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pith:ETKKTK4X

pith:2026:ETKKTK4XWBUMLXKGUEGW4YHMPA

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Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning

Babak Seyed Aghazadeh, Chris Alvino, Dayvid V. R. Oliveira, Jia Huang, Jinda Han, Kailash Thiyagarajan, Milind Pandurang Jagre, Puja Das, Zhinan Cheng

Fortress stabilizes search recommendation models by pruning features that introduce temporal volatility in prediction scores.

arxiv:2605.15299 v1 · 2026-05-14 · cs.IR · cs.AI

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4 Citations open

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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.

Claims

C1strongest claim

Fortress mitigates this trade-off by suppressing the volatility of engagement signals while retaining their predictive value leading to more stable and accurate models.

C2weakest assumption

That the features identified as instability-inducing in the historical snapshots are causally responsible for temporal score fluctuations and that their removal will not materially harm generalization on future data.

C3one line summary

Fortress stabilizes query-to-app relevance models by pruning features that cause inconsistent predictions across time periods while retaining predictive power from engagement signals.

References

20 extracted · 20 resolved · 1 Pith anchors

[1] Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794 2016

[2] Wenqi Fan. 2024. Recommender systems in the era of large language models (llms). IEEE Transactions on Knowledge and Data Engineering (2024), 1–20 2024

[3] Apple intelligence foundation language models 2024

[4] Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024. Large language models are zero- shot rankers for recommender systems. In European Conference on I 2024

[5] Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:51.414684Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

24d4a9ab97b068c5dd46a10d6e60ec782d7b9abc82b0f475efdd4a0021259fb9

Aliases

arxiv: 2605.15299 · arxiv_version: 2605.15299v1 · doi: 10.48550/arxiv.2605.15299 · pith_short_12: ETKKTK4XWBUM · pith_short_16: ETKKTK4XWBUMLXKG · pith_short_8: ETKKTK4X

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ETKKTK4XWBUMLXKGUEGW4YHMPA \
  | 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: 24d4a9ab97b068c5dd46a10d6e60ec782d7b9abc82b0f475efdd4a0021259fb9

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "2f727eb2536d89d647d63ff22883b9a3e26440dd7ab6992f2e6399710e161247",
    "cross_cats_sorted": [
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.IR",
    "submitted_at": "2026-05-14T18:13:05Z",
    "title_canon_sha256": "567150ce3402cc1d15ab72a9c11de8acdd6d6fb86f148780a78b5469e6452bd4"
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