6 papers
SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation
Yu Cui, Yi Xu, Jiahao Wang +6
Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism to recommendation often yields…
Taming the Long Tail: Denoising Collaborative Information for Robust Semantic ID Generation
Yi Xu, Moyu Zhang, Chaofan Fan +5
Item IDs form the backbone of industrial recommender systems, but suffer from representation instability and poor long-tail generalization in large, dynamic item corpora. Semantic…
REG4Rec: Reasoning-Enhanced Generative Model for Large-Scale Recommendation Systems
Haibo Xing, Hao Deng, Yucheng Mao +9
Sequential recommendation aims to predict a user's next action in large-scale recommender systems. While traditional methods often suffer from insufficient information interaction,…
STORE: Semantic Tokenization, Orthogonal Rotation and Efficient Attention for Scaling Up Ranking Models
Yi Xu, Chaofan Fan, Jinxin Hu +3
Ranking models have become an important part of modern personalized recommendation systems. However, significant challenges persist in handling high-cardinality, heterogeneous, and…
MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation
Yi Xu, Moyu Zhang, Chenxuan Li +7
Recommender systems traditionally represent items using unique identifiers (ItemIDs), but this approach struggles with large, dynamic item corpora and sparse long-tail data, limiti…
Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction
Yi Xu, Zhiyuan Lu, Xiaochen Li +5
The Transformer has proven to be a significant approach in feature interaction for CTR prediction, achieving considerable success in previous works. However, it also presents poten…