Triadic concept analysis has been introduced into the field of recommendation systems.However,the fusion step of concepts increases the complexity of constructing triadic concepts,and the concept information is not fully utilized in recommen-dation.This paper directly uses single-condition triadic concepts for recommendation,and designs a construction method and fu-sion recommendation algorithm for single-condition triadic concepts.Firstly,the triadic context is decomposed into multiple single-condition triadic contexts,and the concept proportion is designed as heuristic information to generate single-condition tri-adic concept.Then,the popularity of the recommended items on the single-condition triadic concepts is calculated,and the fu-sion recommendation confidence is designed by combining the item condition weight of the triadic context.Finally,the target user is recommended by combining the fusion recommendation confidence and the recommendation threshold.This paper con-ducts experiments on six public datasets.The results show that on datasets with low sparsity,the algorithm proposed in this paper is slightly better than the recommendation effects of GRHC and GreConD-kNN,and comparable to the effects of IBCF and kNN.
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