4 papers
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…
Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework
Yujie Xing, Xiao Wang, Bin Wu +2
Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning due to their ability to model diverse node interactions. However, existing GTs often…
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…
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…