## 2 Changes in the Statements Corresponding to the Tables  
The statements of Section 5.1 corresponding to these tables would be changed to:  
(The statements of Tables 1-3 remain the same as those in the original paper.) For all datasets, ASLM-LHS outperforms all baselines with moderate margins. Table 4 shows the validation set performances of three different methods under the metric of Recall@K and MRR@K in Reddit and Last.fm datasets. Please note that, for the Reddit dataset, the validation set performance of ASLM-LHS is better than that of ASLM-AP, and, for Reddit and Last.fm datasets, the validation set performances of ASLM-AP and ASLM-LHS are all better than the test set performances with moderate margins, respectively. Table 5 shows the validation set performances of three different methods in 1/10 Reddit and 1/80 Last.fm datasets. The validation set performances of the two ASLM models in the 1/10 Reddit dataset are all better than the test set performances, while, in the 1/80 Last.fm dataset, the majority of the validation set performances of the two ASLM models are better than the test set performances. The statements of Section 6.1 corresponding to these tables would be changed to:  
For Group A, in Table 1, we observe that ASLM-LHS consistently outperforms all the baselines under all measurements for testing cases in both Reddit and Last.fm datasets with moderate margins; In most cases, ASLM-AP outperforms all the baselines in both datasets, except in Recall@10 and Recall@20 scores in the Reddit test set. Specifically, ASLM-LHS improves 9.12% and 22.10% in Recall@5 and MRR@5 scores compared with the II-RNN-LHS method for test cases in the Reddit dataset, respectively. ASLM-AP improves 4.36% and 7.33% in Recall@5 and MRR@5 compared with the II-RNN-AP method for test cases in the Last.fm dataset, respectively. The reason ASLM-AP's Recall@10 and Recall@20 scores are lower than those of II-RNN-LHS in the Reddit dataset is that the average pooling method only utilizes the average of the embedding of each event \(e_{t,i}^{u}\in S_{t}^{u}\) as the representation of the current session \(S_{t}^{u}\), and the important context information, e.g., the sequential patterns and the user's intent captured by the attention mechanism, will be lost when this representation is stored in the user's long-term session representations. It results in the fact that the abundant context information cannot be fully utilized by ASLM-AP, and thus its performance declined. However, the performance of ASLM-AP is still better than II-RNN-AP, which also utilizes the average pooling method. The good performance of ASLM-LHS is attributed to the attention mechanism and the bidirectional-LSTM we employed in the attention-based layer and the last hidden state method. One of the main characteristics of the attention mechanism is that it calculates the importance of each given event and captures the user's short-term intent with it. The bidirectional-LSTM can extract the sequential patterns from each given event in both the forward and the backward directions. Both ASLM-LHS and II-RNN-LHS utilize the last hidden state method which retains the context information from the current session and regards it as one part of the user's long-term session representations, compared with the average pooling method. Therefore, the model's subsequent training process benefits from this method by receiving the valuable context information from history sessions. In addition, in Table 1 in the original paper, we observe that, in the Reddit dataset, the number of sessions per user (namely, 62.15) and the average number of events in a session (namely, 3.00) are much smaller than those in the Last.fm dataset (namely, 645.62 and 8.10), which shows that ASLM-LHS can perform better when the user's history information is less adequate. Tables 1 and 4 show the results that the performance of ASLM for validation cases and that for testing cases in Reddit and Last.fm datasets are at the same level, which assure us the validity of our model.