Abstract
Social recommendation algorithm that integrates social networks is widely used in big data information recommendation. However, there are many relationships among users of social networks, and the influence of each relationship on recommendation is different. Simply introducing a certain social relationship will inevitably affect the accuracy of recommendation algorithm. Based on multi-subnet composited complex network model, the multi-relationship composite network is constructed by loading multi-relationship social network on the user-commodity bipartite graph, and a mass diffusion recommendation algorithm based on multi-relationship composite network is proposed. The experimental results on real datasets Epioions and FilmTrust show that the proposed recommendation algorithm with two kinds of social relations has a significant improvement in recommendation accuracy and diversity compared with the recommendation algorithm with one kind of social relations and the traditional mass diffusion algorithm.
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Shuang, Z., Sheng, B., Gengxin, S. (2019). Mass Diffusion Recommendation Algorithm Based on Multi-subnet Composited Complex Network Model. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_23
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DOI: https://doi.org/10.1007/978-3-030-24265-7_23
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