Emerging short-video platforms like TikTok, Instagram Reels, and ShareChat present unique challenges for recommender systems, primarily originating from a continuous stream of new content. ShareChat alone receives approximately 2 million pieces of fresh content daily, complicating efforts to assess quality, learn effective latent representations, and accurately match content with the appropriate user base, especially given limited user feedback. Embedding-based approaches are a popular choice for industrial recommender systems because they can learn low-dimensional representations of items, leading to effective recommendation that can easily scale to millions of items and users. Our work characterizes the evolution of such embeddings in short-video recommendation systems, comparing the effect of batch and real-time updates to content embeddings. We investigate \emph{how} embeddings change with subsequent updates, explore the relationship between embeddings and popularity bias, and highlight their impact on user engagement metrics. Our study unveils the contrast in the number of interactions needed to achieve mature embeddings in a batch learning setup versus a real-time one, identifies the point of highest information updates, and explores the distribution of $\ell_2$-norms across the two competing learning modes. Utilizing a production system deployed on a large-scale short-video app with over 180 million users, our findings offer insights into designing effective recommendation systems and enhancing user satisfaction and engagement in short-video applications.

翻译：以TikTok、Instagram Reels和ShareChat为代表的新兴短视频平台为推荐系统带来了独特挑战，这些挑战主要源于持续不断的新内容流。仅ShareChat平台每日就接收约200万条新内容，这使得在用户反馈有限的情况下，评估内容质量、学习有效的潜在表示以及精准匹配内容与目标用户群变得尤为复杂。基于嵌入的方法因其能够学习项目的低维表示，从而实现可轻松扩展至数百万项目与用户的高效推荐，已成为工业级推荐系统的常用选择。本研究刻画了短视频推荐系统中此类嵌入的演化特征，比较了批量更新与实时更新对内容嵌入的影响。我们探究了嵌入如何随后续更新而变化，探索了嵌入与流行度偏差之间的关系，并阐明了其对用户参与度指标的影响。本研究揭示了在批量学习与实时学习两种模式下达到成熟嵌入所需交互次数的差异，确定了信息更新峰值点，并探讨了两种竞争学习模式下$\ell_2$范数的分布规律。通过在拥有超过1.8亿用户的大规模短视频应用部署的生产系统进行实验，我们的研究结果为设计高效推荐系统、提升短视频应用的用户满意度与参与度提供了重要见解。