Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical interacted items. Despite their prevalence, these methods usually require training with more meaningful samples to be effective, which otherwise will lead to a poorly trained model. In this work, we propose to train the sequential recommenders as discriminators rather than generators. Instead of predicting the next item, our method trains a discriminator to distinguish if a sampled item is a 'real' target item or not. A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training. The trained discriminator is considered as the final SR model and denoted as \modelname. Experiments conducted on four datasets demonstrate the effectiveness and efficiency of the proposed approach.

翻译：顺序建议通常被视为一种基因任务,即培训一个顺序编码器,根据用户的历史互动项目产生下一个用户利益项目。尽管这些方法很普遍,但通常需要经过更有意义的样本培训才能有效,否则将导致一个训练不良的模式。在这项工作中,我们提议将顺序建议者培训为歧视者而不是产生者。我们的方法不是预测下一个项目,而是训练一个歧视者,以区分抽样项目是否为“真实”目标项目。一个发电机作为辅助模型,与歧视者共同培训,以采样可行的下一个项目,并在培训后将其推出。受过培训的歧视问题被视为最后的SR模型,并被称作\ 模范名称。在四个数据集上进行的实验显示了拟议方法的有效性和效率。