pith:6MCM3MHU
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
MRet maximizes retention in two-sided matching by learning personalized curves that predict how recommendations affect each user's decision to stay.
arxiv:2602.15752 v3 · 2026-02-17 · cs.LG
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\pithnumber{6MCM3MHUO3GROXHXXRNI524NAK}
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Claims
empirical evaluations on synthetic and real-world datasets from a major online dating platform show that MRet achieves higher user retention, since conventional methods optimize matches or fairness rather than retention.
that personalized retention curves learned from each user's profile and interaction history accurately capture how future recommendations will affect that user's decision to stay or leave, and that the joint optimization over both sides correctly allocates limited matches to maximize aggregate retention.
MRet is a dynamic LTR algorithm that maximizes platform-wide user retention in two-sided matching by jointly optimizing retention gains for both the recipient and the recommended users via learned personalized retention curves.
Receipt and verification
| First computed | 2026-05-20T01:05:09.431764Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f304cdb0f476cd175cf7bc5a8eeb8d029df68ba4ae3b8d9687504058952cd31a
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/6MCM3MHUO3GROXHXXRNI524NAK \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f304cdb0f476cd175cf7bc5a8eeb8d029df68ba4ae3b8d9687504058952cd31a
Canonical record JSON
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