4 papers
OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval
Ziwei Li, Shuyao Li, Xufeng Cai +12
In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking. To make this…
MDL: A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization
Shanlei Mu, Yuchen Jiang, Shikang Wu +7
Industrial recommender systems increasingly adopt multi-scenario learning (MSL) and multi-task learning (MTL) to handle diverse user interactions and contexts, but existing approac…
MSN: A Memory-based Sparse Activation Scaling Framework for Large-scale Industrial Recommendation
Shikang Wu, Hui Lu, Jinqiu Jin +9
Scaling deep learning recommendation models is an effective way to improve model expressiveness. Existing approaches often incur substantial computational overhead, making them dif…
HyFormer: Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction
Yunwen Huang, Shiyong Hong, Xijun Xiao +7
Industrial large-scale recommendation models (LRMs) face the challenge of jointly modeling long-range user behavior sequences and heterogeneous non-sequential features under strict…