Recommender systems in social media websites provide value to their communities by recommending engaging content and meaningful connections. Scaling high-quality recommendations to billions of users in real-time requires sophisticated ranking models operating on a vast number of potential items to recommend, becoming prohibitively expensive computationally. A common technique "funnels'' these items through progressively complex models ("multi-stage''), each ranking fewer items but at higher computational cost for greater accuracy. This architecture introduces a trade-off between the cost of ranking items and providing users with the best recommendations. A key observation we make in this paper is that, all else equal, ranking more items indeed improves the overall objective but has diminishing returns. Following this observation, we provide a rigorous formulation through the framework of DR-submodularity, and argue that for a certain class of objectives (reward functions), it is possible to improve the trade-off between performance and computational cost in multi-stage ranking systems with strong theoretical guarantees. We show that this class of reward functions that provide this guarantee is large and robust to various noise models. Finally, we describe extensive experimentation of our method on three real-world recommender systems in Facebook, achieving 8.8% reduction in overall compute resources with no significant impact on recommendation quality, compared to a 0.8% quality loss in a non-personalized budget allocation.

Recommender systems have become increasingly accurate in suggesting content to users, resulting in users primarily consuming content through recommendations. This can cause the user's interest to narrow toward the recommended content, something we refer to as preference amplification. While this can contribute to increased engagement, it can also lead to negative experiences such as lack of diversity and echo chambers. We propose a theoretical framework for studying such amplification in a matrix factorization based recommender system. We model the dynamics of the system, where users interact with the recommender systems and gradually "drift'' toward the recommended content, with the recommender system adapting, based on user feedback, to the updated preferences. We study the conditions under which preference amplification manifests, and validate our results with simulations. Finally, we evaluate mitigation strategies that prevent the adverse effects of preference amplification and present experimental results using a real-world large-scale video recommender system showing that by reducing exposure to potentially objectionable content we can increase user engagement by up to 2%.

Large online services employ thousands of people to label content for applications such as video understanding, natural language processing, and content policy enforcement. While labelers typically reach their decisions by following a well-defined "protocol'', humans may still make mistakes. A common countermeasure is to have multiple people review the same content; however, this process is often time-intensive and requires accurate aggregation of potentially noisy decisions.