A Contrastive Hypergraph Transformer for session-based recommendation （CHT） model was proposed to address the problems of noise interference and sample sparsity in the session-based recommendation itself. Firstly， the session sequence was modeled as a hypergraph. Secondly， the global context information and local context information of items were constructed by the hypergraph transformer. Finally， the Item-Level （I-L） encoder and Session-Level （S-L） encoder were used on global relationship learning to capture different levels of item embeddings， the information fusion module was used to fuse item embedding and reverse position embedding， and the global session representation was obtained by the soft attention module while the local session representation was generated with the help of the weight line graph convolutional network on local relationship learning. In addition， a contrastive learning paradigm was introduced to maximize the mutual information between the global and local session representations to improve the recommendation performance. Experimental results on several real datasets show that the recommendation performance of CHT model is better than that of the current mainstream models. Compared with the suboptimal model S2-DHCN （Self-Supervised Hypergraph Convolutional Networks）， the proposed model has the P@20 of 35.61% and MRR@20 of 17.11% on Tmall dataset， which are improved by 13.34% and 13.69% respectively； the P@20 reached 54.07% and MRR@20 reached 18.59% on Diginetica dataset， which are improved by 0.76% and 0.43% respectively； verifying the effectiveness of the proposed model.
