7, and 2 time slots, corresponding to the daily, weekly, and weekday/weekend patterns, respectively. Figure \[fig:time\] shows the effects of various time intervals.

![image](time.eps){width="82.00000%" height="5.5cm"}

We observe that the impact of the daily patterns is the most significant on both datasets. In addition, the results for different patterns vary widely, suggesting a good strategy for dividing the time slot is important.

Sensitivity to Data Sparsity
----------------------------

To investigate the sensitivity to data sparsity of STA and GE, we conduct extensive experiments to evaluate the performance on two datasets by reducing training data. More precisely, we keep the testing dataset unchanged and reduce the training data randomly by a ratio of 5% to 20% stepped by 5. Due to the space limitation, we only present the results by reducing 20% training data Table \[tab:20less\]. The trends with other ratios are all alike.

We have the following important notes for Table \[tab:20less\].

-   With the reduction of training data, the accuracy values for STA and GE both decrease. However, STA always achieves the best results at different *k* values on two datasets.

-   The reduction of accuracy of our STA model is much smaller than that of GE. For instance, the accuracy@1 