Abstract
Recommender systems have been widely adopted in various large-scale Web applications. Among these applications, online dating application has attracted more and more research efforts. Essentially, online dating data is a bipartite graph with sparse reciprocal links. Reciprocal recommendations consider bi-directional interests of service and recommended users, not merely the service user’s interest. This paper proposes a motif-based graph neural network (MotifGNN) for online dating recommendation task. We first define seven kinds of motifs and then design a motif based random walk algorithm to sample neighbor users to learn feature embeddings of each service user. At last, these learned feature embeddings are used to predict whether a reciprocal link exists or not. Experiments are evaluated on two real-world online dating datasets. The promising results demonstrate the superiority of the proposed approach against a number of state-of-the-art approaches.
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Acknowledgments
This work was supported in part by the National Key R&D Program of China under Grant no. 2018YFB1003800, 2018YFB1003804, the National Natural Science Foundation of China under Grant No. 61872108, and the Shenzhen Science and Technology Program under Grant No. JCYJ20170811153507788.
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Luo, L., Liu, K., Peng, D., Ying, Y., Zhang, X. (2020). A Motif-Based Graph Neural Network to Reciprocal Recommendation for Online Dating. 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_9
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