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：音乐流媒体服务高度依赖其推荐引擎持续为用户提供内容。序列推荐因此在当前文献中受到广泛关注，其中先进方法集中于利用上下文信息（如用户长期与短期历史、项目特征）的自注意力模型；然而，多数研究侧重于长格式内容领域（零售、电影等），而非短格式领域（如音乐）。此外，许多研究未探索在训练过程中融入会话级别的负反馈。本研究探究基于Transformer的自注意力架构以学习隐式会话级别信息用于序列音乐推荐。我们进一步提出一种对比学习任务，通过融入负反馈（如跳过的曲目）来增强正样本命中并惩罚负样本命中。该任务被形式化为一个简单的损失项，可嵌入多种用于序列推荐的深度学习架构。实验表明，相较于忽略用户负反馈的基线架构，该方法能持续提升性能。