Regret vs. Bandwidth Trade-off for Recommendation Systems

Song, Linqi, Fragouli, Christina, Shah, Devavrat

arXiv.org Machine Learning 

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.

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