Some researchers suggest replacing concept lattices with concept sets in recommendation field due to the high time complexity of concept lattice construction. However， the current studies on concept sets do not consider the role of approximate concepts. Therefore， approximate concepts were introduced into recommendation application， and a genetic algorithm based Approximate Concept Generation Algorithm （ACGA） and the corresponding recommendation scheme were proposed. Firstly， the initial concept set was generated through the heuristic method. Secondly， the crossover operator was used to obtain the approximate concepts by calculating the extension intersection set of any two concepts in the initial concept set. Thirdly， the selection operator was used to select the approximate concepts meeting the conditions according to the similarity of extensions and the relevant threshold to update the concept set， and the mutation operator was adopted to adjust the approximate concepts without meeting the conditions to meet the conditions according to the user similarity. Finally， the recommendation to the target users was performed according to the neighboring users’ preferences based on the new concept set. Experimental results show that， on four datasets commonly used by recommender systems， the approximate concepts generated by ACGA algorithm can improve the recommendation effect， especially on two movie scoring datasets， compared with Probabilistic Matrix Factorization （PMF） algorithm， ACGA algorithm has the F1-score， recall and precision increased by nearly 78%， 104% and 57% respectively； and compared with K-Nearest Neighbor （KNN） algorithm， ACGA algorithm has the precision increased by nearly 12%.