Abstract
Recent recommendation strategies attempt to explore relations among both users and items, applying techniques of graph learning and reasoning for solving the so-called information isolated island limitations. However, the graph-based ranking algorithms model the interactions between the user and item either as a user-user (item-item) graph or a bipartite graph that capture pairwise relations. Such modeling cannot capture the complex relationship shared among multiple interactions that can be useful for item ranking.
In this paper, we propose to leverage hypergraph random walk into the ranking process. We develop a new recommendation framework Hypergraph Rank (HGRank), which exploits the weighting methods for hypergraph on both hyperedges and vertices. This leads to the expressive modeling of high-order interactions instead of pairwise relations. Specifically, we take social trust and reliability into the hypergraph weighting process to improve the accuracy of the algorithm. Extensive experimental results demonstrate the effectiveness of our proposed approach.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61762078, 61363058, 61966004), Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (MIMS18-08), Northwest Normal University young teachers research capacity promotion plan (NWNU-LKQN2019-2) and Research Fund of Guangxi Key Laboratory of Trusted Software (kx202003).
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Jiang, Y., Ma, H., Liu, Y., Li, Z. (2020). Exploring User Trust and Reliability for Recommendation: A Hypergraph Ranking Approach. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_28
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DOI: https://doi.org/10.1007/978-3-030-63833-7_28
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