论文标题
可学习的模型增强自我监督学习，以进行连续推荐
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation
论文作者
论文摘要
顺序建议旨在根据用户行为预测下一个项目。最近，已经提出了自我监督的学习（SSL）来提高建议性能。但是，大多数现有的SSL方法都使用统一的数据增强方案，该方案失去了原始序列的序列相关性。为此，在本文中，我们提出了一个可学习的模型增强自我监督的学习，以进行顺序推荐（LMA4REC）。具体而言，LMA4REC首先将模型增强作为数据增强来生成视图的补充方法。然后，LMA4REC使用可学习的Bernoulli辍学来实现模型增强可学习操作。接下来，在对比度观点之间使用自我监督的学习，以从原始序列中提取自我监督的信号。最后，在三个公共数据集上的实验表明，与基线方法相比，LMA4REC方法有效地提高了顺序建议性能。
Sequential Recommendation aims to predict the next item based on user behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to improve recommendation performance. However, most of existing SSL methods use a uniform data augmentation scheme, which loses the sequence correlation of an original sequence. To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec). Specifically, LMA4Rec first takes model augmentation as a supplementary method for data augmentation to generate views. Then, LMA4Rec uses learnable Bernoulli dropout to implement model augmentation learnable operations. Next, self-supervised learning is used between the contrastive views to extract self-supervised signals from an original sequence. Finally, experiments on three public datasets show that the LMA4Rec method effectively improves sequential recommendation performance compared with baseline methods.
