Research map / Learning theory and algorithms / Submodular and game algorithms
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use constrained matching that accounts for provider viability instead of myopic best-match recommendation | Recommender system · Synthetic uniform | Avg. Welfare: -2.41 ± 0.59 -> -1.23 ± 0.57 | Helped |
| Use constrained matching that accounts for provider viability instead of myopic best-match recommendation | Recommender system · Synthetic uniform | Viable Providers: 43.80 ± 1.94 -> 48.00 ± 2.10 | Helped |
| Use constrained matching that accounts for provider viability instead of myopic best-match recommendation | Recommender system · Synthetic skewed | Avg. Welfare: -5.68 ± 0.61 -> -3.40 ± 1.10 | Helped |
| Use constrained matching that accounts for provider viability instead of myopic best-match recommendation | Recommender system · Synthetic skewed | Viable Providers: 34.40 ± 1.96 -> 42.00 ± 3.35 | Helped |
| Increase discount factor from γ=0.1 to γ=1.0 (ablation) | LP-RS · Synthetic skewed | Avg. Welfare: 18.02 ± 1.05 -> 63.62 ± 3.58 | Helped |
| Increase discount factor from γ=0.1 to γ=1.0 (ablation) | LP-RS · Synthetic skewed | Max Regret: 7.24 ± 0.77 -> 37.89 ± 1.39 | Hurt |
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