Abstract
Event-based social networks (EBSNs), are platforms that provide users with event scheduling and publishing. In recent years, the number of users and events on such platforms has increased dramatically, and interactions have become more complicated, which has made modeling heterogeneous networks more difficult. Moreover, the requirement of real-time matching between users and events becomes urgent because of the significant dynamics brought by the widespread use of mobile devices on such platforms. Therefore, we proposed a graph embedding based real-time social event matching model called GERM. We first model heterogeneous EBSNs into heterogeneous graphs, and use graph embedding technology to represent the nodes and their relationships in the graph which can more effectively reflect the hidden features of nodes and mine user preferences. Then a real-time social event matching algorithm is proposed, which matches users and events on the premise of fully considering user preferences and spatio-temporal characteristics, and recommends suitable events to users in real-time efficiently. We conducted experiments on the Meetup dataset to verify the effectiveness of our method by comparison with the mainstream algorithms. The results show that the proposed algorithm has a good improvement on the matching success rate, user satisfaction, and user waiting time.
Supported by the National Key R&D Program of China (Grant No. 2019YFB1405302), the NSFC (Grant No. 61872072 and No. 61672144), and the State Key Laboratory of Computer Software New Technology Open Project Fund (Grant No. KFKT2018B05).
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Wu, G. et al. (2020). A Graph Embedding Based Real-Time Social Event Matching Model for EBSNs Recommendation. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_4
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DOI: https://doi.org/10.1007/978-3-030-62005-9_4
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