Disclosed herein is a method for managing item recommendation using a degree of association between language units and usage history to manage recommendation of similar items with high probability of purchase, rather than a matching method expressed by keywords, recommendation management, by adding or deleting experience items using a vector model-based reasoning method based on a word-to-word association, in a scheme of planning a novel recognition system through the study of human emotions and tastes, T.P.O (Time, Place, Occasion) and various list-specific characteristics (color, texture, etc.) based on the language used in everyday life in consideration of language units and items preferred or experienced and/or purchased by a user, and of applying machine learning technology and natural language understanding technology.