Most of the existing recommendation models are designed based on the principles of learning and matching: by learning the user and item embeddings and using learned or designed functions as matching models, they try to explore the similarity pattern between users and items for recommendation. However, recommendation is not only a perceptual matching task, but also a cognitive reasoning task because user behaviors are not merely based on item similarity but also based on users’ careful reasoning about what they need and what they want.