Edge-enabled federated sequential recommendation with knowledge-aware Transformer

Abstract

The increasing ubiquity of recommendation systems in modern applications has brought about significant changes in various aspects of our daily lives. However, the emergence of privacy concerns has highlighted the need for innovative solutions to address the challenges faced by traditional recommender systems. In this regard, federated learning provides an approach to address privacy concerns by training global models based on edge data. However, the integration of the existing federated learning framework with the latest recommendation system method remains insufficiently explored. To address this issue, we present edge-enabled federated Sequential Recommendation with Knowledge-aware Transformer (KG-FedTrans4Rec) model that incorporates knowledge graph information into sequential recommendation tasks while applying federated learning for privacy preservation. The KG extraction module and sequential processing module in KG-FedTrans4Rec are designed to capture item-related information and sequence inner relationships simultaneously. We leverage Graph Convolutional Networks (GCNs) to aggregate item-related information and user preferences in the KG extraction module. Furthermore, sequential recommender systems model users’ dynamic preferences based on their behaviors to make customized recommendations. In the sequential processing module, we employ replaced token detection and two-stream self-attention strategies to enhance the Transformer-based model. In summary, our proposed KG-FedTrans4Rec model presents a approach to sequential recommendation tasks by incorporating knowledge graph information while preserving user privacy through federated learning. Our experimental results demonstrate the superior performance of our model compared to various recommendation models. This research is expected to make significant contributions to the development of privacy-preserving recommendation systems, which are increasingly essential in modern applications.