Abstract
Bipartite network projection method has been recently employed for personal recommendation. It constructs a bipartite network between users and items. Treating as resource in the network user taste for items, it allocates the resource via links between user nodes and item nodes. However, the taste model employed by existing algorithms cannot differentiate “dislike” and “unrated” cases implied by user ratings. Moreover, the distribution of resource is solely based on node degrees, ignoring the different transfer rates of the links. To enhance the performance, this paper devises a negative-aware and rating-integrated algorithm on top of the baseline algorithm. It enriches the current user taste model to encompass “like”, “dislike” and “unrated” information from users. Furthermore, in the resource distribution stage, we propose to initialize the resource allocation according to user ratings, which also determines the resource transfer rates on links afterward. Extensive experiments conducted on real data validate the effectiveness of the proposed algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Marko, B., Yoav, S.: Fab: Content-based Collaborative Recommendation. Comm. of the ACM 40(3), 66–72 (1997)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R.: GroupLens: Applying Collaborative Filtering to Usenet News. Comm. of the ACM 40(3), 77–87 (1997)
Khoshneshin, M., Street, N.W.: Collaborative Filtering via Euclidean Embedding. In: ACM RecSys 2010, Barcelona, Spain, September 26-30 (2010)
Koren, Y.: Collaborative Filtering with Temporal Dynamics. In: ACM KDD 2009, Paris, France, June 28-July 1 (2009)
Koren, Y., Bell, R., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. IEEE Computer 42(8), 30–37 (2009)
Shi, Y., Larson, M., Hanjalic, A.: Mining Contextual Movie Similarity with Matrix Factorization for Context-Aware Recommendation. ACM TIST 04(01), 1601–1619 (2013)
Liu, H., Maes, P.: InterestMap: Harvesting Social Network Profiles for Recommendations. In: IUI 2005, San Diego, California, USA (January 9, 2005)
Zhou, T., Ren, J., Medo, M., Zhang, Y.: Bipartite Network Projection and Personal Recommendation. Phys. Rev. E 76(4), 46115 (2007)
Zhang, Y., Blattner, M., Yu, Y.: Heat Conduction Process on Community Networks as a Recommendation Model. Phys. Rev. Lett. 99, 154301 (2007)
Zhang, Y., Medo, M., Ren, J., Zhou, T., Li, T., Yang, F.: Recommendation Model Based on Opinion Diffusion, Europhys. Europhys. Lett. 80(2008), 68003 (2008)
Liu, J., Zhou, T., Wang, B., Zhang, Y.: Effects of User’s Tastes on Personalized Recommendation. Int. J. Mod. Phys. C 20, 1925–1932 (2009)
Zhou, T., Jiang, L., Su, R., Zhang, Y.: Effect of Initial Configuration on Network-Based Recommendation. Europhys. Lett. 81(2008), 58004 (2008)
Xia, J., Wu, F., Xie, C., Tu, J.: INBI: An Improved Network-Based Inference Recommendation Algorithm, In: IEEE NAS 2012, June 28-30 (2012)
Fernandez, Y.B., Arias, J.P., Solla, A.G., Cabrer, M.R., Nores, M.L.: Providing Entertainment by Content-based Filtering and Semantic Reasoning in Intelligent Recommender Systems. IEEE Tconsum. Electr. 54(2), 727–735 (2008)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems. In: Proceedings of ICCIT, April 2-4 (2002)
Liu, F., Lee, H.J.: Use of Social Network Information to Enhance Collaborative Filtering Performance. Expert Syst. Appl. 37(7), 4772–4778 (2010)
Liu, J., Zhou, T., Che, H., Wang, B., Zhang, Y.: Effects of High-order Correlations on Personalized Recommendations for Bipartite Networks. Physica A 389(2010), 881–886 (2010)
Liu, J., Zhou, T., Guo, Q.: Information Filtering via Biased Heat Conduction. Phys. Rev. E 84, 37101 (2011)
Quan, J., Fu, Y.: A Novel Collaborative Filtering Algorithm Based on Bipartite Network Projection. JDCTA 6(1), 391–397 (2012)
Sawant, S.: Collaborative Filtering using Weighted Bipartite Graph Projection: A Recommendation System for Yelp. In: CS224W: Social and Information Network Analysis (December 10, 2013)
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
Yin, F., Zhao, X., Zhou, G., Zhang, X., Hu, S. (2014). A Negative-Aware and Rating-Integrated Recommendation Algorithm Based on Bipartite Network Projection. In: Wang, H., Sharaf, M.A. (eds) Databases Theory and Applications. ADC 2014. Lecture Notes in Computer Science, vol 8506. Springer, Cham. https://doi.org/10.1007/978-3-319-08608-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-08608-8_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08607-1
Online ISBN: 978-3-319-08608-8
eBook Packages: Computer ScienceComputer Science (R0)