## 4 Experiments and Results
(2) The second is the experimental effect on dataset 2, which is the Movielens dataset with the size of 1 M:  
As can be seen from Figure 6, under the condition that parameter g remains unchanged and parameter drop keeps increasing, GAFM performs slightly worse on dataset 2 than on dataset 1. It fluctuates greatly before parameter drop is 0.5 and then begins to grow rapidly. Figure 4: Prediction accuracy of GAFM under the influence of the drop value. Figure 5: Prediction accuracy of GAFM under the influence of g value. As can be seen from Figure 7, when parameter drop remains unchanged and parameter gap increases, the performance of model dataset 2 is relatively stable without significant change in growth rate. (3) Finally, the experimental results on dataset 3, namely the Movielens dataset of 10 M size:  
As can be seen from Figure 8, due to the large increase in the data size of dataset 3, the accuracy of the model is generally good but fluctuates greatly in the process of parameter gap remaining unchanged and parameter drop increasing. Figure 6: Prediction accuracy of GAFM under drop. Figure 7: Prediction accuracy of GAFM under the influence of gap. It can be seen from Figure 9 that, different from the previous experiment, the accuracy of the model improved very steadily when parameter drop remained unchanged and parameter g kept increasing, which indicated that parameter g had better robustness compared with parameter drop. (4) Conclusions:  
In this section we present the GAFM in drop and g under the influence of two parameters, forecast accuracy, can be seen from the results of the drop and g is closely relative to both parameters and accuracy, but the drop parameters have significant volatility, and the results of the range is significantly smaller than g parameters under interval, the results of the influence of the parameter g for accuracy is stabler and clearer. Figure 8: Prediction accuracy of GAFM under drop. Figure 9: Prediction accuracy of GAFM under the influence of g.  
C. Evaluation of speed (Q2)  
In this part, corresponding to the previous part, we mainly show the effect of the model's running speed under different data sets, and give corresponding conclusions. (1) The first is the experimental effect on data set 1:  
As for the experiments related to speed, we present them with intuitive running time, which indicates that the faster the growth in the figure, the greater the influence and limitation on speed. As can be seen from Figure 10, when parameter g remains unchanged, the growth rate of the model's running time on dataset 1 gradually increases with the increase of parameter drop. As can be seen from Figure 11, when parameter drop remains unchanged, the running time of the model increases with the increase of parameter g.  
Figure 11: Running time of GAFM under the influence of g.  
Figure 10: Running time of GAFM under the influence of drop. (2) The second is the experimental effect on dataset 2, which is the Movielens dataset with the size of 1 M:  
As can be seen from Figure 12, in data set 2, when parameter g remains unchanged and parameter drop keeps increasing, we can find that the results presented by the model are almost consistent with the experiments related to accuracy, but with more unstable fluctuations. As can be seen from Figure 13, when parameter drop remains unchanged and parameter g keeps increasing, the model presents a very stable time linear growth under dataset 2. (3) Finally, the experimental results on dataset 3, namely the Movielens dataset of 10 M size:  
From Figure 14, we can still find that time has a great relationship with the change of accuracy. Similar to the experiment of accuracy, when parameter g remains unchanged  
Figure 12: Running time of GAFM under the influence of drop. Figure 13: Running time of GAFM under the influence of g.  
and parameter drop keeps increasing, the model has a large fluctuation time growth under dataset 3.