Abstract
Online social platforms have provided a large amount of available information to recommendation systems. With this intuition, social recommendation systems emerged and have attracted increasing attention over the past years. Most existing social recommendation methods only use explicit social relationships among users. However, implicit social relationships can effectively improve the quality of recommendation when users only have few social relationships. To this end, the discovery of implicit relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to fuse direct and indirect friends toward discovering more accurate social recommendation method. We learn users’ preferences by carefully integrating users’ direct and indirect friends. In particular, we construct item rankings based on the feedback from users’ direct and indirect friends on the item. Furthermore, to distinguish the impact of users’ direct friends and indirect friends, we also extend the ranking assumption in item domain to user domain, so that information from user rankings can be leveraged to further improve the recommendation performance. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed method.
Supported by the National Natural Science Foundation of China (61762078, 61363058, 61966004), Research Fund of Guangxi Key Lab of Multisource Information Mining and Security (MIMS1808), 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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Wei, Y., Ma, H., Zhang, R., Li, Z., Chang, L. (2021). Exploring Implicit Relationships in Social Network for Recommendation Systems. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_31
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DOI: https://doi.org/10.1007/978-3-030-75765-6_31
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