3 papers
cs.IR2026
UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
Rongcheng Lin, Yan Sun, Jamey Zhang +4
Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models ov…
cs.IR2025
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Mingfu Liang, Xi Liu, Rong Jin +104
Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommenda…
cs.IR2023
AutoML for Large Capacity Modeling of Meta's Ranking Systems
Hang Yin, Kuang-Hung Liu, Mengying Sun +16
Web-scale ranking systems at Meta serving billions of users is complex. Improving ranking models is essential but engineering heavy. Automated Machine Learning (AutoML) can release…