Pith Number
pith:3TSI63BZ
pith:2020:3TSI63BZCJ3IYX2ONAG56WUQOE
not attested
not anchored
not stored
refs pending
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
arxiv:2012.02476 v1 · 2020-12-04 · cs.LG · cs.AI · cs.IR
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{3TSI63BZCJ3IYX2ONAG56WUQOE}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Cited by
Receipt and verification
| First computed | 2026-07-05T01:56:59.995481Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
dce48f6c3912768c5f4e680ddf5a90711bc145611744fa0990aca686b2a531fc
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3TSI63BZCJ3IYX2ONAG56WUQOE \
| 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: dce48f6c3912768c5f4e680ddf5a90711bc145611744fa0990aca686b2a531fc
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "307d3b2291603c8eb6247f1bc2645e64aea5cc84088efdd23ddc80723754d72b",
"cross_cats_sorted": [
"cs.AI",
"cs.IR"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2020-12-04T08:58:35Z",
"title_canon_sha256": "9a04cbb4dfb7dd1b1e0cea19634f1f87ad75a86e6079637eae5a66fa5cd6fe55"
},
"schema_version": "1.0",
"source": {
"id": "2012.02476",
"kind": "arxiv",
"version": 1
}
}