IRLGMLOct 15, 2018

Regret vs. Bandwidth Trade-off for Recommendation Systems

arXiv:1810.06313v1
Originality Incremental advance
AI Analysis

This addresses bandwidth limitations in recommendation systems, but it appears incremental as it builds on existing bandit and latent structure frameworks.

The paper tackles the problem of designing recommendation systems under wireless bandwidth constraints, demonstrating a tight trade-off between regret and bandwidth for contextual multi-armed bandits and latent structure scenarios.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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