## 5 Results and Discussion
### Comparison of Performances  
OPNN had the strongest baseline on both the Movielens-1M and Taobao datasets. ALIEN outperformed OPNN with the improvements of 1.14% and 2.41% in AUC score; and 1.49% and 2.50% in ACC score for both the Movielens-1M and Taobao datasets, respectively. For both datasets, although DIN learns the representation of user interests from historical behavior with the candidate item, it has the worst performance without the modeling of feature interactions. For approaches which modeled low-order feature interactions, IPNN and OPNN held the lead compared with DeepFM on Movielens-1M, and the advantage of PNN's two variants against DeepFM became more obvious on the Taobao dataset. In terms of PNN's two variants, OPNN outperformed IPNN on both datasets. Despite being without the modeling of high-order feature interactions and user's historical behavior, they still provided competitive results. The good performance of ALIEN is attributed mainly to the traits that two types of latent information are taken into consideration simultaneously, i.e., attributes-driven and user-behavior-driven latent information, which are extracted from low and high-order feature interactions and the user's history items, respectively, to construct a comprehensive user profile and finally provide a more precise prediction. Please note that for the Movielens-1M dataset, as shown in Table 2, there were abundant interactions for each user but there were not enough fields of features, whereas for the Taobao dataset, interactions for each user were scarce but the number of feature fields was adequate. These results demonstrate that ALIEN can achieve a better performance when it is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Movielens-1M**} & \multicolumn{2}{c}{**Taobao**} \\  & **AUC** & **ACC** & **AUC** & **ACC** \\ \hline DIN & 0.8808 & 0.7993 & 0.8223 & 0.7675 \\ DeepFM & 0.9257 & 0.8447 & 0.8665 & 0.7866 \\ IPNN & 0.9293 & 0.8536 & 0.8710 & 0.7880 \\ OPNN & 0.9294 & 0.8543 & 0.8718 & 0.7916 \\ ALIEN & **0.9400** & **0.8670** & **0.8928** & **0.8114** \\  & **(+1.14\%)** & **(+1.49\%)** & **(+2.41\%)** & **(+2.50\%)** \\ \hline \hline \end{tabular}
\end{table}
Table 6: AUC and ACC scores for the ALIEN models and the baselines for the test sets in Movielens-1M and Taobao datasets.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **MovieLens-1M** & **Taobao** \\ \hline Embedding size & 32 & 32 \\ Learning rate & 0.1 & 0.1 \\ Dropout rate & 0 & 0 \\ MLP\({}^{L^{\prime}}\) layer sizes & [800, 1] & [4500, 2300, 1200, 600, 1] \\ Highest order of feature interactions modeled & 4 & 4 \\ Mini-batch size & 50 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Configurations for ALIEN.  
short of features or the user's history interactions. In other words, ALIEN is capable of coping with severe situations.  
Please note that in [8; 39], the authors reported the experimental results of PNN, DeepFM and DIN on different datasets, i.e., Movielens-20M, Amazon, Alibaba, Criteo and Company [8; 39], which were not utilized in this study. On Movielens-20M, Amazon and Alibaba datasets, DIN was reported to outperform IPNN and DeepFM, and DeepFM performed better than IPNN. On Criteo and Company datasets, DeepFM was reported to outperform the two variants of PNN, i.e., IPNN and OPNN. However, the analysis of the baselines' performances on these datasets is beyond the topic of this paper, since the valuable user and item attributes and the timestamps of each user's history interactions do not exist simultaneously in these five datasets, thereby making them fall short of the requirement for our experiment.