## 4 Experiments and Results
In the process structure of GAFM, we set the Dense layer with 512,256, and 128 units, respectively. Batchsize is 128 and epochs is 15, representing 15 iterations. For DRFM's exploration strategy, we set the exploration factor \(\alpha\) to 0.1. Accuracy evaluation (Q1)  
In order to answer our first question, that is, the accuracy of the model, we show the accuracy effect of DRFM and other models in this part and give corresponding explanations. Figure 7 shows the model's performance on dataset 1:  
Figure 8 shows how the model behaves in dataset 2:  
(3) Figure 9 shows how the model behaves in dataset 3:  
Figure 9: Accuracy of each model in dataset 3. Figure 7: Accuracy of each model in dataset 1. Conclusions:  
As can be seen from the performance of the three datasets, the training trend of the DRFM model is the same as that of the other models on the whole, which indicates that the DRFM model successfully breaks the defect of a large number of iterations and a low starting point of reinforcement learning. The DRFM model achieves the same starting point as other models with the help of the GAFM model's efficient expression effect, which is a major breakthrough. In addition, it can be seen from the trend of accuracy that the DRFM model has a large increase in the early iterations and is fast and stable in the later iterations. The average accuracy is higher than that of other models and significantly higher than that of DeepCrossing model. Running time evaluation (Q2)  
In this part, corresponding to the above experiment, we mainly studied the performance of the running time of each model on the dataset, that is, the evaluation of the running speed. Figure 10 shows how the model behaves in dataset 1:  
Figure 11: Running time of each model on dataset 2. Figure 10: Running time of each model on dataset 1. (3)  
Figure 12 shows how the model behaves in dataset 3:  
(4) Analysis and comparison of deployment efficiency:  
In this section, we show the running time of each model on the dataset. From a practical application point of view, it is a good reflection of the deployment efficiency mentioned at the beginning of this article because model-driven recommendation systems do not require additional effort to consume time and performance. From this point, we can intuitively draw the preliminary results: The deployment efficiency of DRFM in the recommendation system is quite good, second only to GAFM, and far exceeds the effect of other models. In principle, it is not difficult to understand why the time complexity of DRFM is higher than that of GAFM. This is because DRFM has added the random exploration and self-learning updating mechanism of reinforcement learning, and the result of an external loop plus an internal loop inevitably increases the time complexity of the model. However, this is necessary for the recommendation system to obtain high exploration ability and performance; otherwise, it cannot be called the deep reinforcement learning model. We can clearly see from the experimental results of the first part that DRFM is significantly better than GAFM in performance, which is of inestimable value in practical application. On the other hand, we mentioned the two latest research achievements, the DDPPO algorithm and the DE-RL algorithm, in the second part of related work. Because they are oriented toward reinforcement learning rather than recommendation systems and it is difficult to transform the algorithm, we cannot reproduce them and make actual comparisons online in a short time. However, by comparing the public results and data of the two experiments with the results of our DRFM model, we can still find that our model is better than the two algorithms in terms of deployment efficiency or running speed, although the performance is almost equal. We are unable to give a detailed analysis, but we believe that this is due to the excellent performance of the GAFM model and the high efficiency of the random exploration strategy. We will focus on this question in the next phase of our research.