论文标题
对比度：对比度变异自动编码器，用于顺序建议
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
论文作者
论文摘要
为了利用用户行为序列中的丰富信息，在现实世界推荐系统中已广泛采用顺序建议。但是，当前的方法遇到了以下问题：1）用户项目交互的稀疏性，2）顺序记录的不确定性，3）长尾项目。在本文中，我们建议将对比度学习纳入变分自动编码器的框架中，以同时解决这些挑战。首先，我们介绍了Contastelbo，这是一个新颖的培训目标，将传统的单视Elbo扩展到两视图案例，从理论上讲，从两种观点的角度来看，VAE和对比度学习之间建立了联系。然后，我们提出了对比度变异自动编码器（简而言之对比），这是一种具有对比度正则化的两条VAE模型，作为对比度的实施例，以进行顺序建议。我们进一步介绍了两个简单但有效的增强策略，称为模型增强和变分的增强策略，以创建序列的第二视图，从而使对比度学习成为可能。在四个基准数据集上的实验证明了对比度的有效性和拟议的增强方法。代码可在https://github.com/yuwang-1024/contrastvae中找到
Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods suffer from the following issues: 1) sparsity of user-item interactions, 2) uncertainty of sequential records, 3) long-tail items. In this paper, we propose to incorporate contrastive learning into the framework of Variational AutoEncoders to address these challenges simultaneously. Firstly, we introduce ContrastELBO, a novel training objective that extends the conventional single-view ELBO to two-view case and theoretically builds a connection between VAE and contrastive learning from a two-view perspective. Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation. We further introduce two simple yet effective augmentation strategies named model augmentation and variational augmentation to create a second view of a sequence and thus making contrastive learning possible. Experiments on four benchmark datasets demonstrate the effectiveness of ContrastVAE and the proposed augmentation methods. Codes are available at https://github.com/YuWang-1024/ContrastVAE
