## 5 Discussion  
As can be seen from the experimental results in the fourth part, the DRFM model inherits the high accuracy and low running time of its core model, the GAFM model, and has many advantages compared with other models. Specifically, we found the following:  
1. From the perspective of reinforcement learning, using more effective and technical agents can effectively reduce the number of iterations in the experiment and significantly enhance the effect. This is because stronger agents and effective exploration strategies can make the model have higher accuracy in early iterations, thus raising the lower limit of learning and, to some extent, raising the upper limit.
2. From the point of view of the recommendation system, the DRFM model makes further progress in the final recommendation accuracy and reaches a new height. This is due to the self-updating process integrating reinforcement learning, which enables the model to dynamically adjust parameters to obtain feedback. Although an increase in time complexity is inevitable, the overall accuracy and speed of the double advantage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & _Dataset1_ & _Dataset2_ & _Dataset3_ \\ \hline _DRFM_ & 0.8493 & 0.8389 & 0.8292 \\ _GAFM_ & 0.8438 & 0.8342 & 0.8238 \\ _DeepCrossing_ & 0.8269 & 0.8269 & 0.8033 \\ _Wide and Deep_ & 0.8450 & 0.8317 & 0.8211 \\ _DeepFM_ & 0.8495 & 0.8335 & 0.8225 \\ _AFM_ & 0.8427 & 0.8327 & 0.8266 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of each model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & _Dataset1_ & _Dataset2_ & _Dataset3_ \\ \hline _DRFM_ & 16.88 & 366 & 19764 \\ _GAFM_ & 16.03 & 339 & 19189 \\ _DeepCrossing_ & 17.56 & 398 & 21045 \\ _Wide and Deep_ & 17.49 & 447 & 22356 \\ _DeepFM_ & 19.22 & 452 & 23452 \\ _AFM_ & 19.84 & 476 & 24476 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time of each model.  
\begin{table}
\begin{tabular}{c c} \hline \hline _BASELINES_ & _INPROVEMENT_ \\ \hline _GAFM_ & +1.56\% \\ _DeepCrossing_ & +6.03\% \\ _Wide and Deep_ & +1.96\% \\ _DeepFM_ & +1.19\% \\ _AFM_ & +1.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Growth of the DRFM compared with other models.  
3. It has been proved that the model has different performances in different sizes and different types of datasets, but the overall trend is the same. Specifically, the recommendation environment built by the dataset adopted in the experiment is user-oriented and object-oriented, respectively, so the members of environment and state are different. However, the experimental proof does not affect the effective and fast learning of DRFM model and the recommendation results. In other words, the DRFM model is not only suitable for specific recommendation scenarios but also has high generality.  
In this paper, several models compared with DRFM have different performances from experimental results. Except for the DeepCrossing model, the accuracy of the other models is not very different, but only DRFM and GAFM are faster in terms of running speed. The advantages of the DeepCrossing model in running speed can also be seen by comparing the other three models. In addition, in terms of model parameters, although we fixed the exploration factor \(\alpha=0.1\) in the experiment, which actually represents the weight we assign to the exploration, we found through many comparative experiments that whether the exploration is large or small has a great impact on the direction and effect of the model iteration. We finally draw the conclusion that the value range of parameter \(\alpha\) should be between 0.05 and 0.25, beyond which the effect will decrease greatly. In general, the DRFM model achieves or even exceeds our expectations, and has good effects and advantages in all aspects.