A Novel Similarity Measure Based on Weighted Bipartite Network for Collaborative Filtering Recommendation

Abstract:

This paper presents a novel approach to compute user similarity based on weighted bipartite network and resource allocation principle for collaborative filtering recommendation. The key is to calculate the asymmetric user weighted matrix and translate it into a symmetric user similarity matrix. We carry out extensive experiments over Movielens data set and demonstrate that the proposed approach can yield better recommendation accuracy and can partly to alleviate the trouble of sparseness. Compare with traditional collaborative filtering recommendation algorithms based on Pearson correlation similarity and adjusted cosine similarity, the proposed method can improve the average predication accuracy by 6.7% and 0.6% respectively.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 1834-1837
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.1834
• Online since: December 2012
• Authors: Jian Xun Xia, Fei Wu, Chang Sheng Xie
• Keywords: Collaborative Filtering (CF), Personal Recommendation, Similarity Measure, Weighted Bipartite Network

References

[1] G. Z and C. Lei, Study on e-commerce recommendation based on content analysis, in E-Business and E-Government (ICEE), 2011 International Conference on, may 2011, p.1–4.

[2] B. Sarwar, G. Karypis, J. Konstan and J. Reidl, Item-based collaborative filtering recommendation algorithms, in Proceedings of the 10th international conference on World Wide Web, ser. WWW '01, 2001, p.285–295.

DOI: 10.1145/371920.372071

[3] S. Zhang, W. Wang, J. Ford, F. Makedon and J. Pearlman, Using singular value decomposition approximation for collaborative filtering, in E-Commerce Technology, 2005. CEC 2005. Seventh IEEE International Conference on, july 2005, p.257–264.

DOI: 10.1109/icect.2005.102

[4] J. Mai, Y. Fan and Y. Shen, A neural networks-based clustering collaborative filtering algorithm in e-commerce recommendation system, in Web Information Systems and Mining, 2009. WISM 2009. International Conference on, nov. 2009, p.616–619.

DOI: 10.1109/wism.2009.129

[5] Z. He, The study of personalized recommendation based on web data mining, in Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on, may 2011, p.386 –390.

DOI: 10.1109/iccsn.2011.6014747

[6] G. Dou, Y. Zhu and Y. Han, Research on selection system based on bayesian recommendation model, in Advanced Mechatronic Systems (ICAMechS), 2011 International Conference on, aug. 2011, p.35–38.

[7] M. Zanin, P. Cano, J. M. Buld´u and O. Celma, Complex networks in recommendation systems, in Proceedings of the 2nd WSEAS International Conference on Computer Engineering and Applications, ser. CEA'08. Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS), 2008, p.120–124.

[8] Y. Zhang, M. Blattner and Y. Yu, Heat conduction process on community networks as a recommendation model, Phys. Rev. Lett., vol. 99, p.154301, Oct 2007.

DOI: 10.1103/physrevlett.99.169902

[9] T. Zhou, J. Ren, M. Medo and Y. Zhang, Bipartite network projection and personal recommendation, Phys. Rev. E, vol. 76, Oct 2007.

DOI: 10.1103/physreve.76.046115

[10] Y. Zhang, M. Medo, J. Ren, T. Zhou, T. Li and F. Yang, Recommendation model based on opinion diffusion, EPL, vol. 80, no. 6, p.68003, 2007.

DOI: 10.1209/0295-5075/80/68003

[11] J. Liu, M. Shang and D. Chen, Personal recommendation based on weighted bipartite networks, in Proceedings of the 6th international conference on fuzzy systems and knowledge discovery - Volume 5, ser. FSKD'09, 2009, p.134–137.

DOI: 10.1109/fskd.2009.469