The field of recommender systems is moving towards addressing the challenges of bias mitigation, algorithm adaptation, and interference in online experiments. Researchers are exploring new methods to ensure fairness and accuracy in recommender systems, such as using systematic literature reviews and taxonomies to identify robust bias mitigation techniques. Additionally, there is a growing interest in understanding the dynamics of two-sided attention markets and developing new frameworks for entity representation learning and deep reinforcement learning for ranking utility tuning. Notable papers in this area include: Bias Mitigation for AI-Feedback Loops in Recommender Systems, which presents a systematic literature review and taxonomy of bias mitigation methods. Direct Profit Estimation Using Uplift Modeling under Clustered Network Interference, which proposes a practical methodology for optimizing policies using interference-aware estimators. ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems, which introduces a framework for automatically finding the minimal set of hyperparameter changes needed to satisfy guardrails. Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest, which proposes a general framework for personalized utility tuning using deep reinforcement learning.