Two Stage Recommender

Two-stage recommender systems aim to improve efficiency and accuracy by first filtering a large item pool to a smaller subset of candidates, then ranking those candidates for final presentation. Current research focuses on enhancing both stages, exploring architectures like neural networks and large language models, and employing techniques such as Bayesian methods and non-autoregressive generative models to address challenges like positional bias and computational cost. This approach is significant because it allows for scalability and improved performance in real-world applications, such as online media and e-commerce, while also enabling investigations into fairness and theoretical guarantees for these complex systems.