The field of recommender systems is moving towards more efficient and effective methods for generating personalized recommendations. Recent developments have focused on improving the accuracy and fairness of these systems, with a particular emphasis on addressing biases in user feedback and ensuring that content creators are treated fairly. Notably, researchers are exploring new approaches to modeling temporal and sequential information in user behavior sequences, as well as developing more efficient and scalable algorithms for generating recommendations. Some papers have introduced innovative frameworks and techniques, such as target-aware learning and bias-adaptive preference distillation, which have shown promising results in improving the performance of recommender systems. Noteworthy papers include: GRank, which presents a novel structured-index-free retrieval paradigm that improves recall and query performance. BPL, which introduces a bias-adaptive preference distillation learning framework that performs well in both factual and counterfactual test environments.