A neighborhood recommendation algorithm under three-way causality force by combining the three-way decision ideas,causal force theory,and formal concept analysis are proposed.Considering the influence of extreme user rating on recommendation accuracy,we classify users by defining the degree of leniency and severity,and correct extreme user rating.Based on the modified score matrix,the three cosine similarity and the similarity structure importance of nodes are calculated to find the expert nodes.Un-der the objective function and constraint conditions that the weak concept of the object needs to meet,the cluster is carried out to ob-tain the neighborhood,and the key conditional attributes and decision attributes are identified according to the attribute density in the neighbourhood,and the confidence between them is calculated.The three-way causality force extraction recommendation rules are combined to carry out neighborhood recommendation for community members.The experimental results show that the proposed algo-rithm is significantly better than other traditional recommendation algorithms in terms of accuracy,recall,and F1.
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