that uses the same information as GE does. Our standard STA model, on the other hand, uses less information than GE because it does not include the contents of POIs.

Experimental Evaluation
=======================

In this section, we first introduce the experimental setup and then compare our experimental results with those of baselines. Finally we show the performance of our method for addressing the data sparsity and cold start problem.

Experimental Setup
------------------

**Datasets** We evaluate our methods on two real-life LBSN datasets: Foursquare and Gowalla. A number of researchers have conducted experiments on data collected from these two social networks  [@Yuan_sigir13; @Chen_aaai15; @Gao_aaai15; @Xie_cikm16; @Yin_tkde16]. However, many of them are collected from various regions or in different time spans. For a fair comparison with GE, we use the publicly available version [^1] provided by the authors of  [@Xie_cikm16].

The two datasets have different scales such as geographic ranges, the number of users, POIs, and check-ins. Hence they are good for examining the performance of algorithms on various data types. Their statistics are listed in Table \[tbl:dataset\].

[ccc]{} & **Foursquare** & **Gowalla**\
\# of users & 114,508 & 107,092\
\# of POIs & 62,462 & 1,280,969\
\# of Check-ins & 1,434,668 