CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems
This work addresses the problem of conformity bias for users of large-scale industrial recommender systems, such as Facebook Watch, by providing a scalable solution that improves engagement metrics, though it is incremental relative to existing multi-task models.
The paper tackled conformity bias in large-scale recommender systems by introducing CAM2, a conformity-aware multi-task ranking model that disentangles user conformity from true interests, resulting in a 0.50% increase in user engagement and a 0.21% increase in daily active users on Facebook Watch.
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.