论文标题
ElecRec：培训顺序推荐人作为歧视者
ELECRec: Training Sequential Recommenders as Discriminators
论文作者
论文摘要
顺序建议通常被视为一项生成任务，即训练顺序编码器，以根据其历史相互作用的项目生成用户兴趣的下一项。尽管存在盛行，但这些方法通常需要使用更有意义的样本进行培训才能有效，否则将导致训练有素的模型。在这项工作中，我们建议将顺序推荐人培训为歧视者，而不是发电机。我们的方法没有预测下一个项目，而是训练一个歧视器，以区分采样项目是否为“真实”目标项目。作为辅助模型的发电机与鉴别器共同培训，以取样合理的替代性下一个项目，并将在训练后抛弃。训练有素的判别器被视为最终的SR模型，并将其称为\ modelName。在四个数据集上进行的实验证明了拟议方法的有效性和效率。
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.
