0.255, respectively, much better than 0.225 for GE. Similar results can be observed in Fig.  \[fig:comp\] (b) on Gowalla dataset. This clearly demonstrates the effectiveness of our translation based framework.

While STA shows drastic improvement over GE for all *k*s on Foursquare, the trend is not that obvious on Gowalla when *k* = 15, 20. This is because there is a much smaller number of relations in Gowalla than that in Foursquare. As shown in Table  \[tbl:dataset\], Gowalla only has 3,636 relation patterns ($<$t, l$>$ pairs) while Foursquare has 28,868 pairs. Hence the learnt embeddings for entities and relations are worse than those on Foursquare, and incur the less accurate results when *k* is large.

Besides the improvement over GE, STA outperforms STA-H and STA-E as well. The reason is that TransR can differentiate the entities in the transformed relation space. Nevertheless, we see a less significant enhancement of STA over STA-H on Gowalla. This also conforms to the characteristics of the data: the graph of Gowalla is much larger but has less *tl* relation edges than that of Foursquare, and the advantage of TransR over TransE is not obvious on such a dataset.

Effects of Model Parameters
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The 