Learning with Exposure Constraints in Recommendation Systems
This addresses the challenge of balancing user welfare with content provider incentives in dynamic recommendation systems, representing an incremental improvement by applying bandit methods to exposure constraints.
The paper tackles the problem of maintaining content provider viability in recommendation systems by modeling it as a contextual multi-armed bandit with exposure constraints, where arms (providers) require minimum pulls to stay available, and it develops algorithms with sub-linear regret and proves optimality up to logarithmic factors.
Recommendation systems are dynamic economic systems that balance the needs of multiple stakeholders. A recent line of work studies incentives from the content providers' point of view. Content providers, e.g., vloggers and bloggers, contribute fresh content and rely on user engagement to create revenue and finance their operations. In this work, we propose a contextual multi-armed bandit setting to model the dependency of content providers on exposure. In our model, the system receives a user context in every round and has to select one of the arms. Every arm is a content provider who must receive a minimum number of pulls every fixed time period (e.g., a month) to remain viable in later rounds; otherwise, the arm departs and is no longer available. The system aims to maximize the users' (content consumers) welfare. To that end, it should learn which arms are vital and ensure they remain viable by subsidizing arm pulls if needed. We develop algorithms with sub-linear regret, as well as a lower bound that demonstrates that our algorithms are optimal up to logarithmic factors.