pith:QP225GXE
Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation
ContextSim anchors LLM agents in daily life scenarios to simulate contextual user interactions for more reliable recommender evaluation.
arxiv:2604.09549 v2 · 2026-01-26 · cs.IR · cs.AI
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\pithnumber{QP225GXENRVBVBJGUHQFXJNEWS}
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Claims
Experiments across domains show our method generates interactions more closely aligned with human behavior than prior work. We further validate our approach through offline A/B testing correlation and show that RS parameters optimized using ContextSim yield improved real-world engagement.
That LLM agents with generated life scenarios and enforced consistency at action and trajectory levels accurately capture the contextual factors shaping genuine human decision-making.
ContextSim generates more human-aligned user interactions for recommender systems via context-aware life simulation and consistency enforcement, yielding parameters that improve real-world engagement.
Formal links
Receipt and verification
| First computed | 2026-06-02T02:04:52.938344Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/QP225GXENRVBVBJGUHQFXJNEWS \
| 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: 83f5ae9ae46c6a1a8526a1e05ba5a4b49fd4e123a6186430b70a105de0e0083c
Canonical record JSON
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