Research map / Bandits and online learning / Contextual bandits and Thompson sampling
On Context-Dependent Clustering of Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use context-dependent user neighborhoods and share feedback across users | CAB · Tuenti | CTR | Helped |
| Use context-dependent user neighborhoods and share feedback across users | CAB · KDD Cup | CTR | Helped |
| Use context-dependent user neighborhoods and share feedback across users | CAB · Avazu | CTR | Helped |
| Use context-dependent user neighborhoods and share feedback across users | CAB · LastFM | cumulative regret relative to RAN | Helped |
| Use context-dependent user neighborhoods and share feedback across users | CAB · Delicious | cumulative regret relative to RAN | Hurt |
| Restrict neighborhood candidates to users whose vectors have been updated before (ablation) | CAB · Experiments across five datasets | prediction performance | No clear effect |
| Vary the separation parameter γ (ablation) | CAB · Experiments across five datasets | prediction performance | No clear effect |
| Use contextual Thompson Sampling instead of the tested bandit algorithms | CAB and competitors · Experiments (preliminary; results not reported) | performance | No clear effect |
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