Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mutual information estimation is based on the calculation of Kullback Leibler divergence with several limitations, including asymmetrical estimation, the exponential need of the sample size, and training instability. Also, existing data augmentations are mostly stochastic and can potentially break sequential correlations with random modifications. These two issues motivate us to investigate an alternative robust mutual information measurement capable of modeling uncertainty and alleviating KL divergence limitations. To this end, we propose a novel self-supervised learning framework based on Mutual WasserStein discrepancy minimization MStein for the sequential recommendation. We propose the Wasserstein Discrepancy Measurement to measure the mutual information between augmented sequences. Wasserstein Discrepancy Measurement builds upon the 2-Wasserstein distance, which is more robust, more efficient in small batch sizes, and able to model the uncertainty of stochastic augmentation processes. We also propose a novel contrastive learning loss based on Wasserstein Discrepancy Measurement. Extensive experiments on four benchmark datasets demonstrate the effectiveness of MStein over baselines. More quantitative analyses show the robustness against perturbations and training efficiency in batch size. Finally, improvements analysis indicates better representations of popular users or items with significant uncertainty. The source code is at https://github.com/zfan20/MStein.
翻译:自我监督的顺序建议通过以设计良好的数据增强度来最大限度地增加相互信息,大大提高了建议绩效。然而,相互信息估算的基础是计算Kullback Leebelr的差异,并有多种限制,包括不对称的估计、抽样规模的指数需求以及培训不稳定性。此外,现有的数据增强大多是随机变化的,有可能以随机修改来打破顺序相关关系。这两个问题促使我们调查一种能够模拟不确定性和减少 KL差异限制的替代性稳健的相互信息测量方法。为此,我们提出了基于相互瓦塞尔-斯蒂因差异最小化MStein的新颖的自我监督学习框架。我们建议用瓦塞尔斯坦-斯泰因差异最小化的MStein进行新的自我监督学习框架,我们建议用瓦塞斯坦-斯丁差异最小化差异最小化的测量方法来衡量扩大序列之间的相互信息。瓦塞斯坦差异性差异性测量方法建立在2瓦瑟斯坦距离的基础上,并且可能以随机放大的批量大小,我们提议根据瓦塞斯坦-斯蒂因差异度测量标准进行新的对比性学习损失。在四个基准数据设置上进行广泛的实验,用更精确的模型分析,最终显示MS的定量分析。