Fine-Grained Global Modeling Learning for Personalized Federated Sequential Recommender

Abstract

Personalized sequential recommender has become a key task in the consumer electronics domain. Existing methods for personalized sequential recommenders primarily focus on modeling user behavior and have achieved satisfactory recommender results. However, the inherent quadratic computational complexity in most existing methods often causes typically results in inefficiencies, impeding real-time suggestions. Additionally, these methods cannot be fine-tuned to the personalized needs of users across different scenarios. To address these challenges, we propose the Fine-Grained Global Modeling Learning for Personalized Federated Sequential Recommender (FedSR). Specifically, we design the Associative Mamba Block to model user profiles from a global perspective and enhance prediction efficiency. Furthermore, we introduce the Variable Response Mechanism to fine-tune parameters according to the personalized needs of users. Additionally, we design the Dynamic Magnitude Loss to retain more local personalized information during training. Extensive experiments on three real-world datasets confirms that the proposed FedSR surpasses current approaches in terms of both performance and efficiency, up to 9.48% improvement.