# FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network
## 4 Experiment and Discussion
### Discussion and Results
#### 4.2.3 Model Performance under Different Condition Vectors
Based on the experimental results, our model outperforms other baselines on both datasets and has achieved remarkable enhancements on the Movielens 100k dataset. The results of the high sparsity Ciao dataset shown in Table 6 are very noteworthy. Although it has a similar number of users and items to the Movielens 100k dataset, the scarcity of interaction records greatly reduces the recommendation performance. Comparing it with the baseline model indicates that the GAN model architecture can be effectively applied in the recommendation system field, and the generator's combination of filters and MLP can efficiently extract user preference information. The above experimental results show that the improvement of our model has achieved certain results. The model generates embedding vectors based on user and item IDs to store the information representation learned by the model. After improving the data distribution through filters, MLP can generate recommendation lists more accurately. The introduction of GAN architecture helps the model strengthen this learning process. The introduction of \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metrics** & **P@5** & **P@20** & **R@5** & **R@20** & **G@5** & **G@20** & **M@5** & **M@20** \\ \hline ItemPop & 0.030 & 0.023 & 0.039 & 0.126 & 0.046 & 0.063 & 0.054 & 0.067 \\ BPR & 0.035 & 0.024 & 0.040 & 0.140 & 0.050 & 0.065 & 0.067 & 0.079 \\ FISM & 0.060 & 0.037 & 0.071 & 0.179 & 0.080 & 0.110 & 0.127 & 0.147 \\ IRGAN & 0.034 & 0.022 & 0.042 & 0.110 & 0.045 & 0.065 & 0.080 & 0.087 \\ CFGAN & 0.068 & 0.040 & 0.079 & 0.190 & 0.089 & 0.117 & 0.151 & 0.164 \\ \hline FMGAN-ns & 0.065 & 0.039 & 0.076 & 0.186 & 0.087 & 0.113 & 0.149 & 0.163 \\ FMGAN-im & **0.070** & **0.043** & **0.081** & **0.192** & **0.092** & **0.120** & **0.153** & **0.166** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison results in Ciao. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Metrics** & **P@5** & **P@20** & **R@5** & **R@20** & **G@5** & **G@20** & **M@5** & **M@20** \\ \hline ItemPop & 0.182 & 0.139 & 0.105 & 0.253 & 0.165 & 0.196 & 0.255 & 0.293 \\ BPR & 0.350 & 0.237 & 0.117 & 0.288 & 0.372 & 0.381 & 0.558 & 0.575 \\ FISM & 0.428 & 0.285 & 0.146 & 0.354 & 0.464 & 0.429 & 0.675 & 0.686 \\ IRGAN & 0.320 & 0.223 & 0.110 & 0.278 & 0.346 & 0.370 & 0.539 & 0.525 \\ CFGAN & 0.445 & 0.327 & 0.149 & 0.360 & 0.477 & 0.440 & 0.682 & 0.701 \\ \hline FMGAN-ns & 0.446 & 0.330 & 0.145 & 0.359 & 0.473 & 0.437 & 0.675 & 0.698 \\ FMGAN-im & **0.467** & **0.340** & **0.154** & **0.365** & **0.496** & **0.454** & **0.698** & **0.719** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison results in Movielens 100K. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metrics** & **P@5** & **P@20** & **R@5** & **R@20** & **G@5** & **G@20** & **M@5** & **M@20** \\ \hline ItemPop & 0.155 & 0.120 & 0.075 & 0.195 & 0.153 & 0.179 & 0.251 & 0.296 \\ BPR & 0.340 & 0.251 & 0.075 & 0.207 & 0.347 & 0.361 & 0.535 & 0.554 \\ FISM & 0.419 & 0.304 & 0.106 & 0.269 & 0.442 & 0.398 & 0.635 & 0.649 \\ IRGAN & 0.262 & 0.213 & 0.071 & 0.165 & 0.265 & 0.245 & 0.302 & 0.337 \\ CFGAN & 0.431 & 0.307 & 0.107 & 0.164 & 0.452 & 0.404 & 0.643 & 0.659 \\ \hline FMGAN-ns & 0.433 & 0.308 & 0.106 & 0.162 & 0.451 & 0.402 & 0.641 & 0.654 \\ FMGAN-im & **0.435** & **0.311** & **0.110** & **0.169** & **0.458** & **0.406** & **0.650** & **0.662** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison results in Movielens 1M. different input vectors expands the structure of the model and provides more directions for future improvement.