Learning large-scale industrial recommender system models by fitting them to historical user interaction data makes them vulnerable to conformity bias. This may be due to a number of factors, including the fact that user interests may be difficult to determine and that many items are often interacted with based on ecosystem factors other than their relevance to the individual user. In this work, we introduce CAM2, a conformity-aware multi-task ranking model to serve relevant items to users on one of the largest industrial recommendation platforms. CAM2 addresses these challenges systematically by leveraging causal modeling to disentangle users' conformity to popular items from their true interests. This framework is generalizable and can be scaled to support multiple representations of conformity and user relevance in any large-scale recommender system. We provide deeper practical insights and demonstrate the effectiveness of the proposed model through improvements in offline evaluation metrics compared to our production multi-task ranking model. We also show through online experiments that the CAM2 model results in a significant 0.50% increase in aggregated user engagement, coupled with a 0.21% increase in daily active users on Facebook Watch, a popular video discovery and sharing platform serving billions of users.

翻译：通过将大规模工业推荐系统模型拟合到历史用户交互数据上进行学习，使其容易受到符合性偏差的影响。这可能是由多种因素造成的，包括用户兴趣难以确定，以及许多项目的交互行为往往取决于生态系统的其他因素而非其与个体用户的相关性。在本研究中，我们提出了CAM2——一种符合性感知的多任务排序模型，用于在最大的工业推荐平台之一上向用户推送相关项目。CAM2通过利用因果建模将用户对流行项目的符合性行为与其真实兴趣分离，系统地解决了这些挑战。该框架具有通用性，可扩展以支持任何大规模推荐系统中符合性和用户相关性的多种表征。我们提供了更深入的实践见解，并通过与生产环境中的多任务排序模型相比在离线评估指标上的改进，证明了所提出模型的有效性。此外，在线实验表明，CAM2模型在Facebook Watch（一个服务数十亿用户的流行视频发现与分享平台）上实现了聚合用户参与度显著提升0.50%，同时日活跃用户增加0.21%。