Abstract
Although model-based Collaborative Filtering approaches have been widely used in recsys in the past few years. But In practical application, where users’ rating data arrives sequentially and frequently, the model-based approaches have to re-trained completely for new records. For users’ social information has been succeed used in recommendation system in previous work. In this paper, we proposed several online collaborative filtering algorithms using users’ social information to improve the performance of online recommender systems. The algorithms can better use the prior rating and the social network information, which compute fast and scalable in large data. The contribution of this paper are mainly two-fold: (1) We propose an online collaborative filtering algorithm which can better use the social information and prior knowledge; (2) We solve the problem of cold start and users with few ratings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Linden, G., Smith, B., York, J.: Amazon.com recommendation: Item-to-item collaborative filterng. IEEE Internet Computing 7, 76–80 (2003)
Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: SIGIR, Salvador, Brazil, pp. 114–121 (2005)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS (2007)
Rennie, J.D.M., Srebro, N.: Fast maximum margin matrix factorization for collaborative prediction. In: ICML 2005, Bonn, Germany, pp. 713–719 (2005)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann Machines for collaborative filtering. In: Proc. 24th Annual International Conference on Machine Learning, pp. 791–798 (2007)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22(1), 89–115 (2004)
Abernethy, J., Canini, K., Langford, J., Simma, A.: Online collaborative filtering. UC Berkeley, Tech. Rep. (2011)
Ling, G., Yang, H., King, I., Lyu, M.R.: Online learning for collaborative filtering. In: IJCNN, pp. 1–8 (2012)
Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: Scalable online collaborative filtering. In: WWW, pp. 271–280 (2007)
Liu, N.N., Zhao, M., Xiang, E.W., Yang, Q.: Online evolutionary collaborative filtering. In: RecSys, pp. 95–102 (2010)
Bedi, P., Kaur, H., Marwaha, S.: Trust based recommender system for semantic web. In: Proc. of IJCAI 2007, pp. 2677–2682 (2007)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proc. of SIGIR 2009, Boston, MA, USA, pp. 203–210 (2009)
Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proc. of RecSys 2007, Minneapolis, MN, USA, pp. 17–24 (2007)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proc. of WSDM 2011, Hong Kong, China (2011)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Computer 42(8), 30–37 (2009)
Weimer, M., Karatzoglou, A., Le, Q.V., Smola, A.J.: Cofi: Rank-maximum margin matrix factorization for collaborative ranking. In: NIPS (2007)
Bell, R., Koren, Y., Volinsky, C.: The BellKor Solution to the Netflix Prize (2008)
Bennett, J., Lanning, S.: The Netflix Prize. KDD Cup and Workshop (2007), http://www.netflixprize.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Z., Lu, H. (2014). Online Recommender System Based on Social Network Regularization. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_61
Download citation
DOI: https://doi.org/10.1007/978-3-319-12637-1_61
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12636-4
Online ISBN: 978-3-319-12637-1
eBook Packages: Computer ScienceComputer Science (R0)