通过负反馈优化音乐推荐，让真正想听的歌更近，跳过的歌更远。
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
- 用跳过行为构建对比学习任务，调整歌曲嵌入空间距离。
- 在三个数据集上提升下一首命中率与排名效果，跳过率显著下降。
- 适合做流媒体音乐推荐系统优化的研究者和工程师参考。
现代音乐流媒体服务高度依赖推荐引擎为用户提供内容。序列化推荐——在单个会话中持续提供上下文连贯的新内容——是当前研究的热点。用户反馈（对推送内容的正向或负向响应）用于学习用户偏好以驱动推荐。本文将这一思想扩展至会话式推荐，通过在损失函数中建模负反馈（即跳过行为），实现上下文连贯的音乐推荐。我们提出一种序列感知的对比子任务，对会话中的项目嵌入进行结构化，使得真实下一个正向项目（忽略跳过的项目）在嵌入空间中更接近，而跳过的曲目则与会话中所有项目保持更远距离。该设计直接影响基于K近邻搜索的下一首推荐排序，同时提升了真实下一首项目的排名。在三种音乐推荐数据集上，将该任务融入现有领先方法，均取得一致的性能提升，体现在下一首命中率、项目排序和跳过降权方面，且随着用户反馈增多，收益愈发明显。
原文摘要 · Abstract (English)
Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation -- continuously providing new items within a single session in a contextually coherent manner -- has been an emerging topic in current literature. User feedback -- a positive or negative response to the item presented -- is used to drive content recommendations by learning user preferences. We extend this idea to session-based recommendation to provide context-coherent music recommendations by modelling negative user feedback, i.e., skips, in the loss function. We propose a sequence-aware contrastive sub-task to structure item embeddings in session-based music recommendation, such that true next-positive items (ignoring skipped items) are structured closer in the session embedding space, while skipped tracks are structured farther away from all items in the session. This directly affects item rankings using a K-nearest-neighbors search for next-item recommendations, while also promoting the rank of the true next item. Experiments incorporating this task into SoTA methods for sequential item recommendation show consistent performance gains in terms of next-item hit rate, item ranking, and skip down-ranking on three music recommendation datasets, strongly benefiting from the increasing presence of user feedback.
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