Adaptive Item-Level Recursion for Efficient Sequential Recommendation

Sequential recommenders can improve performance by allocating computational depth based on an item's contribution to a prediction. This method moves away from fixed-budget allocation, which wastes capacity on converged items and lacks depth for critical ones.