基于音乐收听和品味的冷启动用户播客推荐
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste
摘要：
推荐系统越来越多地用于预测和提供符合用户口味的内容，但将新用户与相关内容相匹配的任务仍然是一个挑战。我们认为播客是一种采用率快速增长的新兴媒体，并讨论了应用传统推荐方法解决冷启动问题时出现的挑战。使用音乐消费行为，我们研究了两种主要的技术来推断Spotify用户对超过20万个播客的偏好。我们的研究结果表明，离线和在线实验的消费高达50%的显着改善。我们对模型性能进行了广泛的分析，并研究了音乐数据作为输入源在推荐中引入偏见的程度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
