4 papers
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…
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…
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…
SIDE: Semantic ID Embedding for effective learning from sequences
Dinesh Ramasamy, Shakti Kumar, Chris Cadonic +4
Sequence-based recommendations models are driving the state-of-the-art for industrial ad-recommendation systems. Such systems typically deal with user histories or sequence lengths…