Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approaches model transition probabilities between items in a sequence, whether through Markov chains, recurrent networks, or more recently, Transformers. However both old and new issues remain, including data-sparsity and noisy data; such issues can impair the performance, especially in complex, parameter-hungry models. In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues. Contrastive SSL constructs augmentations from unlabelled instances, where agreements among positive pairs are maximized. It is challenging to devise a contrastive SSL framework for a sequential recommendation, due to its discrete nature, correlations among items, and skewness of length distributions. To this end, we propose a novel framework, Contrastive Self-supervised Learning for sequential Recommendation (CoSeRec). We introduce two informative augmentation operators leveraging item correlations to create high-quality views for contrastive learning. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method on improving model performance and the robustness against sparse and noisy data. Our implementation is available online at \url{https://github.com/YChen1993/CoSeRec}
翻译:序列建议包含一套模拟动态用户行为的技术,以预测相继用户数据中未来互动的动态用户行为。 在其核心方面, 此类方法以模型模式模式模式在项目顺序之间的过渡概率, 无论是通过Markov 链条、 经常性网络, 还是在更近的变异器。 但是,老问题和新问题都依然存在, 包括数据差异和繁杂的数据; 这些问题会损害性能, 特别是在复杂、 参数 - 饥饿模型中。 在本报告中, 我们调查对比性自闭式学习( SSL) 应用顺序建议的情况, 以此缓解其中一些问题。 对比性SSLI 构建了从无标签实例( 即正对对对等之间的协议得到最大化的无标签实例) 的增强。 设计对比性 SLS 框架, 包括数据差异性、 各项目之间的关联性、 以及长度分布的偏差。 为此, 我们提出一个新的框架, 对比性自上自定义的自闭式学习( CORec ), 我们引入两个信息增强项目关联性操作者, 以创建高品质的在线观点, 用于对比性、 对比性、 实验性、 测试性、 数据性、 测试性运行性、 、 、 测试性、 测试性、 数据性能、 测试性、 测试性、 测试性、 测试性、 、 测试性、 以及系统性、 测试性