Enhanced side information fusion framework for sequential recommendation

Abstract

The fusion of side information in sequential recommendation (SR) is a recommendation system technique that combines a user’s historical behavior sequence with additional side information to provide more accurate personalized recommendations. Recent methods are based on self-attention mechanisms, incorporating side information as part of the attention matrix to update item representations. We believe that the integration method via self-attention mechanisms does not fully utilize side information. Therefore, we designed a new Enhanced Side Information Fusion framework (ESIF) for sequential recommendations. Specifically, we have altered the fusion strategy by using an attention matrix to simultaneously update the representations of items and side information, thereby increasing the use of side information. The attention matrix serves to balance various features, ensuring effective utilization of side information throughout the fusion process. We designed a Gated Linear Representation Fusion Module, comprising linear transformations and gated units. The linear transformation processes the input data, while the gated unit dynamically adjusts the degree of information flow based on the input. This module then combines the updated item representation with the side information representation for more efficient use of side information. Additionally, user interaction behavior data inevitably contains noise. The presence of noise can disrupt the model’s performance, affecting the accuracy and reliability of the results. Therefore, we introduced a denoising module in ESIF to enhance recommendation accuracy by reducing noise. Our experimental results demonstrate that ESIF achieves superior performance across five real-world datasets, surpassing the current state-of-the-art side information fusion SR models.