimate target user’s rating for the target item. In this paper, these methods are implemented and their performance is evaluated on the large dataset. The major attention of this paper is on exploring different ways in which predictions from UbCF and IbCF can be combined to minimize overall prediction error. Predictions from UbCF and IbCF are combined through simple and weighted averaging and performance of these fusion approaches is compared with the performance of UbCf & IbCF when implemented individually. Results are encouraging and demonstrate usefulness of fusion approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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)
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. Inform. Sci. 180(22), 4290–4311 (2010)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)
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. Reasoning 51(7), 785–799 (2010)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inform. Syst. (TOIS) 22(1), 143–177 (2004)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM (1999)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 194–201. ACM Press/Addison-Wesley Publishing Co. (1995)
Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 337–344. ACM (2004)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 77–118. Springer, Boston (2015)
Liu, X., Aggarwal, C., Li, Y.F., Kong, X., Sun, X., Sathe, S.: Kernelized matrix factorization for collaborative filtering. In: SIAM Conference on Data Mining, pp. 399–416 (2016)
Patel, R., Thakkar, P., Kotecha, K.: Enhancing movie recommender system. Int. J. Adv. Res. Eng. Technol. (IJARET), ISSN 0976–6499 (2014)
Rao, N., Yu, H.F., Ravikumar, P.K., Dhillon, I.S.: Collaborative filtering with graph information: Consistency and scalable methods. In: Advances in Neural Information Processing Systems, pp. 2107–2115 (2015)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186. ACM (1994)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
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)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth". In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co. (1995)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Thakkar, P., Varma, K., Ukani, V. (2018). Outcome Fusion-Based Approaches for User-Based and Item-Based Collaborative Filtering. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-319-63645-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-63645-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63644-3
Online ISBN: 978-3-319-63645-0
eBook Packages: EngineeringEngineering (R0)