LO-FAR ranks sparse ID-list features by stand-alone held-out predictive signal and reports downstream NE gains competitive with shuffle importance and BSN at 100–400 retained features in about two CPU-hours.
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LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation
LO-FAR ranks sparse ID-list features by stand-alone held-out predictive signal and reports downstream NE gains competitive with shuffle importance and BSN at 100–400 retained features in about two CPU-hours.