Abstract
By analyzing the searching and recommendation system in the electronic commerce field, this paper provides a retrieval sorting and recommendation algorithm for e-commerce service. An information transmission matrix is constructed based on the ‘customer-product’ bipartite networks, and the rankings of customers and products can be obtained by analyzing the network structures. Then we propose a community detection algorithm for bipartite networks, by employing the information transmission matrix. Finally, the recommendation scheme based on both customers and products is obtained. It makes recommendation results more comprehensive and reasonable, which satisfies the various requirements of the customers.
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References
Ye, Q.: URL Rule Based Focused Crawl and its Application. Zhengjiang University (2007)
Page, L., Brin, S., et al.: The PageRank citation ranking: bringing order to the web. In: Stanford InfoLab, pp. 1–14 (1998)
Huang, D., Qi, H.: PageRank algorithm research. Comput. Eng. 32(4), 145–146 (2006)
Ching, W.-K., Huang, X., Ng, M.K., Siu, T.-K.: Markov Chains Models, Algorithms and Applications. Tsinghua University Press, Beijing (2015)
Zhou, T., Ren, J., Medo, M., et al.: Bipartite network projection and personal recommendation. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 76(4 Pt 2), 046115 (2007)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(6 Pt 2), 066133 (2003)
Wu, X., Yan, D.: Analysis and research on method of data dimensionality reduction. Appl. Res. Comput. 26(8), 2832–2835 (2009)
Yao, F., Chen, L.: Similarity propagation based link prediction in bipartite networks. Comput. Sci. 43(4), 86–91 (2016)
Chen, B.L., Chen, L., Zou, S.R., et al.: Detecting community structure in bipartite networks based on matrix factorisation. Comput. Sci. 6(6), 599–607 (2013)
Chairman-Karloff, H.: Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics (1998)
Yang, B., Cheung, W., Liu, J.: Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19(10), 1333–1348 (2007)
Wu, H., Wang, Y.-J., Wang, Z., Wang, X., Du, S.-Z.: Two-phase collaborative filtering algorithm based on co-clustering. J. Softw. 21(5), 1042–1054 (2010)
Acknowledgements
This work is partially supported by Liaoning Natural Science Foundation under Grant No. 20170540320, the Doctoral Scientific Research Foundation of Liaoning Province under Grant No. 20170520358, the Fundamental Research Funds for the Central Universities under Grant No. N161702001, No. N172410005-2.
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Chen, D., Liu, C., Huang, X., Wang, D., Yan, J. (2020). A Probability Transition Matrix-Based Recommendation Algorithm for Bipartite Networks. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_99
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DOI: https://doi.org/10.1007/978-3-030-32456-8_99
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