Regret vs. Bandwidth Trade-off for Recommendation Systems
Information Retrieval
2018-10-16 v1 Machine Learning
Machine Learning
Abstract
We consider recommendation systems that need to operate under wireless bandwidth constraints, measured as number of broadcast transmissions, and demonstrate a (tight for some instances) tradeoff between regret and bandwidth for two scenarios: the case of multi-armed bandit with context, and the case where there is a latent structure in the message space that we can exploit to reduce the learning phase.
Keywords
Cite
@article{arxiv.1810.06313,
title = {Regret vs. Bandwidth Trade-off for Recommendation Systems},
author = {Linqi Song and Christina Fragouli and Devavrat Shah},
journal= {arXiv preprint arXiv:1810.06313},
year = {2018}
}