Research map / Learning theory and algorithms / Prediction and decision losses
Online Reciprocal Recommendation with Theoretical Performance Guarantees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use i-SMILE's cluster-based matching instead of OOMM | Reciprocal recommendation · Synthetic and real-world online dating datasets | discovered matches | Helped |
| Use i-SMILE's cluster-based matching instead of UROMM | Reciprocal recommendation · Synthetic and real-world online dating datasets | discovered matches | Helped |
| Exploit estimated preference clusters under the paper's clusterability assumptions | SMILE · Clusterable preference graphs | matches uncovered: M_T(\textsc{smile})=\Theta(M) | Helped |
| Use OOMM's reciprocal-edge sampling without structural assumptions | Online reciprocal recommendation · Adversarial preference graphs | expected matches uncovered: \mathcal{O}\left(\frac{T}{n^2}M\right) | No clear effect |
| Interleave cluster exploration with match exploitation | SMILE · Synthetic and real-world online dating datasets | discovered matches over time | Helped |
| Prioritize exploring reciprocal feedback after a discovered like, rather than treating likes and dislikes alike (ablation) | SMILE implementation · Synthetic and real-world online dating datasets | discovered matches | Helped |
| Allow a radius of a 1/log(n) fraction when testing whether users belong to the same cluster (ablation) | SMILE implementation · Synthetic and real-world online dating datasets | discovered matches | Mixed |
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