Music streaming services heavily rely on their recommendation engines to continuously provide content to their consumers. Sequential recommendation consequently has seen considerable attention in current literature, where state of the art approaches focus on self-attentive models leveraging contextual information such as long and short-term user history and item features; however, most of these studies focus on long-form content domains (retail, movie, etc.) rather than short-form, such as music. Additionally, many do not explore incorporating negative session-level feedback during training. In this study, we investigate the use of transformer-based self-attentive architectures to learn implicit session-level information for sequential music recommendation. We additionally propose a contrastive learning task to incorporate negative feedback (e.g skipped tracks) to promote positive hits and penalize negative hits. This task is formulated as a simple loss term that can be incorporated into a variety of deep learning architectures for sequential recommendation. Our experiments show that this results in consistent performance gains over the baseline architectures ignoring negative user feedback.

翻译：音乐流媒体服务在很大程度上依赖其推荐引擎持续向用户提供内容。因此，序列推荐在当前文献中备受关注，最先进的方法主要聚焦于自注意力模型，利用长期和短期用户历史记录及项目特征等上下文信息。然而，大多数此类研究针对于长篇内容领域（如零售、电影等），而非音乐等短篇内容。此外，许多研究未在训练过程中融入用户会话级别的负面反馈。在本研究中，我们探索了基于Transformer的自注意力架构，用于学习序列音乐推荐中的隐式会话级别信息。我们还提出了一种对比学习任务，以融合负面反馈（例如跳过的曲目），从而促进正面命中并惩罚负面命中。该任务表现为一个简单的损失项，可集成到多种用于序列推荐的深度学习架构中。实验结果表明，与忽视用户负面反馈的基线架构相比，本方法在性能上取得了持续提升。