Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning
The paper introduces Fortress, a framework designed to stabilize search recommendations by addressing temporal instability in predictive models. It focuses on identifying and pruning features that cause inconsistent predictions, thereby enhancing model reliability. Fortress has been validated through experiments, showing significant improvements in prediction stability and classification performance.
- ▪Fortress aims to enhance model stability and accuracy in search and recommendation systems.
- ▪The framework follows a four-step process to prune instability-inducing features and retrain models.
- ▪Experiments demonstrate notable improvements in prediction stability and classification performance.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15299 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | FGEYtKpxU_1m |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Information Retrieval arXiv:2605.15299 (cs) [Submitted on 14 May 2026] Title:Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning Authors:Milind Pandurang Jagre, Jia Huang, Dayvid V. R. Oliveira, Zhinan Cheng, Babak Seyed Aghazadeh, Puja Das, Chris Alvino, Jinda Han, Kailash Thiyagarajan View a PDF of the paper titled Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning, by Milind Pandurang Jagre and 8 other authors View PDF HTML (experimental) Abstract:In search and recommendation systems, predictive models often suffer from temporal instability when certain input features introduce volatility in output scores.
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