5 citations · 7 across the 4 of their papers we have counts for
5 papers
Masked Diffusion Generative Recommendation
Lingyu Mu, Hao Deng, Haibo Xing +4
Generative recommendation (GR) typically first quantizes continuous item embeddings into multi-level semantic IDs (SIDs), and then generates the next item via autoregressive decodi…
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…
HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation
Hao Deng, Haibo Xing, Kanefumi Matsuyama +8
Transformer-based sequential recommendation (TSR) models have shown superior performance in recommendation systems, where the quality of item representations plays a crucial role.…
CSMF: Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval
Hao Deng, Haibo Xing, Kanefumi Matsuyama +6
Multi-objective embedding-based retrieval (EBR) has become increasingly critical due to the growing complexity of user behaviors and commercial objectives. While traditional approa…
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…