# 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  
The generator's generation process involves a condition vector c, which is selected as the user's interaction list in the paper, termed FMGAN-im (Implicit feedback). However, c can take various forms, such as using a random noise vector (random noise). FMGAN-ns (negative sampling) employs the negative sampling method to generate a pseudo-interaction list by randomly selecting some uninteracted items. Noise vectors are generated via uniform sampling with a length equal to the number of items. In negative sampling, a certain proportion (0.8 times the number of items in this paper) of uninteracted item subscripts is sampled with uniform probability, and the user interaction data (i.e., 0, 1) of these subscripts are extracted as a pseudo-interaction list. The results are presented in Figure 6. It is apparent that using random noise vectors yields poor results because the filter structure used in this study truncates the noise vectors, resulting in similar filtered vector distributions, making it difficult for each user to distinguish their respective characteristics, thereby deteriorating the recommendation effect. The model employs the negative sampling list as the condition vector, which yields satisfactory results. However, it uses a portion of the data for training, and multiple random selections lack substantial user bases. During the generator's training process, random sampling is necessary for each iteration, and the computation cost varies. Hence, it is not employed as the final condition vector in this study. Figure 5: The figure represents the data distribution: (**a**) Initial preference distribution; (**b**) Preference distribution after the filter layer. The blue line represents data probability density. The black line represents normal distribution fitting curve on the same data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Model** & **Prec@5** & **Recall@5** & **NDCG@5** & **MRR@5** \\ \hline \multirow{2}{*}{Movielens 100K} & FMGAN-im & **0.467** & **0.154** & **0.496** & **0.698** \\  & MLP & 0.421 & 0.139 & 0.449 & 0.657 \\ \hline \multirow{2}{*}{Movielens 1M} & FMGAN-im & **0.435** & **0.110** & **0.458** & **0.650** \\  & MLP & 0.402 & 0.101 & 0.425 & 0.629 \\ \hline \multirow{2}{*}{Ciao} & FMGAN-im & **0.070** & **0.081** & **0.092** & **0.153** \\  & MLP & 0.061 & 0.071 & 0.086 & 0.146 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of FMGAN-im and MLP on Movielens 100K/1M and Ciao. Figure 7 illustrates the Learning trend of FMGAN-im, FMGAN-ns, and MLP on Movielens 100K. The upward trend of both models is similar, but our model can continue improving for an extended period to achieve superior results. Furthermore, the training epoch does not significantly increase, and the degree of computational complexity remains acceptable. Finally, to validate the efficacy of our model, we compare it with the baselines in the top-N recommendation task. The baseline models are defined as follows:  
* ItemPop: A model that uses the most purchase records for recommendation, which is the simplest non-personalized algorithm method. * BPR: The model pairs purchased items with unpurchased items and optimizes the correct ranking order between item pairs. * FISM: The model uses two low-latitude latent vectors to simulate the item-item similarity matrix. Figure 6: Metrics of different condition vectors. Figure 7: Learning trends of FMGAN-im, FMGAN-ns, and MLP on Movielens 100K. * IRGAN: The model applies the GAN architecture to the recommendation system and uses the binary classification loss to train point states or point pairs. This method is specifically introduced in Section 2.2. * CFGAN: Improves the use of vectors as training parameters and introduces a sampling method to improve the training process. This method is specifically introduced in Section 2.2. Tables 4-6 present the performance of the baseline model and our model on two real-world datasets.