GE has already significantly outperformed a number of baselines including JIM  [@Yin_cikm15joint], PRME  [@Feng_ijcai15], and Geo-SAGE  [@Wang_kdd15Geo]. We thus only show our improvements over GE.

Also note that although we choose the TransR technique in knowledge graph embedding to materialize our STA model, the essential of our proposed framework is the translation of $<$time, location$>$ pairs in the embedding space. This indicates that we do not rely on a specific translation model. Hence we can use TransE  [@Bordes_ml14] and TransH  [@Wang_aaai14] to realize STA. We denote the resulting methods as STA-E and STA-H baselines, respectively.

**Settings** We first organize the quadruples (u, v, t, l) in each dataset by users to get each user’s profile $D_u$. We then rank the records in $D_u$ according to the check-in timestamps, and finally divide these ordered records into two parts: the first 80% as the training data, and the rest 20% data as the test data. Moreover, the last 10% check-in records in the training data are used as a validation set for tuning the hyper-parameters. We use the accuracy@*k* (*k* = {1, 5, 10, 15, 20}) as our evaluation metric. All these settings, as well as the computation approach to accuracy@*k*, are 