46 citations · 48 across the 19 of their papers we have counts for
19 papers
SPARC: Sequence-aware Progressive Attribute Routing and Compression Framework for Generative Recommendation
Chang Liu, Changfa Wu, Hui Qian +5
Generative recommendation tokenizes items as discrete Semantic IDs (SIDs) and autoregressively generates target items from users' historical SID sequences. Although existing SIDs i…
Beyond Semantic IDs: Encoding Business-Value Ranking into Document Identifiers for Generative Retrieval
Gui Ling, Zhihong Chen, Yu Li +7
Generative Retrieval (GR) formulates retrieval as a sequence-to-sequence generation task, assigning each document a document identifier (DocID) and retrieving it through autoregres…
TSGR: Taobao Search Generative Retrieval
Tianyu Zhan, Gui Ling, Tong Xiong +9
Generative retrieval (GR) has demonstrated strong promise for industrial e-commerce search by training a single autoregressive model to directly generate the Semantic IDs (SIDs) of…
Prompt Generation Technical Report
Dan Ou, Gui Ling, Hao Wan +25
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing…
LoopMemGR: From Behavior Logs to Evolving Memory for Generative Recommendation
Hui Qian, Changfa Wu, Chang Liu +6
Generative recommendation formulates next-item prediction as conditional autoregressive generation over discrete Semantic IDs, enabling end-to-end recommendation over large-scale i…
BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE
Juntong Wu, Jialiang Cheng, Qishen Yin +5
Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K…