Recommender systems are increasingly successful in recommending personalized content to users. However, these systems often capitalize on popular content. There is also a continuous evolution of user interests that need to be captured, but there is no direct way to systematically explore users' interests. This also tends to affect the overall quality of the recommendation pipeline as training data is generated from the candidates presented to the user. In this paper, we present a framework for exploration in large-scale recommender systems to address these challenges. It consists of three parts, first the user-creator exploration which focuses on identifying the best creators that users are interested in, second the online exploration framework and third a feed composition mechanism that balances explore and exploit to ensure optimal prevalence of exploratory videos. Our methodology can be easily integrated into an existing large-scale recommender system with minimal modifications. We also analyze the value of exploration by defining relevant metrics around user-creator connections and understanding how this helps the overall recommendation pipeline with strong online gains in creator and ecosystem value. In contrast to the regression on user engagement metrics generally seen while exploring, our method is able to achieve significant improvements of 3.50% in strong creator connections and 0.85% increase in novel creator connections. Moreover, our work has been deployed in production on Facebook Watch, a popular video discovery and sharing platform serving billions of users.

翻译：推荐系统在向用户推荐个性化内容方面日益成功。然而，这些系统往往倾向于利用热门内容。同时，用户兴趣不断演变且亟需捕捉，但目前缺乏系统化探索用户兴趣的直接方法。这也会影响推荐管道的整体质量，因为训练数据是从呈现给用户的候选项中生成的。本文提出了一种面向大规模推荐系统的探索框架以应对这些挑战。该框架由三部分组成：首先是以用户-创作者探索为核心，聚焦于识别用户感兴趣的最佳创作者；其次是在线探索框架；第三是平衡探索与利用的馈送组合机制，以确保探索性视频获得最优曝光率。我们的方法可轻松集成到现有大规模推荐系统中，仅需极少量修改。我们还通过定义用户-创作者连接相关的指标来分析探索的价值，并理解其如何助力推荐管道整体性能，在创作者与生态价值方面取得显著的在线提升。与探索过程中常见的用户参与度指标下降不同，我们的方法在强创作者连接上实现了3.50%的显著提升，在新兴创作者连接上实现了0.85%的增长。此外，本工作已在服务数十亿用户的热门视频发现与分享平台Facebook Watch上部署生产。