4 papers
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…
LEMUR: Large scale End-to-end MUltimodal Recommendation
Xintian Han, Honggang Chen, Quan Lin +14
Traditional ID-based recommender systems often struggle with cold-start and generalization challenges. Multimodal recommendation systems, which leverage textual and visual data, of…