## 4 Experiments
### Experimental Setup  
**Datasets:** The experiments are conducted on two real-world datasets Foursquare and Gowalla ([[https://www.ntu.edu.sg/home/gaocong/datacode.htm](https://www.ntu.edu.sg/home/gaocong/datacode.htm)]([https://www.ntu.edu.sg/home/gaocong/datacode.htm](https://www.ntu.edu.sg/home/gaocong/datacode.htm))). Foursquare is the most popular POI recommender system in the world where users can query interesting POIs (i.e., hotels and restaurants). In addition, different from traditional search engines, Foursquare provides APIs for users to share their check-in records and reviews with others as a social network. Therefore, there are numerous check-in records generated each day. The dataset of Foursquare used in this paper is collected from August 2010 to July 2011 in Singapore, including 342,850 check-ins. Gowalla is also a famous POI recommender system where the dataset used in this paper is collected from February 2009 to October 2010 in Virginia and Nevada USA, including 736,148 check-in records. Table 1 shows the fields of each check-in record and a check-in sample. To relieve the influence of long-tail data to the robustness of the proposed model and baselines, we eliminated the users who have fewer than 10 check-ins, and the POIs which have been visited by fewer than 10 users (POI2 Vec removed the users who have fewer than five check-ins, and the POIs which have been visited by fewer than five users). Table 2 shows the statistics of used Foursquare and Gowalla datasets in this paper. \begin{table}
\begin{tabular}{l l} \hline \hline
**Sample** & **Field (Description)** \\ \hline USER\_42 & User\_ID \\ \hline LOC\_11903 & POI\_ID \\ \hline (37.4191867667, 122.2122015167) & coordinate (the lat. and long. of POI) \\ \hline
04:12 & checkin\_time (hour:min) \\ \hline
0 & date\_id (the representation of date) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The fields of each check-in record. **Baselines:** To evaluate the effectiveness of MTPR, the proposed approach is compared with several state-of-art methods considering the geographical influence including PRME [8], POI2 Vec [6], and GeoIE [9]. In addition, to validate the effectiveness of adversarial learning and geographical influence, we also compare MTPR to its variants which eliminate the adversarial loss or the geographical LSTM. These baselines are briefly introduced as below:  
* **PRME [8]:** transforms each POI into a point in a K-dimensional Euler space using the metric embedding and the distance between the transformed points is considered as the similarity between the corresponding POIs. * **POI2 Vec [6]:** is an approach to learning the latent representation (i.e., embedding) of users and POIs based on the individual sequential check-in records simultaneously considering the geographical influence and the user preference. The geographical location is split into blocks and the geographical relationship between POIs is measured using the Huffman tree. * **GeoIE [9]:** is a method to providing POI recommendations considering the POI-specific geographical influence including the geographical influence of POI, the geographical susceptibility of POI, and their physical distance. * **Geo-ALM [10]:** is a geographical information based adversarial learning model which utilized a game setting to generate a few but critical negative samples (i.e., POIs that users have never been to) for improving the effectiveness of POI recommendation. * **MTPR-NoGeo:** is a variant of MTPR which eliminates the geographical location loss \(loss_{loc}\) and the corresponding geo-LSTM module. * **MTPR-NoGen:** is a variant of MTPR which eliminates the generation loss \(loss_{gen}\) and trains the generator only based on the prediction loss \(loss_{pred}\) and geographical location loss \(loss_{loc}\).