Diversified Multinomial Logit Contextual Bandits
Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of within-assortment diversity, while submodular/combinatorial bandits encode diversity in rewards but lack structured choice probabilities. We bridge this gap with the $\textit{diversified multinomial logit}$ (DMNL) contextual bandit, which augments MNL choice probabilities with a generally submodular diversity function, thereby formalizing the relevance--diversity trade-off within a single model. Incorporating diversity renders exact MNL assortment optimization intractable. We
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 46%Hyperparameter tuning approach question [R] →
- LinkedLinked via arxiv author · 85%Heesang Ann →
“Diversified Multinomial Logit Contextual Bandits”
- LinkedLinked via arxiv author · 85%Taehyun Hwang →
“Diversified Multinomial Logit Contextual Bandits”
- LinkedLinked via arxiv author · 85%Min-hwan Oh →
“Diversified Multinomial Logit Contextual Bandits”