Correction: Huang et al. An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions. Appl. Sci. 2018, 8 , 2426

We, the authors, wish to make the following corrections to our paper [1] [...]


Changes in Tables
The values in Tables 5-9 in the original paper would be changed to those in Tables 1-5 in this correction, respectively.

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 u t,i ∈ S u t as the representation of the current session S u t , 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.
For Group B, similar results can be seen in Tables 2 and 3 that ASLM-LHS and ASLM-AP outperform all the baselines under all measurements in 1/10 Reddit, 1/80 Last.fm, and Tmall datasets. For the Tmall dataset, the evaluation result of ASLM-LHS shown in Table 3 improves 70.86%, 49.06%, and 114.50% in R@10, R@20, and MRR@20 scores compared with the SWIWO-I method, respectively. Please note that, for the Tmall dataset, as shown in Table 1 in the original paper, there is scarcely enough sessions for each user (namely, 3.99 per user) and number of average session length (2.99, per user) compared with those in Reddit and Last.fm. For 1/10 Reddit and 1/80 Last.fm, since the user history information is reduced dramatically compared with the full Reddit and Last.fm datasets, most of the models' performances significantly decrease accordingly. However, both ASLM models still outperform the strongest baselines, as shown in Table 2. It demonstrates that ASLM can reach better performance even when it is severely short of both long-term and short-term user behavior.
When K changes from 5 to 20, for the Reddit and 1/10 Reddit datasets, there is a downward trend in the relative scores of ASLM-LHS and ASLM-AP. For the Last.fm dataset, there is an upward trend in those of ASLM-LHS. The reason for the upward trend is that there is more abundant user history information in the Last.fm dataset, and, with the increase of K, ASLM-LHS still has the potential to further capture the context information implied by the Last.fm dataset, which results in some space to improve its performance.
In terms of the two versions of ASLM (ASLM-AP using average pooling and ASLM-LHS using the last hidden state), Tables 1-3 show that, for the Reddit dataset, the test performance of ASLM-LHS has an obvious advantage compared to that of ASLM-AP, while they are at the same level for the other four datasets. As mentioned above, the model's subsequent training process benefits from the last hidden state method by receiving the valuable context information from history sessions. Figures   Figures 8 and 9 in the original paper would be changed to Figures 1 and 2 in this correction, respectively.