2020/05/29 by Marco Morik, Ashudeep Singh, Jessica Hong +1
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Artificial intelligence #Auction Theory and Applications #Computer science #Convergence (economics) #Estimator #Function (biology) #Information retrieval #Learning to rank #Machine learning #Mathematics #Rank (graph theory) #Ranking (information retrieval) #Recommender system #Reinforcement Learning in Robotics #Revenue #Statistics #cs.CY #cs.IR #stat.ML
paper · pdf · doi:10.1145/3397271.3401100
First two authors contributed equally. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 2020
arxiv created 2020/05/29 · arxiv updated 2020/06/01 · openalex publication_date 2020/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users -- as done by virtually all learning-to-rank algorithms -- can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.