of GE decreases from 0.225 to 0.154, showing a 31.69% drop. In contrast, our STA model only has a 20.00% change. This strongly suggests that our model is more robust to the data sparsity.

-   The declination of accuracy on Foursquare is more obvious than on Gowalla. The reason may be that Foursquare is much sparser in users’ check-ins than Gowalla, hence reducing the training data has a greater impact on Foursquare.

Test for Cold Start Problem
---------------------------

In this experiment, we further compare the effectiveness of our extended STA-C model with GE when addressing the cold-start problem. The cold start POIs are defined as those visited by less than 5 users  [@Yin_tkde16]. To test the performance of cold start POI recommendations, we select users who have at least one cold-start check-in as test users. For each test user, we choose his/her check-in records associated with cold-start POIs as test data and the remains as training data. Since there is no content information for POIs in Gowalla, we conduct experiments, just as GE did, only on Foursquare. The results are shown in Fig. \[fig:cold\].

![Test for Cold Start Problem on Foursquare[]{data-label="fig:cold"}](cold1.eps){width="56.00000%" height="3.6cm"}

From Fig. \[fig:cold\], it is clear that our 