Abstract
POI (point-of-interest) recommendation as an important location-based service has been widely utilized in helping people discover attractive locations. A variety of available check-in data provide a good opportunity for developing personalized POI recommender systems. However, the extreme sparsity of check-in data and inefficiency of exploiting unobserved feedback pose severe challenges for POI recommendation. To cope with these challenges, we develop a heterogeneous graph embedding-based personalized POI recommendation framework called HRec. It consists of two modules: the learning module and the ranking module. Specifically, we first propose the learning module to produce a series of intermediate feedback from unobserved feedback by learning the embeddings of users and POIs in the heterogeneous graph. Then we devise the ranking module to recommend each user the ultimate ranked list of relevant POIs by utilizing two pairwise feedback comparisons. Experimental results on two real-world datasets demonstrate the effectiveness and superiority of the proposed method.
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Acknowledgments
This work is supported by the National Key Research and Development Program of China, and National Natural Science Foundation of China (No. U163620068).
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Su, Y. et al. (2019). HRec: Heterogeneous Graph Embedding-Based Personalized Point-of-Interest Recommendation. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11955. Springer, Cham. https://doi.org/10.1007/978-3-030-36718-3_4
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DOI: https://doi.org/10.1007/978-3-030-36718-3_4
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