# Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction
## 3 Experimental Verification
### Performance Comparison Experiments of Different Algorithms  
This paper chooses to compare experiments with other similar _FM_ family recommendation algorithms: AFM, NFM, ONN, DeepFM, and xDeepFM.  
AFM: The AFM model is based on the _FM_ model by introducing an attention mechanism to learn the importance of different second-order crossover features;  
NFM: The NFM model uses a Bi-interaction Pooling structure for feature crossover learning as a way to improve the ability of _FM_ to capture multi-order interaction information between features;  
ONN [16]: the ONN model distinguishes between different Embedding layer operations on the same feature and proposes a new Embedding model;  
xDeepFM [17]: xDeepFM proposes a new neural model called a compressed interaction network to replace the _FM_ part in DeepFM.  
On the MovieLens-20M dataset, the evaluation metric values of each algorithm are shown in Table 1, and the training time of models is shown in Figure 5. On the Amazon dataset, the evaluation metric values of each algorithm are shown in Table 2.  
From Tables 1 and 2, it can be seen that because the Amazon dataset is more sparse, all kinds of algorithms generally work better on the MovieLens-20M dataset. AFM introduces the attention mechanism but does not learn the higher-order features, so the results of each index are poor. ONN, DeepFM, xDeepFM, and fDeepFM all modify the Embedding layer to improve the model expression in the shallow layer. Then use \(DNN\) to learn higher-order features, which makes the algorithm increasingly more effective. The algorithm of fDeepFM works best because the item features are divided into word-like features and text description-like features for extraction separately. This makes the item feature content  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & _MAE_ & _RMSE_ & _F1-Score_ & _AUC_ \\ \hline AFM & 0.7451 & 0.9442 & 0.8523 & 0.7861 \\ NFM & 0.7367 & 0.9395 & 0.8475 & 0.7794 \\ ONN & 0.7309 & 0.9321 & 0.8584 & 0.7843 \\ DeepFM & 0.7334 & 0.9354 & 0.8623 & 0.7950 \\ xDeepFM & 0.7256 & 0.9240 & 0.8738 & 0.8074 \\ fDeepFM & 0.7165 & 0.9114 & 0.8790 & 0.8178 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical comparison of evaluation indexes of the algorithm in MovieLens-20M data set.  
Figure 5: Comparison of training time.  
information integrate more emotion and contextual semantics and introduces the GRU model to learn the change of user interest, and over time the recommendation effect is improved. So the _MAE_ value, _RMSE_ value, _F1-Score_ value, and _AUC_ value are improved by 1.69%, 2.4%, 1.67%, and 2.28%, respectively, on the MovieLens-20M dataset compared with DeepFM; on the Amazon dataset, the _MAE_ value, _RMSE_ value, _F1-Score_, and _AUC_ values were optimized by 3.2%, 3.86%, 1.63%, and 2.2%, respectively, compared to DeepFM.  
From Figure 5, it can be seen that the AFM and NFM training time is short, but the AFM time is the shortest, indicating that the addition of an attention mechanism can shorten the training time. ONN takes the longest to train because this model sacrifices complexity to improve recommendation accuracy. The training time of DeepFM, xDeepFM, and fDeepFM increases sequentially, and the fDeepFM increases the training time compared with the DeepFM model, but its accuracy is improved.