# FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network
## 1 Introduction  
Information retrieval systems play a vital role in information dissemination and filtering. These systems encompass a range of applications, such as web search [1], recommendation systems [2], personalized advertisements [3], and text retrieval [4]. An accurate and efficient recommendation system can mitigate the effects of information overload by providing users with a curated list of relevant content that aligns with their query conditions [5], thereby greatly improving the user experience. Collaborative filtering (CF) [6] recommendation is commonly used in recommendation systems. CF generates representation vectors of users and items by learning user history interaction information, and calculates the similarities and associations between users and items based on the representation vectors. CF has shown excellent performance in generating personalized recommendations, but the computational complexity of CF increases as the system grows. Handling large datasets and computing similarity measures can become challenging and affect the algorithm's scalability. Recently, the graph convolutional neural network [7] has become the state-of-the-art method in the recommendation problem. This method has a strong ability to process structured data. A well-designed graph convolutional model can learn correlations of different data sources and high-order information. However, the graph convolutional neural network model is usually not easy to construct suitable graph nodes and edges, and the design of the network structure is also relatively complicated. It is also necessary to use more side information in the dataset to extract features. No matter what method is applied for recommendation, it needs to learn from the user's historical interaction information. Obtaining user history interaction informationfor the recommendation process can be challenging as it is typically generated during the user's usage and not collected through questionnaires [8]. There are several ways to collect data for recommendation models. One common approach is to obtain specific evaluations of items from users, display these evaluations, and create a user-item index in the evaluation matrix. Another method that aligns better with modern software operation is to collect implicit feedback, where a user's click on an item is considered a positive evaluation, and non-clicked items are treated as negative evaluations. Implicit feedback data are collected easily, and there is a greater abundance of interaction information than with explicit feedback. When users rely on software to make decisions, the collected data may be influenced by a range of factors such as exposure, conformity, and location, resulting in data deviations. Exposure bias, for instance, arises when users are only exposed to specific items, and position bias is formed because users tend to interact more with items placed in front. These deviations cause the interaction distribution of users and items to deviate from their real preference distribution [9], thereby affecting the recommendation model's input. Consequently, it becomes challenging for the model to learn user preferences from noisy data. For example, when using implicit feedback data, treating all uninteracted items as negative feedback can create exposure bias. In such cases, the recommendation model may fail to analyze items similar to the user's favorite item \(i\), which they have not yet observed. To address this problem, evaluation indicators such as the propensity score method [10] or sampling methods [11] can be employed to mitigate exposure bias. By using these methods, the likelihood of exposure bias can be reduced, and the model can learn user preferences more accurately. User interaction with information is a spontaneous behavior, resulting in limited amounts of data collected, reflected in a list of interaction histories of varying lengths.