10 citations · 16 across the 8 of their papers we have counts for
9 papers
OxygenREC-v2: Internalizing Discrimination into Generative Recommendation
Guo Tang, Hanye Wu, Changjiang Han +9
Generative recommendation unifies retrieval and ranking within a single model by autoregressively decoding semantic identifier (SID) sequences. Yet reliably incorporating behavior…
Relevance Matters: A Multi-Task and Multi-Stage Large Language Model Approach for E-commerce Query Rewriting
Aijun Dai, Jixiang Zhang, Haiqing Hu +3
For e-commerce search, user experience is measured by users' behavioral responses to returned products, like click-through rate and conversion rate, as well as the relevance betwee…
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…
LREF: A Novel LLM-based Relevance Framework for E-commerce
Tian Tang, Zhixing Tian, Zhenyu Zhu +5
Query and product relevance prediction is a critical component for ensuring a smooth user experience in e-commerce search. Traditional studies mainly focus on BERT-based models to…
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…
Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model
Enqiang Xu, Yiming Qiu, Junyang Bai +6
In large e-commerce platforms, search systems are typically composed of a series of modules, including recall, pre-ranking, and ranking phases. The pre-ranking phase, serving as a…