Abstract
With the thriving of online social networks, there emerges a new recommendation scenario in many social apps, called Friend-Enhanced Recommendation (FER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals are explicitly shown to users. Different from conventional social recommendation, the unique friend referral circle in FER may significantly change the recommendation paradigm, making users to pay more attention to enhanced social factors. In this paper, we first formulate the FER problem, and propose a novel Social Influence Attentive Neural network (SIAN) solution. In order to fuse rich heterogeneous information, the attentive feature aggregator in SIAN is designed to learn user and item representations at both node- and type-levels. More importantly, a social influence coupler is put forward to capture the influence of the friend referral circle in an attentive manner. Experimental results demonstrate that SIAN outperforms several state-of-the-art baselines on three real-world datasets. (Code and dataset are available at https://github.com/rootlu/SIAN).
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China (No. 61772082, 61806020, 61702296), the National Key Research and Development Program of China (2018YFB1402600), and the Tencent WeChat Rhino-Bird Focused Research Program. This work is also supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2018-001). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. Yuanfu Lu is also supported by 2019 Tencent Rhino-Bird Elite Training Program.
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Lu, Y. et al. (2021). Social Influence Attentive Neural Network for Friend-Enhanced Recommendation. In: Dong, Y., Mladenić, D., Saunders, C. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12460. Springer, Cham. https://doi.org/10.1007/978-3-030-67667-4_1
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