Aiming at the problem that fuzzy formal concept analysis is difficult to apply to large-scale datasets in recommendation applications,a recommendation method based on a heuristic construction of fuzzy concept set is proposed.Sub-contexts are construc-ted for each user based on the similarity between users.Then,new heuristic information is used on the sub-contexts to generate fuzzy concepts with users and items as clues,respectively.Finally,using the internal information of fuzzy concepts,a recommendation confidence integrated with user weights is designed to achieve personalized recommendations for users.The experimental results on six real datasets show that the proposed method has higher recommendation efficiency,and can achieve better recommendation results on sparse data sets compared with classical collaborative filtering algorithms.
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