24 citations · 33 across the 2 of their papers we have counts for
3 papers
cs.IR2021★ 24 cited
What are you optimizing for? Aligning Recommender Systems with Human Values
Jonathan Stray, Ivan Vendrov, Jeremy Nixon +2
We describe cases where real recommender systems were modified in the service of various human values such as diversity, fairness, well-being, time well spent, and factual accuracy…
cs.LG2021★ 9 cited
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Martin Mladenov, Chih-Wei Hsu, Vihan Jain +7
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in…
cs.LG2019
Gradient-based Optimization for Bayesian Preference Elicitation
Ivan Vendrov, Tyler Lu, Qingqing Huang +1
Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and con…