Multi-Perspective Attention Mechanism for Bias-Aware Sequential Recommendation
This work addresses bias issues in sequential recommendation systems, which is an incremental improvement for enhancing recommendation accuracy and fairness in domains like e-commerce or content platforms.
The paper tackles the problem of bias amplification in sequential recommender systems, which leads to the Matthew Effect and limits the capture of dynamic user preferences, by proposing MABSRec, a model that uses multi-perspective attention and graph neural networks, achieving significant advantages in all evaluation metrics.
In the era of advancing information technology, recommender systems have emerged as crucial tools for dealing with information overload. However, traditional recommender systems still have limitations in capturing the dynamic evolution of user behavior. To better understand and predict user behavior, especially taking into account the complexity of temporal evolution, sequential recommender systems have gradually become the focus of research. Currently, many sequential recommendation algorithms ignore the amplification effects of prevalent biases, which leads to recommendation results being susceptible to the Matthew Effect. Additionally, it will impose limitations on the recommender system's ability to deeply perceive and capture the dynamic shifts in user preferences, thereby diminishing the extent of its recommendation reach. To address this issue effectively, we propose a recommendation system based on sequential information and attention mechanism called Multi-Perspective Attention Bias Sequential Recommendation (MABSRec). Firstly, we reconstruct user sequences into three short types and utilize graph neural networks for item weighting. Subsequently, an adaptive multi-bias perspective attention module is proposed to enhance the accuracy of recommendations. Experimental results show that the MABSRec model exhibits significant advantages in all evaluation metrics, demonstrating its excellent performance in the sequence recommendation task.