## 4 Experiments and Results  
Settings  
In terms of experimental setup, the DRFM model is a deep reinforcement learning model constructed with the GAFM model as the core, so our experimental steps and experimental comparisons are based on the GAFM model. Specifically, the GAFM model has advantages in both accuracy and speed. Therefore, our experiment also focused on two questions:  
1. Does our model significantly improve prediction accuracy compared to other existing models? 2. Is our model significantly faster than other existing models? 1. The dataset  
In terms of dataset selection, in order to conduct comparative experiments, we still selected the three training sets used in GAFM model training and built a recommendation system environment around the training sets. The first dataset is the data [PERSON] extracted from the 1994 census database [27]. Each data record a person's age and work, etc. Incomecan be predicted using this dataset. The other two datasets are from public data and the Movielens [28] series. We select datasets of 1 M and 10 M, respectively, to achieve multi-classification of film grades. The model for effectiveness evaluation is mainly on two aspects. The first is the prediction accuracy, we will be according to certain scale datasets are divided into two parts of the training set and test set. The model of training in the training set, evaluation of the effect on the test set, and a certain number of iterations are carried out to ensure that the final effect after models is based on feedback learning to balance. The specific evaluation method is as follows: we add the square variance of the predicted result and the real value to get the total deviation value, and the proportion of the remaining correct value to the total value is the accuracy rate. For the evaluation of time efficiency, we counted the total time from training to completion of the evaluation of the model and made a horizontal comparison. Baselines  
Finally, we compared DRFM to the following model:  
* GAFM: As our core model, we first and foremost compared it in many ways. * DeepCrossing: Although an early model, it works well in a variety of small scenarios with very little time complexity, so we used it primarily as a speed comparison target. * Wide and Deep: As an original series of models, it first proposed the concepts of memory and generalization; thus, it is regarded as a basic standard of Wide and Deep models. * DeepFM: As one of the latest models in recommendation systems, it has a good performance in recommendation accuracy, so it is used as a comparison target for accuracy. * AFM: The AFM model adopts the attention mechanism, and the prediction accuracy of the model is further improved. We use its selection as the main comparison object of accuracy. Parameter function explanation:  
There are many parameters involved in this experiment, and a considerable part of them are related parameters inherited from the GAFM model. Specifically, parameter \(g\) is the parameter regulated by _gate_ structure in the GAFM model. Its function is to compress the scale of the input data, and its value is between 0 and 1. Parameter \(d\) is the parameter adjusted by _drop_ structure in the GAFM model. Its function is to accelerate the operation process of gradient descent by cutting out corresponding neurons from the perspective of the network structure, and its value is between 0 and 1. In the framework of reinforcement learning, the parameter we need to explain is \(\alpha\), which determines the exploration intensity when we adopt the random exploration strategy. It is figuratively understood as the exploration factor, and its value is between 0 and 1. Parameter Settings:  
For the GAFM model, the two most important parameters are \(d\) and \(g\), because the model iteration in DRFM is mainly to update various parameters of the GAFM model, so the initial values of \(d\) and \(g\) are set as the optimal values obtained in our experiment. That is \(d=0.82\), \(g=0.91\).