2 papers
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.LG2025
DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations
Sohini Roychowdhury, Adam Holeman, Mohammad Amin +3
For online ad-recommendation systems, processing complete user-ad-engagement histories is both computationally intensive and noise-prone. We introduce Dynamix, a scalable, personal…