论文标题
根据音乐的聆听和品味，推荐播客的冷启动用户
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste
论文作者
论文摘要
推荐系统越来越多地用于预测和服务与用户口味相符的内容，但是将新用户与相关内容匹配的任务仍然是一个挑战。我们认为播客是一种新兴的媒介，采用迅速增长，并讨论采用传统建议方法来解决冷门问题时会出现的挑战。使用音乐消费行为，我们研究了两种主要技术，用于推断超过200k播客的偏好。我们的结果显示，离线和在线实验的消费量最高50 \％。我们提供有关模型性能的广泛分析，并研究音乐数据作为输入源的程度引入建议中的偏见。
Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem. Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts. Our results show significant improvements in consumption of up to 50\% for both offline and online experiments. We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.
