1 citations · 2 across the 19 of their papers we have counts for
9 papers · 1 filter
CADET: Context-Conditioned Ads CTR Prediction With a Decoder-Only Transformer
David Pardoe, Neil Daftary, Miro Furtado +20
Click-through rate (CTR) prediction is fundamental to online advertising systems. While Deep Learning Recommendation Models (DLRMs) with explicit feature interactions have long dom…
Generative Sequential Notification Optimization via Multi-Objective Decision Transformers
Borja Ocejo, Ruofan Wang, Ke Liu +7
Notifications are an important communication channel for delivering timely and relevant information. Optimizing their delivery involves addressing complex sequential decision-makin…
Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn
Shihai He, Julie Choi, Tianqi Li +8
Notification recommendation systems are critical to driving user engagement on professional platforms like LinkedIn. Designing such systems involves integrating heterogeneous signa…
From Features to Transformers: Redefining Ranking for Scalable Impact
Fedor Borisyuk, Lars Hertel, Ganesh Parameswaran +14
We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modifie…
Efficient user history modeling with amortized inference for deep learning recommendation models
Lars Hertel, Neil Daftary, Fedor Borisyuk +2
We study user history modeling via Transformer encoders in deep learning recommendation models (DLRM). Such architectures can significantly improve recommendation quality, but usua…
LiNR: Model Based Neural Retrieval on GPUs at LinkedIn
Fedor Borisyuk, Qingquan Song, Mingzhou Zhou +11
This paper introduces LiNR, LinkedIn's large-scale, GPU-based retrieval system. LiNR supports a billion-sized index on GPU models. We discuss our experiences and challenges in crea…