用epinets解决推荐系统冷启动内容的探索与利用难题
Epinet for Content Cold Start
- 引入epinets实现复杂神经网络下的高效不确定性量化
- 在Facebook Reels上提升用户流量和互动效率
- 适合关注推荐系统冷启动与在线学习的研究者
在线内容及其用户规模的快速增长，给现代推荐系统带来了日益严峻的匹配挑战。与自然语言等领域的机器学习不同，推荐系统需自主收集数据，单纯依赖已有知识会导致恶性反馈循环，而盲目探索又会损害用户体验并降低参与度。这一探索-利用权衡在经典的多臂老虎机问题中体现明显，如上置信界（UCB）和汤普森采样（TS）算法表现优异。然而，这些方法在不具备共轭先验结构的场景中难以扩展。近期基于epinets的可扩展不确定性量化方法，使复杂神经网络下的汤普森采样得以高效近似。本文首次将epinets应用于在线推荐系统，在Facebook Reels视频平台的实验表明，该方法显著提升了用户流量和互动效率。
原文摘要 · Abstract (English)
The exploding popularity of online content and its user base poses an evermore challenging matching problem for modern recommendation systems. Unlike other frontiers of machine learning such as natural language, recommendation systems are responsible for collecting their own data. Simply exploiting current knowledge can lead to pernicious feedback loops but naive exploration can detract from user experience and lead to reduced engagement. This exploration-exploitation trade-off is exemplified in the classic multi-armed bandit problem for which algorithms such as upper confidence bounds (UCB) and Thompson sampling (TS) demonstrate effective performance. However, there have been many challenges to scaling these approaches to settings which do not exhibit a conjugate prior structure. Recent scalable approaches to uncertainty quantification via epinets have enabled efficient approximations of Thompson sampling even when the learning model is a complex neural network. In this paper, we demonstrate the first application of epinets to an online recommendation system. Our experiments demonstrate improvements in both user traffic and engagement efficiency on the Facebook Reels online video platform.
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