基于反向延长增强的对抗生成网络推荐算法OA北大核心CSTPCD

Addressing the challenge of suboptimal performance in existing sequential recommendation models due to severe data sparsity,this paper proposed a generative adversarial network recommendation algorithm based on reverse extension en-hancement.The approach extended and enhanced interaction sequences to obtain high-quality training data,mitigating the is-sue of insufficient model training caused by data sparsity.Firstly,it extended the project sequences backwardly using pseudo-prior terms to deepen the features of the project sequences.Secondly,it shifted the target of extension enhancement from short sequences to all user sequences,thoroughly exploring contextual information embedded in long sequences and alleviating noise issues arising from an excessively large proportion of pseudo-prior terms in augmented sequences.Finally,it employed a gener-ative adversa-rial network with shared project embeddings,and jointly trained the discriminator and generator to enhance the model's recommendation performance.Experimental results on three public datasets demonstrate an average improvement of 30％in hit rate(HR@N)and normalized discounted cumulative gain(NDCG@N)compared to the optimal baseline ELEC-Rec,confirming the effectiveness of reverse extension enhancement in mining sequence features and alleviating data sparsity.