Learning Graph-based POI Embedding for Location-based Recommendation

By M. Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, Sen Wang. International Conference on Information and Knowledge Management, 2016

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How researchers see this paper

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Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (2019, background): “semantic annotation of POIs [34], POI recommendation [10, 35], next visitor prediction [11].”
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (2019, methodology): “These two metrics are popularly used for evaluating recommendation results, such as (Liu et al. 2016a; He et al. 2016; Xie et al. 2016).”
Network Representation Learning: A Survey (2017, background): “Using vector representation to represent complex networks has now been gradually advanced to many other domains, such as point-of-interest recommendation in urban computing [15], and knowledge graph search [18] in knowledge engineering and database systems.”