Abstract
The rapid growth of Location-Based Social Networks (LBSNs) has led to the generation of large amounts of users’ check-in data, which has driven the development of many location-based recommendation services. Point-of-Interest (POI) recommendation is one such service that helps users find places they are interested in based on the current time and location. Unlike traditional recommendation tasks, users’ check-in data contains rich heterogeneous data such as time, geographical information and social relationship information; thus it is challenging to capture the complex contextual relationships between these heterogeneous information for POI recommendation. To solve this problem, we propose a Metapath Aggregated Heterogeneous Graph Embeddings method(MAHGE). Specially, it firstly proposes a novel method to construct the heterogeneous LBSN graph which innovatively models time as the relationship on the edges of the graph in order to capture the complex dependency between user and time. Then, it proposes to profile the target node based on meta-paths because meta-path reflects the characteristics of target node from a multi-dimensional perspective. Moreover, it introduces a graph embedding method based on meta-path aggregation to learn the vector representation of the target node with attention mechanism. Finally, extensive experiments on two real-word datasets are conducted, and the results show the effectiveness of this method.
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Acknowledgments
This work is supported by the National Key R & D Program of China (No.2022YFF0503900), the Key R & D Program of Shandong Province (No.2021CXGC010104).
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Tian, J., Chang, M., Ding, Z., Han, X., Chen, Y. (2022). MAHGE: Point-of-Interest Recommendation Using Meta-path Aggregated Heterogeneous Graph Embeddings. In: Wu, H., et al. Spatial Data and Intelligence. SpatialDI 2022. Lecture Notes in Computer Science, vol 13614. Springer, Cham. https://doi.org/10.1007/978-3-031-24521-3_18
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DOI: https://doi.org/10.1007/978-3-031-24521-3_18
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