10 citations · 19 across the 9 of their papers we have counts for
4 papers · 1 filter
Scalable Neural Contextual Bandit for Recommender Systems
Zheqing Zhu, Benjamin Van Roy
High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neu…
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych +4
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected retur…
Evaluating Online Bandit Exploration In Large-Scale Recommender System
Hongbo Guo, Ruben Naeff, Alex Nikulkov +1
Bandit learning has been an increasingly popular design choice for recommender system. Despite the strong interest in bandit learning from the community, there remains multiple bot…
Deep Exploration for Recommendation Systems
Zheqing Zhu, Benjamin Van Roy
Modern recommendation systems ought to benefit by probing for and learning from delayed feedback. Research has tended to focus on learning from a user's response to a single recomm…