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pith:NKFJJQR3

pith:2026:NKFJJQR3XNORXBHAWAAH3TA7NL

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UniER: A Unified Benchmark for Item-level and Path-level Exercise Recommendation

Guiyong Zhuang, Jiapu Wang, Liangda Fang, Quanlong Guan, Shirui Pan, Xinghe Cheng, Yixin Liu, Yusheng Xie

A unified benchmark shows path-level exercise recommendation consistently outperforms item-level methods across effectiveness, robustness, and sparse data conditions.

arxiv:2605.16750 v1 · 2026-05-16 · cs.IR · cs.AI

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Claims

C1strongest claim

Through multi-dimensional analyses covering effectiveness, generalizability, robustness, and efficiency, our results reveal the systematic dominance of PLER and expose the pedagogical failure of ILER's fragmented recommendations under extreme sparsity and noise.

C2weakest assumption

The four dataset generation methods produce data that faithfully reflects real student learning dynamics and that the Weighted Cognitive Gain metric correctly measures cumulative learning benefit across both single-step and multi-step recommendation paradigms.

C3one line summary

UniER unifies ILER and PLER with the WCG metric across 9 datasets and 18 methods, showing systematic PLER superiority especially under sparsity and noise.

References

38 extracted · 38 resolved · 0 Pith anchors

[1] NR4DER: Neural Re- ranking for Diversified Exercise Recommendation, 2025

[2] Exploiting Cognitive Structure for Adaptive Learning, 2019

[3] Reassessing the Effectiveness of Reinforcement Learning based Recom- mender Systems for Sequential Recommendation, 2025

[4] User-item fairness tradeoffs in recommendations, 2024

[5] Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation, 2024

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Receipt and verification

First computed	2026-05-20T00:03:19.699256Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6a8a94c23bbb5d1b84e0b0007dcc1f6ac727c98c219e533719ff15a6b747747d

Aliases

arxiv: 2605.16750 · arxiv_version: 2605.16750v1 · doi: 10.48550/arxiv.2605.16750 · pith_short_12: NKFJJQR3XNOR · pith_short_16: NKFJJQR3XNORXBHA · pith_short_8: NKFJJQR3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NKFJJQR3XNORXBHAWAAH3TA7NL \
  | 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: 6a8a94c23bbb5d1b84e0b0007dcc1f6ac727c98c219e533719ff15a6b747747d

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "672d31f0a103ac6a10f5af216dd7839b194e9816fbb00abd2c137bfec3b4002a",
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      "cs.AI"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.IR",
    "submitted_at": "2026-05-16T02:07:58Z",
    "title_canon_sha256": "4e5a0ed3bacc77a2a2e5101d5c928d18fbcd462c14c0521ab369d19d5a251215"
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