跨不同内容类型排序：Multinomial Blending 的稳健优势
Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending
AI总结：
针对跨内容类型排序的传统LTR算法适配难题，提出可结合多数现有LTR算法的multinomial blending方法，经多维度对比及Amazon Music的A/B测试验证其应用价值。
摘要：
越来越多媒体流媒体服务已扩展其服务内容，纳入多种内容类型的实体。例如，最初仅提供音乐的音频流媒体服务，如今也提供播客、周边商品和视频。由于不同内容类型对应的用户参与模式存在差异，将不同内容类型的物品排序后整合到单一展示列表中，对传统Learning-to-Rank（LTR，学习排序）算法构成了重大挑战。我们探索了一种用于跨内容类型排序的简单方法，名为multinomial blending（MB，多项混合），该方法可与大多数现有LTR算法结合使用。我们不仅在排序质量方面将MB与现有基线进行对比，还从其他行业相关视角展开比较，包括可解释性、易用性，以及在用户行为变化、排序模型需重新训练的动态环境中的稳定性。最后，我们报告了Amazon Music排序场景下的A/B测试结果。
英文摘要：
An increasing number of media streaming services have expanded their offerings to include entities of multiple content types. For instance, audio streaming services that started by offering music only, now also offer podcasts, merchandise items, and videos. Ranking items across different content types into a single slate poses a significant challenge for traditional learning-to-rank (LTR) algorithms due to differing user engagement patterns for different content types. We explore a simple method for cross-content-type ranking, called multinomial blending (MB), which can be used in conjunction with most existing LTR algorithms. We compare MB to existing baselines not only in terms of ranking quality but also from other industry-relevant perspectives such as interpretability, ease-of-use, and stability in dynamic environments with changing user behavior and ranking model retraining. Finally, we report the results of an A/B test from an Amazon Music ranking use-case.
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。
