same as those in  [@Xie_cikm16].

We use the default settings in the original TransR  [@Lin_aaai15] as the parameter settings for our STA model. Specifically, we set the learning rate $\lambda = 0.0001$, the margin $\gamma = 2$, the mini-batch size $B=4800$, and the embedding dimensions $m = d = 100$, and we traverse over all the training data for 1000 rounds.

Comparison with baselines
-------------------------

For a fair comparison, we implement GE using the same LINE software provided by the authors of  [@Tang_www15] on our data divisions. All the parameters for GE are same as those in  [@Xie_cikm16]. We find a slightly difference (less than 1% in accuracy) between the original results and those by our implemented GE. This is understandable and acceptable considering the randomness when sampling negative edges in LINE and initiating the centers of clusters of regions. All parameters for STA-E and STA-H use the default settings in  [@Bordes_ml14] and  [@Wang_aaai14]. We present the comparison results on Foursquare and Gowalla in Fig. \[fig:comp\] (a) and (b), respectively.

![image](comp.eps){width="82.00000%" height="5.2cm"}

From Fig. \[fig:comp\] (a), it is clear that all our proposed STA-style models significantly outperform GE. For instance, the accuracy@1 for STA, STA-H, and STA-E is 0.307, 0.280, 