# Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction
## 4 Experiments
### Performance Comparison  
Table 3 presents the performance comparison results between the baseline models in Section 4.1.2 and the proposed model in Section 3 on two datasets. For simplicity, we denote feature interaction models as type **I**, sequence recommendation models as type **II**, and unified models as type **III**. According to the results, we obtain the following observations:  
* Among all eleven feature interaction baselines, FINAL performs the best. Several reasons are present to explain this. First, the multiplicative feature interaction in the FINAL model can achieve both point-wise and vector-wise feature interactions. However, except for DCNv2, the other feature interaction baselines can only achieve either point-wise or vector-wise feature interaction. Second, the FINAL model simulates a fast exponentiation algorithm to achieve exponential degree growth of any base. Therefore, two factorized interaction layers can reach a high polynomial degree. However, AutoInt and DESTINE can only achieve base-2 exponential degree growth, and the rest of the feature interaction baselines cannot achieve exponential degree growth after stacking multiple layers. Third, the multiplicative feature gating mechanism in the implementation of FINAL also achieves a form of soft feature selection and further improves the model performance. * Among all six sequential recommendation baselines, DMR has the best performance because it not only leverages the attention network to capture the dynamics in the user behavior sequence but also uses the dot product to explicitly extract the static user-item similarity and further match the user with the target item. Other sequential baselines mainly focus on improving the representation learning of the user behavior but fail to obtain direct relations between the user behavior and the target item. * Compared with the feature interaction and sequence recommendation baselines, FESeq complements the advantages of two types of CTR prediction models. It can learn higher-order feature interactions, enhance the relevance of the original features, and increase the power of sequence feature learning in the sequential model. Compared with JointCTR, the proposed model extracts higher-order features from heterogeneous \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Type** & **Models** & **Ele.me** & \multicolumn{2}{c}{**Bundle**} \\ \cline{3-6}  & & **AUC** & **Logloss** & **AUC** & **Logloss** \\ \hline \multirow{8}{*}{I} & Wide and Deep & 0.5489 & 0.0950 & 0.8761 & 0.0111 \\  & IPNN & 0.5535 & 0.0932 & 0.8724 & 0.0126 \\  & DeepFM & 0.5460 & 0.0935 & 0.8750 & 0.0115 \\  & DCN & 0.5495 & 0.0932 & 0.8779 & 0.0112 \\  & AutoInt & 0.5392 & 0.0950 & 0.8783 & 0.0114 \\  & ONN & 0.5432 & 0.0938 & 0.8783 & 0.0114 \\  & DCNv2 & 0.5426 & 0.0937 & 0.8759 & 0.0113 \\  & DESTINE & 0.5444 & 0.0944 & 0.8749 & 0.0113 \\  & DualMLP & 0.5431 & 0.0943 & 0.8788 & 0.0112 \\  & FinalMLP & 0.5497 & 0.0933 & 0.8791 & 0.0111 \\  & FINAL & 0.5528 & 0.0933 & 0.8796 & 0.0113 \\ \hline \multirow{8}{*}{II} & DIN & 0.5988 & 0.0924 & 0.8684 & 0.0115 \\  & SASRec & 0.5867 & 0.0927 & 0.8578 & 0.0114 \\ \cline{1-1}  & DIEN & 0.5569 & 0.0931 & 0.8743 & 0.0123 \\ \cline{1-1}  & BST & 0.5497 & 0.0933 & 0.8766 & 0.0112 \\ \cline{1-1}  & TiSASRec & 0.5914 & 0.0925 & 0.8768 & 0.0122 \\ \cline{1-1}  & DMR & 0.6039 & 0.0921 & 0.8798 & 0.0112 \\ \hline \multirow{2}{*}{III} & JointCTR & 0.5562 & 0.0930 & 0.8882 & **0.0109** \\ \cline{1-1}  & FESeq & **0.6127** & **0.0918** & **0.8912** & 0.0117 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison results. The bold number denotes the best value in the column and the underlined number denotes the second best value in the column. feature fields, further enhancing the expressiveness of the model. Therefore, FESeq is superior to all the baselines in terms of the CTR prediction performance (+1.46% in the AUC and \(-\)0.32% in Logloss on the dataset Ele.me; +0.30% in the AUC on the dataset Bundle).