pith:FLFORGOA
UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence
UxSID uses semantic IDs and dual-level attention to model ultra-long user sequences with target-aware preferences.
arxiv:2605.09040 v3 · 2026-05-09 · cs.AI · cs.IR · cs.LG
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\pithnumber{FLFORGOAYQFLR45VFX7JQXBA3Q}
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Record completeness
Claims
By utilizing Semantic IDs (SIDs) and a dual-level attention strategy, UxSID captures target-aware preferences without the heavy cost of item-specific models... achieving state-of-the-art performance and a 0.337% revenue lift in large-scale advertising A/B test.
That semantic grouping via SIDs plus dual-level attention actually preserves target-aware preferences better than item-specific search or item-agnostic compression without introducing new biases or losing critical signals in the ultra-long sequences.
UxSID introduces semantic-group shared interest memory with Semantic IDs and dual-level attention to model ultra-long user sequences, claiming state-of-the-art results and a 0.337% revenue lift in advertising A/B tests.
Receipt and verification
| First computed | 2026-05-20T00:04:35.115672Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2acae899c0c40ab8f3b52dfe985c20dc1cd5cece6267a749edce769301162556
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FLFORGOAYQFLR45VFX7JQXBA3Q \
| 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: 2acae899c0c40ab8f3b52dfe985c20dc1cd5cece6267a749edce769301162556
Canonical record JSON
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