978 citations · 1.3k across the 23 of their papers we have counts for
17 papers · 1 filter
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.…
Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents
Jiwen Zhou, Xiang Liu, Mingming Li +5
Recommender systems operate as Black-Boxes, leaving users and regulators unable to steer their outputs toward specific intentions or audit their behavior. This lack of controllabil…
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…
BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation
Xi Zhou, Famin Wu, Mingming Li +4
Sequential recommendation systems are widely adopted but often deployed as black-box APIs, which has driven recent interest in model extraction to replicate their capabilities loca…
RecBundle: A Next-Generation Geometric Paradigm for Explainable Recommender Systems
Hui Wang, Tianzhu Hu, Mingming Li +6
Recommender systems are inherently dynamic feedback loops where prolonged local interactions accumulate into macroscopic structural degradation such as information cocoons. Existin…
A Cognitive Distribution and Behavior-Consistent Framework for Black-Box Attacks on Recommender Systems
Hongyue Zhang, Mingming Li, Dongqin Liu +6
With the growing deployment of sequential recommender systems in e-commerce and other fields, their black-box interfaces raise security concerns: models are vulnerable to extractio…
Boundary-Aware Multi-Behavior Dynamic Graph Transformer for Sequential Recommendation
Jingsong Su, Xuetao Ma, Mingming Li +2
In the landscape of contemporary recommender systems, user-item interactions are inherently dynamic and sequential, often characterized by various behaviors. Prior research has exp…
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…
pEBR: A Probabilistic Approach to Embedding Based Retrieval
Han Zhang, Yunjiang Jiang, Mingming Li +3
Embedding-based retrieval aims to learn a shared semantic representation space for both queries and items, enabling efficient and effective item retrieval through approximate neare…
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…
Semantic-Enhanced Relational Metric Learning for Recommender Systems
Mingming Li, Fuqing Zhu, Feng Yuan +1
Recently, relational metric learning methods have been received great attention in recommendation community, which is inspired by the translation mechanism in knowledge graph. Diff…
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…
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval
Chunyuan Yuan, Yiming Qiu, Mingming Li +3
Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classific…
Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search
Binbin Wang, Mingming Li, Zhixiong Zeng +5
Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) metho…