ELECRec: Training Sequential Recommenders as Discriminators
This work addresses the need for more effective training in sequential recommendation, though it appears incremental by reframing the task.
The paper tackles the problem of sequential recommendation by proposing to train models as discriminators rather than generators, which improves effectiveness and efficiency, as demonstrated by experiments on four datasets.
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.