## 4 Experiments and Results  
A. Settings  
Before conducting experiments on our proposed model, we raised two core questions, around which we conducted experimental verification:  
Q1. Does our model have a significant improvement in prediction accuracy over other existing models? Q2. Does our model have a significant increase in speed compared to other existing models? (1) Data set  
In the selection of data sets, we choose three different data sets. The first data set is the data extracted by [PERSON] from the 1994 census database [28]. Each data set records a person's age, work and other data, and income can be predicted by using this data set. The other two data sets came from the public data and the Movielens [29] series. We selected data sets of 1 M and 10 M, respectively, to achieve multi-classification of movie ratings. The first dataset has 14 features, which are numerical features age, education_num, capital_gain, capital_loss and hours_per_week. Category features include workclass, education, marital_status, occupation, relationship, race, gender, native_country and income_bracket. For the Movielens dataset, we took category features gender and genres, numerical features user_id, movie_id, age, timestamp and occupation. (2) Evaluation methodology  
For model evaluation mainly from two aspects, the first is the prediction accuracy, we divide the data sets into training set and test set according to a certain scale, the model of training in the training set, evaluation of the effect on the test set, specific evaluation methods for, we will acquire the results of prediction and a numerical \((y_{i}-(m\cdot x_{i}+b))\) and acquire the total deviation. The proportion of the remaining correct value in the total value is the accuracy. For the evaluation of time efficiency, we statistically calculated the total time consuming from training to evaluation completion of the model, and made a horizontal comparison. (3) Baselines  
We ended up comparing our model to the following:  
(a) Wide & Deep: As an original series of models, it will be used as a basic standard for the Wide & Deep model. (b) PNN:PNN model plays an important role in machine learning tasks, and its fast speed makes it an important object of speed indicator. (c) AFM:AFM model adopts attention mechanism, and the prediction accuracy of the model is further increased. We take its selection as the main comparison object of accuracy  
(4) Parameter setting  
In the GAFM model, the values of the two core parameters drop and g are the focus of our experimental discussion, considering that the parameter value is meaningless when it is 0, and the result increases too fast when the parameter value is too low, we select the values with a step of 0.01 in the range from 0.1 to 1, respectively, which convenient for experiment \(g_{1}\) and \(g_{2}\) values are the same, while other parameters are fixed parameters of the model. In the process structure, we set the Dense layer of 512, 256 and 128 units, respectively. The value of batchsize is 128, and the value of epochs is 15. B. Accuracy assessment (Q1)  
In order to answer the first question we raised, that is, the evaluation of model accuracy, we present the prediction accuracy effect of the model on different data sets in this part, and give the corresponding conclusions. (1) The first is the experimental results on dataset 1:  
As can be seen from Figure 4, when parameter g, namely gate structural parameter, remains unchanged, with the increase of parameter drop, the accuracy of GAFM running on dataset 1 increases slowly at first and then fast. As can be seen from Figure 5, when parameter drop remains unchanged, with the increase of parameter g, the accuracy rate of the model on dataset 1 shows a rapid but slightly fluctuating increase.