Abstract
Recommendation, in the filed of machine learning, is known as a technique of identifying user preferences to new items with ratings from recommender systems. Recently, one novel recommendation model using Green’s function treats recommendation as the process of label propagation. Although this model outperforms many standard recommendation methods, it suffers from information loss during graph construction because of data sparsity. In this paper, aiming at solving this problem and improving prediction accuracy, we propose an enhanced semi-supervised Green’s function recommendation model. The main contributions are two-fold: 1) To reduce information loss, we propose a novel graph construction method with global and local consistent similarity; 2) We enhance the recommendation algorithm with the multi-class semi-supervised learning framework. Finally, experimental results on real world data demonstrate the effectiveness of our model.
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
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17(6), 734–749 (2005)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1), 177 (2004)
Ding, C., Simon, H., Jin, R., Li, T.: A learning framework using Green’s function and kernel regularization with application to recommender system. In: Proceedings of the 13th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, p. 269. ACM, New York (2007)
Dueck, D., Frey, B.: Probabilistic sparse matrix factorization. University of Toronto technical report PSI-2004-23 (2004)
Jin, R., Chai, J., 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, New York (2004)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Computer 42(8), 30–37 (2009)
Ma, H., King, I., Lyu, M.: Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 46. ACM, New York (2007)
McLaughlin, M., Herlocker, J.: A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: Proceedings of the 27th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 329–336. ACM, New York (2004)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. Advances in neural information processing systems 20, 1257–1264 (2008)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, p. 295. ACM, New York (2001)
Wang, F., Ma, S., Yang, L., Li, T.: Recommendation on item graphs. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 1119–1123. Springer, Heidelberg (2006)
Wang, J., De Vries, A., Reinders, M.: 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, p. 508. ACM, New York (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, D., King, I. (2010). An Enhanced Semi-supervised Recommendation Model Based on Green’s Function. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-17537-4_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17536-7
Online ISBN: 978-3-642-17537-4
eBook Packages: Computer ScienceComputer Science (R0)