Generative Adversarial Networks Based on Contrastive Learning for Sequential Recommendation

Generative Adversarial Networks(GAN) has made key breakthroughs in computer vision and other fields, so some scholars have tried to apply it to sequential recommendation. However, the recommendation performance of GAN-based algorithms is unsatisfactory. The reason for this is that the discriminator cannot distinguish the original data from the generated data well if it only relies on the target function. Based on this, we propose Generative Adversarial Networks based on Contrastive Learning for sequential recommendation (shortened to CtrGAN). Firstly, the generator generates item sequences that the user may be interested in. Additionally, the true item sequences of the user are subjected to a mask operation, which means that the sequences with mask operation are fake. Therefore, both generative sequences and fake sequences can be used in Contrastive Learning to train the generator. The true sequences and their mask operations are then combined with the generative sequences to employ the discriminator for distinguishing them. Finally, the contrastive loss and discriminative loss are combined to guide the generator to generate item sequences that the user may be interested in. Compared with existing sequential recommendation algorithms, experimental results illustrate that CtrGAN has better recommendation accuracy.