# FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network
## 1 Introduction
Inconsistent data lengths can lead to inconsistencies in the training format of the recommendation system, making it difficult to extract user and item features using a collaborative method. This challenge has been partially resolved by applying vector training approaches in various recommender methods. This approach combines a user's preferences for items into a vector, unifying the training format, which is the natural input format for neural networks. Different network layers can be used to address the problem of large differences in historical interaction data [12]. Filter-enhanced Multi-Layer Perceptron (MLP) is a novel data processing module in the network form, originally used for signal processing by blocking signals of specific frequencies and allowing signals of other frequencies to pass. In the recommendation models, it can be used as a learnable and efficient noise reduction method incorporated into the network to further enhance the recommendation system's efficiency. In order to overcome the noise caused by the deviation in the collected data and the data length inconsistency in the user interaction information, this paper proposes a filter-enhanced Multi-Layer Perceptron recommendation model based on the Generative Adversarial Network (GAN) framework. Firstly, a generative adversarial model is constructed using the GAN architecture, consisting of a generator G and a discriminator D. G generates fake user interaction lists by learning from the real data distribution, while D distinguishes between the probability of user interaction lists coming from real data versus generated lists. Once the model converges, G generates high-confidence recommendations for specific users. Secondly, to better extract user preferences from historical data, G converts users and items into latent vectors and applies filters to remove noise from the information in order to achieve the debias effect. Multiple linear layers are then utilized to increase the model's fitting nonlinearity data capabilities, thereby improving convergence. D also utilizes multi-linear layers to process the data. Lastly, the study compared three different conditional vectors to determine the optimal model input. The major contributions of this paper are summarized as follows:  
1. We proposed a filter-enhanced MLP recommendation model based on the Generative Adversarial Network framework to solve the problem of recommendation bias. Through comparisons of two real-world datasets from Movielens and Ciao, we demonstrated the effectiveness of filter-enhanced MLP to improve data partitioning to address recommendation bias and achieve better results compared to baseline models. 2. We designed three different condition vectors to enhance the learning ability of the model and verified the influence of different condition vectors on the model effect through experimental comparison. This paper is structured as follows. Section 2 provides an overview of the key related work. In Section 3, we present our proposed model, detailing its design and implementation. Section 4 presents the experimental results of our model on two real-world datasets from Movielens and Ciao, comparing the performance to that of the baseline models. Finally, Section 5 concludes the paper by summarizing our findings and outlining areas for future research.