A Collaborative Filtering Recommendation Algorithm Based on Bipartite Graph

Abstract:

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With the rapid growth and wide application of Internet, everyday there are many of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. The recommendation algorithm is the process to alleviative the problem. Collaborative filtering algorithm is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering algorithm has the problem of sparsity, which will influence the efficiency of prediction. In this paper, a collaborative filtering recommendation algorithm based on bipartite graph is proposed. The algorithm takes users, items and tags into account, and also studies the degree of tags which may affect the similarity of users. The collaborative filtering recommendation algorithm based on bipartite graph can alleviate the sparsity problem in the electronic commerce recommender systems.

Info:

Periodical:

Advanced Materials Research (Volumes 756-759)

Edited by:

S.Z. Cai and Q.F. Zhang

Pages:

3865-3868

Citation:

D. E. Chen and Y. L. Ying, "A Collaborative Filtering Recommendation Algorithm Based on Bipartite Graph", Advanced Materials Research, Vols. 756-759, pp. 3865-3868, 2013

Online since:

September 2013

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$41.00

[1] Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on Trust Network and Trust Factor, Journal of Convergence Information Technology, Vol. 8, No. 5, p.1111 ~ 1118, (2013).

DOI: https://doi.org/10.4156/jcit.vol8.issue5.129

[2] Songjie Gong, Learning User Interest Model for Content-based Filtering in Personalized Recommendation System, International Journal of Digital Content Technology and its Applications, Vol. 6, No. 11, p.155 ~ 162, (2012).

DOI: https://doi.org/10.4156/jdcta.vol6.issue11.20

[3] Songjie Gong, Privacy-preserving Collaborative Filtering based on Randomized Perturbation Techniques and Secure Multiparty Computation, International Journal of Advancements in Computing Technology, Vol. 3, No. 4, p.89 ~ 99, (2011).

DOI: https://doi.org/10.4156/ijact.vol3.issue4.10

[4] Zhang Z K, Zhou T, Zhang Y C. Personalized recommendation via integrated diffusion on user- item- tag tripartite graphs[ J] . Physica A, 2010, 389: 179- 186.

DOI: https://doi.org/10.1016/j.physa.2009.08.036

[5] Fouss F, Pirotte A, Sarens M. Random-walk computation of simi-larities between nodes of a graph, with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering . (2007).

DOI: https://doi.org/10.1109/tkde.2007.46

[6] Fouss F, Pirotte A, Renders J M, etal. A novel way of computing dissimilarities between nodes of a graph, with application to collab-orative filtering. Proceedings of IEEE/WIC/ACM Interna-tional Joint Conference on Web Intelligence . (2005).

DOI: https://doi.org/10.1109/wi.2005.9

[7] Songjie Gong, A Personalized Recommendation Algorithm on Integration of Item Semantic Similarity and Item Rating Similarity, Journal of Computers, Volume 6, Number 5, May 2011, pp: 1047-1054.

DOI: https://doi.org/10.4304/jcp.6.5.1047-1054

[8] Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, Journal of Software, Volume 5, Number 7, July 2010, pp: 745-752.

DOI: https://doi.org/10.4304/jsw.5.7.745-752

[9] Sarkar P, Moore A. A tractable approach to finding closest trun-cated-commute-time neighbors in large graphs. Proceedings of UAI . (2007).