37 citations · 120 across the 24 of their papers we have counts for
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cs.IR2026
LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation
Egemen Erbayat, Luis Duque, Sohini Roychowdhury +2
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 e…
cs.IR2026★ 1 cited
COFFEE: COdesign Framework for Feature Enriched Embeddings in Ads-Ranking Systems
Sohini Roychowdhury, Doris Wang, Qian Ge +2
Diverse and enriched data sources are essential for commercial ads-recommendation models to accurately assess user interest both before and after engagement with content. While ext…