Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning
In search and recommendation systems, predictive models often suffer from temporal instability when certain input features introduce volatility in output scores. This instability can degrade model reliability and user experience especially in multi-stage systems where consistent predictions are critical for downstream decision making. We introduce Fortress, a general framework for enhancing model stability and accuracy by identifying and pruning features that contribute to inconsistent prediction scores over time. Fortress leverages historical snapshots temporally partitioned datasets capturing score fluctuations for the same entity across periods and follows a four-step process: (1) coll...
| Authors | Milind Pandurang Jagre, Jia Huang, Dayvid V. R. Oliveira et al. |
|---|---|
| Primary category | cs.IR |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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