5 papers · 1 filter
SORT: A Systematically Optimized Ranking Transformer for Industrial-scale Recommenders
Chunqi Wang, Bingchao Wu, Taotian Pang +9
While Transformers have achieved remarkable success in LLMs through superior scalability, their application in industrial-scale ranking models remains nascent, hindered by the chal…
MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling
Bin Wu, Feifan Yang, Zhangming Chan +8
Lifelong user interest modeling is crucial for industrial recommender systems, yet existing approaches rely predominantly on ID-based features, suffering from poor generalization o…
LLM-CoT Enhanced Graph Neural Recommendation with Harmonized Group Policy Optimization
Hailong Luo, Bin Wu, Hongyong Jia +2
Graph neural networks (GNNs) have advanced recommender systems by modeling interaction relationships. However, existing graph-based recommenders rely on sparse ID features and do n…
Scaling Transformers for Discriminative Recommendation via Generative Pretraining
Chunqi Wang, Bingchao Wu, Zheng Chen +3
Discriminative recommendation tasks, such as CTR (click-through rate) and CVR (conversion rate) prediction, play critical roles in the ranking stage of large-scale industrial recom…
Graph Foundation Models for Recommendation: A Comprehensive Survey
Bin Wu, Yihang Wang, Yuanhao Zeng +7
Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role i…