WatchLens：用于在线视频推荐实验的可配置平台
WatchLens: A Configurable Platform for Online Video Recommendation Experiments
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中文总结 AI 辅助
WatchLens是一款开源可配置平台，可在单一工作流中关联在线视频推荐策略与用户播放等行为，通过模块化架构实现灵活配置，支持会话级效果比较，为在线视频推荐研究提供可复现的单服务器部署方案。
中文摘要 AI 辅助
研究视频推荐系统如何影响用户行为，需要将播放行为与产生该行为的推荐条件关联起来的在线实验。现有的用户研究基础设施仅提供其中一项功能，无法在单一实验工作流中同时实现两者。我们提出WatchLens，一款开源平台，填补这一空白。WatchLens采用模块化架构，用户界面、内容源和推荐策略均可独立配置，策略可分别分配给信息流和观看页面，同时标准化日志层在记录时将推荐策略和排名位置附加到每个事件中。该设计使研究人员能够分析推荐策略和排名位置如何影响下游播放行为、会话延续以及信息流与观看页面间的导航，策略与结果的关联存在于每个事件中，无需事后重构。我们通过短视频案例研究演示WatchLens，在保持界面、信息流策略和内容池不变的情况下仅改变观看页面策略，展示该平台如何支持会话级别的推荐效果比较，适用于真实观看行为。WatchLens作为公开可用的单服务器可部署系统发布，用于可复现的在线视频推荐研究。
英文摘要
Studying how video recommender systems shape user behavior requires online experiments that link playback behavior with the recommendation conditions that produced it. Existing user-study infrastructure provides one or the other, but not both within a single experimentation workflow. We present WatchLens, an open-source platform that fills this gap. WatchLens adopts a modular architecture in which user interfaces, content sources, and recommendation policies are independently configurable, with policies assignable separately to the feed and the watch page, while a standardized logging layer attaches the recommendation policy and ranking position to every event at recording time. This design enables researchers to analyze how recommendation policies and ranking positions shape downstream playback behavior, session continuation, and navigation between the feed and the watch page, with the linkage between policy and outcome available in each event rather than reconstructed afterwards. We demonstrate WatchLens with a short-form video case study that holds the interface, feed policy, and content pool constant while varying only the watch-page policy, showing how the platform supports session-level comparison of recommendation effects on real viewing behavior. WatchLens is released as a publicly available, single-server deployable system for reproducible online video recommendation research.
