proposed STA-C model consistently beats GE when recommending cold start POIs. The superior performance of STA-C model is due to the translation of content and geography information *wl* from an ordinary POI *v* to a cold start POI $v_c$. As long as there is an existing *v* sharing one $<$word, location$>$ pair with $v_c$, our STA-C model can get a translation for $v_c$. In contrast, GE utilizes the bipartite graphs of POI-Word and POI-Location. The weight of an edge in the graph is calculated by a TF-IDF value of the word or the frequency of a location. The edge weight is proportional to the probability of edge sampling. Since there are few check-in records for cold start POIs, a $v_c$-word and $v_c$-location edge has an extremely rare chance to be selected and updated. Consequently, the learnt embedding for $v_c$ will be poor and further deteriorates the recommendation accuracy.

Conclusion
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We present a novel spatiotemporal aware model STA for learning representations of users, spatiotemporal patterns, and POIs. The basic idea is to capture the geographic and temporal effects using a $<$time, location$>$ pair, and then model it as a translation connecting users and POIs. We realize STA using the knowledge 