The field of personalized recommendation systems is moving towards more sophisticated and adaptive methods to handle sparse domains and cross-functional coordination. Recent research has focused on developing novel loss functions, multi-agent reinforcement learning frameworks, and cooperative knowledge transfer approaches to improve the accuracy and robustness of recommendation systems. These advancements have shown significant improvements in key metrics such as recall and NDCG, and have the potential to enhance firm-wide profitability. Noteworthy papers include:

• Adaptive Weighted Loss for Sequential Recommendations on Sparse Domains, which proposes a dynamic weighted loss function to prioritize sparse domains.
• Closing the Loop: Coordinating Inventory and Recommendation via Deep Reinforcement Learning on Multiple Timescales, which develops a unified multi-agent RL framework for joint optimization across distinct functional modules.
• MARCO: A Cooperative Knowledge Transfer Framework for Personalized Cross-domain Recommendations, which leverages cooperative multi-agent reinforcement learning to mitigate negative transfer issues.
• Fine-grained auxiliary learning for real-world product recommendation, which proposes an auxiliary learning strategy to boost coverage through learning fine-grained embeddings.