## 4 Experimental Setup
### Datasets  
We experimented with three different datasets and two sub-datasets: Reddit (Subreddit dataset: [[https://www.kaggle.com/colemaclean/subreddit-interactions](https://www.kaggle.com/colemaclean/subreddit-interactions)]([https://www.kaggle.com/colemaclean/subreddit-interactions](https://www.kaggle.com/colemaclean/subreddit-interactions))), Last.fm (Last.fm dataset: [[https://blog.csdn.net/hopygreat/article/details/96444827](https://blog.csdn.net/hopygreat/article/details/96444827)]([https://blog.csdn.net/hopygreat/article/details/96444827](https://blog.csdn.net/hopygreat/article/details/96444827))), Tmall (Tmall dataset: [[https://tianchi.aliyun.com/datalab/dataSet.html?spm=5176.100073.0.0.40236](https://tianchi.aliyun.com/datalab/dataSet.html?spm=5176.100073.0.0.40236) fc1 kIb4f8&dataId=42]([https://tianchi.aliyun.com/datalab/dataSet.html?spm=5176.100073.0.0.40236](https://tianchi.aliyun.com/datalab/dataSet.html?spm=5176.100073.0.0.40236) fc1 kIb4f8&dataId=42)), 1/10 Reddit and 1/80 Last.fm. The descriptive statistics for the datasets are shown in Table 1. The distribution histograms of the number of sessions by the frequency of users for Reddit, Last.fm and Tmall datasets are illustrated in Figures 4 and 5. Moreover, another set of distribution histograms, namely the one of the number of events by the frequency of sessions for Reddit, Last.fm and Tmall datasets, is given in Figures 6 and 7. Tables 2 and 3 show the standard deviation and the skewness (We used the skew function provided by SciPy to calculate the skewness. See also: [[https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html)]([https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html))) of them. To conserve space, only the standard deviation and the skewness but not the histograms of the two distributions for 1/10 Reddit and 1/80 Last.fm datasets are given in Tables 2 and 3. Figure 5: Distribution histogram of the number of sessions by the frequency of users for Tmall dataset. Figure 6: Distribution histograms of the number of events by the frequency of sessions for Reddit and Last.fm datasets. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Reddit** & **Last.fm** & **Tmall** & **1/10 Reddit** & **1/80 Last.fm** \\ \hline Standard & 2.08 & 6.14 & 1.45 & 0.70 & 0.42 \\ deviation & 4.08 & 0.83 & 3.05 & 6.61 & 13.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Standard deviation and skewness of the number of events by the frequency of sessions for the datasets. Figure 7: Distribution histogram of the number of events by the frequency of sessions for Tmall dataset. Reddit datasetReddit contains a log of user interactions on different subreddits (sub-forums) at different timestamps. Since the dataset does not split the interactions into sessions, we did this by setting a time limit for inactivity and integrate the actions that occurred within one hour into a session as in [7]. Also, as in [7], we replicated their model against ours on a training set of 80% and holdout set of 20% using the same Recall and MRR (Mean Reciprocal Rank) metrics as they did [7]. We discuss the two evaluation metrics in Section 4.3. Our results replicate theirs (not shown here to minimize space). Additionally, for a deeper assessment as advocated in [55], we split the sessions into a training set (60%), a validation set (20%) and a test set (20%) to establish the performance of the alternative models on an identical basis. Last.fm datasetFor Last.fm dataset, we split logs of every user into sessions the same way as that for Reddit. Instead, we chose a 30-minute limit as in [7]. The same procedure for assessment was used on Last.fm dataset as stated above. Afterwards, we split the sessions similar to that for Reddit dataset. 1/10 Reddit datasetFor the 1/10 Reddit dataset, we select 10% random samples from the original Reddit dataset.