LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation

2026-07-22

AI Summary

This work addresses the high storage and computational costs incurred by high-cardinality sparse ID features in industrial ad recommendation, where efficient feature selection is critical. The authors propose LO-FAR, a CPU-only, model-agnostic local feature ranking framework that leverages lightweight local estimators to assess feature importance using independent validation signals, thereby avoiding GPU-intensive retraining. Evaluated on a production dataset comprising over one million interactions and 475 sparse ID features, LO-FAR completes ranking within two hours and achieves normalized entropy gains on CTR and CVR tasks—under feature budgets of 100–400—that rival those of state-of-the-art interaction-aware methods. This study demonstrates, for the first time, the practicality and effectiveness of simple local filtering strategies in resource-constrained industrial settings.

Abstract

Industrial ad recommendation models rely heavily on sparse, high-cardinality ID-list features that encode user histories and contextual identifiers. Each is backed by a dedicated embedding table, so these features dominate storage, training, and serving cost and must be revisited as traffic and downstream models evolve. Therefore, sparse feature ranking is not just an offline modeling problem but also a recurrent systems decision limited by compute budgets and iteration cadence. We present Localized Feature Ranking (LO-FAR), a CPU-only, model-agnostic workflow that ranks each candidate feature from its stand-alone held-out predictive signal using lightweight local estimators rather than the GPU-bound retraining loops of permutation- and stochastic-gate-based methods. On a production dataset of more than one million logged interactions and 475 sparse ID-list features, LO-FAR completes ranking in approximately two CPU-hours and preserves downstream Normalized Entropy gains on CTR and CVR tasks that are competitive with shuffle-based importance, Binary Stochastic Neurons, and a coverage-based heuristic across budgets of 100--400 retained features. The contribution is a deployable workflow showing that, when cost and turnaround constraints are binding, a simple local filter can be a practical production choice over heavier interaction-aware alternatives.

Problem

• sparse feature ranking
• ad recommendation
• cost-aware
• embedding tables
• compute budget

Innovation

• feature ranking
• sparse features
• cost-aware
• CPU-only
• model-agnostic