1 citations · 1 across the 10 of their papers we have counts for
10 papers
A Better Spur Should Start From Each Objective
Shanwen Mao, Hao Zhang, Guangtao nie +4
Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing traditional linear scalariza…
CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation
Xiang Liu, Jingsong Su, Shuqi Zhao +7
Semantic-ID-based generative recommendation represents each item as a hierarchical discrete token sequence and reformulates next-item prediction as constrained sequence generation.…
HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation
Shuqi Zhao, Jingsong Su, Xiang Liu +9
Generative recommendation models that formulate the task as sequence generation overcome the objective fragmentation problem of traditional cascade architectures, yet existing appr…
Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer
Huimu Wang, Xingzhi Yao, Yiming Qiu +6
While semantic ID-based generative retrieval enables efficient end-to-end modeling in industrial applications, these methods face a persistent trade-off. On one hand, data-rich hea…
RAD-DPO: Robust Adaptive Denoising Direct Preference Optimization for Generative Retrieval in E-commerce
Zhiguo Chen, Guohao Sun, Yiming Qiu +6
Generative Retrieval (GR) is rapidly transforming e-commerce search by replacing traditional multi-stage pipelines with the autoregressive decoding of structured Semantic IDs (SIDs…
A Simple and Effective Framework for Symmetric Consistent Indexing in Large-Scale Dense Retrieval
Huimu Wang, Yiming Qiu, Xingzhi Yao +5
Dense retrieval has become the industry standard in large-scale information retrieval systems due to its high efficiency and competitive accuracy. Its core relies on a coarse-to-fi…
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search
Peng Cheng, Huimu Wang, Jinyuan Zhao +8
Traffic allocation is a process of redistributing natural traffic to products by adjusting their positions in the post-search phase, aimed at effectively fostering merchant growth,…
Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval
Zhirui Kuai, Zuxu Chen, Huimu Wang +11
Generative retrieval (GR) has emerged as a transformative paradigm in search and recommender systems, leveraging numeric-based identifier representations to enhance efficiency and…
Generative Retrieval with Preference Optimization for E-commerce Search
Mingming Li, Huimu Wang, Zuxu Chen +5
Generative retrieval introduces a groundbreaking paradigm to document retrieval by directly generating the identifier of a pertinent document in response to a specific query. This…
A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search
Huimu Wang, Mingming Li, Dadong Miao +5
Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enh…