2 papers
cs.IR2026
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…
cs.IR2026
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…