RecLearn is a recommender learning framework for students and beginners that provides implementations of recommendation algorithms across two stages: matching recommendation (Top-k) and ranking recommendation (CTR prediction). The framework includes various neural network architectures implemented with TensorFlow2.x, including factorization machines, deep cross networks, and deep feature interaction models. It supports standard datasets like Criteo and provides example code for training and evaluation.