graph embedding technique. Our method has two distinguished advantages. 1) We learn a joint representation for spatiotemporal patterns whose components contribute together to a user’s choice in POIs. 2) The translation mechanism enables the learnt POI embeddings to be in the same semantic space with that of the query POI.

We conduct extensive experiments on two real-life datasets. Our results show that STA achieves the state-of-the-art performance in recommendation accuracy. It also significantly outperforms the baselines in terms of the effectiveness in addressing both the data sparsity and cold start problems.

Acknowledge {#acknowledge .unnumbered}
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The work described in this paper has been supported in part by the NSFC project (61572376).

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