_Applied_  
_sciences_  
**Correction: [PERSON] et al. An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions.**_Appl. Sci._**2018, 8, 2426**  
**[PERSON] 1**, [PERSON] 2, [PERSON] 1,*, [PERSON] 1 and [PERSON] 1  
1 School of Computer Science, Beijing University of Posts & Telecommunications, Beijing 100876, China; [EMAIL_ADDRESS] (R.H.); [EMAIL_ADDRESS] (H.E. ); [EMAIL_ADDRESS] (Z.O.) 2 Leavey School of Business, Santa Clara University, Santa Clara, CA 95053, USA; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel. : +86-139-1102-3433  
**Citation:** [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Correction: [PERSON] et al. An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions.**_Appl. Sci._**2018, 8, 2426.**_Appl. Sci._**2018, 11, 6633. [[https://doi.org/10.3390/app11146633](https://doi.org/10.3390/app11146633)]([https://doi.org/10.3390/app11146633](https://doi.org/10.3390/app11146633))  
Received: 10 June 2021  
Accepted: 25 June 2021  
Published: 19 July 2021  
**Problems:** MDP1 stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **R@5** & **R@10** & **R@20** & **MRR@5** & **MRR@10** & **MRR@20** \\ \hline Most Popular & 0.0569 & 0.0689 & 0.0866 & 0.0470 & 0.0487 & 0.0499 \\ II-RNN-AP & 0.1009 & 0.1341 & 0.1784 & 0.0682 & 0.0725 & 0.0755 \\ II-RNN-LHS & 0.0855 & 0.1142 & 0.1555 & 0.0588 & 0.0625 & 0.0654 \\  & **0.1053** & 0.1387 & 0.1837 & **0.0732** & **0.0776** & **0.0806** \\  & **(+4.36\%)** & (+3.46\%) & (+3.01\%) & (**+7.33\%)** & (**+6.99\%)** & **(+6.75\%)** \\ \multirow{2}{*}{ASLM-LHS} & 0.1042 & **0.1415** & **0.1914** & 0.0693 & 0.0742 & 0.0776 \\  & (+3.34\%) & **(+5.52\%)** & **(+7.31\%)** & (+1.66\%) & (+2.34\%) & (**+2.78\%)** \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{l c c c c c} \hline  & **R@5** & **R@10** & **R@20** & **MRR@5** & **MRR@10** & **MRR@20** \\ \hline Most Popular & 0.1451 & 0.2155 & 0.2882 & 0.0966 & 0.1060 & 0.1111 \\ II-RNN-AP & 0.1484 & 0.2161 & 0.2928 & 0.0969 & 0.1058 & 0.1112 \\  & 0.1614 & 0.2292 & 0.3062 & 0.1061 & 0.1151 & 0.1204 \\  & **0.1904** & **0.2507** & 0.3245 & **0.1256** & **0.1336** & **0.1387** \\  & **(+17.96\%)** & **(+9.38\%)** & (+5.98\%) & (**+18.37\%)** & (**+16.07\%)** & **(**+15.17\%)** \\ \multirow{2}{*}{ASLM-LHS} & 0.1886 & 0.2488 & **0.3251** & 0.1234 & 0.1314 & 0.1366 \\  & (+16.83\%) & (+8.55\%) & **(+6.19\%)** & (+16.27\%) & (+14.13\%) & (**+13.48\%)** \\ \hline \end{tabular}
\end{table} 
Table 2: Group B: Recall and MRR scores for the ASLM models and the baselines for the test sets in 1/10 Reddit and 1/80 Last.fm datasets. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **R@5** & **R@10** & **R@20** & **MRR@5** & **MRR@10** & **MRR@20** \\ \hline Most Popular & 0.0234 & 0.0420 & 0.0123 \\ SWIWO-I & 0.3177 & 0.3810 & 0.1903 \\ SWIWO & 0.3082 & 0.3703 & 0.1885 \\ ASLM-AP & 0.5412 & 0.5661 & 0.4037 \\  & (+70.34\%) & (+48.57\%) & (+112.12\%) \\ \multirow{2}{*}{ASLM-LHS} & **0.5428** & **0.5679** & **0.4082** \\  & **(+70.86\%)** & **(+49.06\%)** & **(+114.50\%)** \\ \hline \end{tabular}
\end{table} 
Table 3: Group B: Recall and MRR scores for the ASLM models and the baselines in the Tmall dataset.