7 papers · 1 filter
Residual Trajectory Distillation for Generative Retrieval
Weihao Shen, Wei Chen, Fuwei Zhang +4
Generative retrieval has emerged as a general retrieval paradigm, representing items with discrete Semantic IDs (SIDs) and retrieving them through autoregressive identifier generat…
Learning Multiresolution Relevance for Hierarchical Generative Retrieval
Weihao Shen, Wei Chen, Fuwei Zhang +4
Generative retrieval with semantic identifiers (SIDs) makes successive decisions over a document hierarchy. Relevant documents for the same query may share coarse prefixes and dive…
Requirement--Evidence Alignment for Compositional E-Commerce Queries
Weihao Shen, Wei Chen, Fuwei Zhang +6
Compositional e-commerce queries express multiple requirements that must hold jointly, yet existing rerankers collapse these constraints into aggregate relevance and often promote…
Unpaired Modality-Agnostic Generative Recommendation
Weihao Shen, Wei Chen, Fuwei Zhang +6
Generative Recommendation (GR) formulates recommendation as autoregressive generation over discrete semantic identifiers (IDs). Although recent multimodal GR methods improve semant…
SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
Wei Chen, Xingyu Guo, Shuang Li +6
Generative Recommendation (GR) has emerged as a promising paradigm by formulating item recommendation as a sequence-to-sequence generation task over item identifiers. Recent studie…
DUET: Dual Model Co-Training for Entire Space CTR Prediction
Yutian Xiao, Meng Yuan, Fuzhen Zhuang +9
The pre-ranking stage plays a pivotal role in large-scale recommender systems but faces an intrinsic trade-off between model expressiveness and computational efficiency. Owing to t…
Hyperbolic Diffusion Recommender Model
Meng Yuan, Yutian Xiao, Wei Chen +3
Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender sy…