Case-Based Mixture of Experts Recommendation With Federated and Reinforcement Learning for Bias Reduction

Wearable lifelog data provides continuous physiological and behavioral signals, enabling contextual understanding of users’ real-world states. However, conventional recommendation models rely heavily on static behavioral logs and fail to incorporate contextual variability, leading to limited personalization and biased decision-making. To address these limitations, this study proposes a case-based Mixture-of-Experts (MoE) framework that integrates case routing, federated learning (FL), and reinforcement learning (RL) for context-adaptive action recommendation. The model first classifies user states—derived from multimodal lifelog features such as heart-rate variability, activity intensity, sleep patterns, and fatigue—into multiple context-specific cases. A shared DeepFM–SASRec backbone encodes both static and sequential physiological patterns, while case-specific Adapter, LoRA, and MoE expert heads refine representations to capture intra-case variability. Federated learning enables robust model training under heterogeneous; privacy-sensitive lifelog distributions, and reinforcement learning optimizes long-term physiological improvement by modeling state transitions and action outcomes. Experimental results demonstrate that the proposed framework achieves substantial gains in both accuracy and fairness. The final variant improves HitRate@20 from 0.628 to 0.881 (+40.3%) and reduces Bias Ratio from 0.92 to 0.64 (–30%), effectively balancing precision and behavioral diversity. These findings confirm that combining case-based routing, MoE specialization, FL-driven robustness, and RL-based policy optimization provides a powerful and scalable foundation for next-generation lifelog-driven action recommendation systems.