1 citations · 1 across the 18 of their papers we have counts for
18 papers
EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy
Kening Zheng, Aoying Zheng, Zhigang Chang +21
Long-form factuality verification is commonly implemented as a static decompose-search-verify pipeline, with separately prompted modules processing claims and invoking external sea…
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…
DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search
Junzhao Zhang, Tao Zhang, Liren Yu +4
Industrial e-commerce search systems ultimately aim to optimize the user-level long-term objective, such as n-day cumulative purchases or gross merchandise value (GMV) per user. Ho…
Ripple-Pivot Search: Active Parallel Decoding for Diffusion Large Language Models
Yushi Ye, Xu Chen, Haoyun Jiang +9
Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive language models, offering the potential for substantially faster inference throu…
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…
KARMA: Knowledge-Action Regularized Multimodal Alignment for Personalized Search at Taobao
Zhi Sun, Wenming Zhang, Yi Wei +4
Large Language Models (LLMs) are equipped with profound semantic knowledge, making them a natural choice for injecting semantic generalization into personalized search systems. How…
AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation
Jingcao Xu, Jianyun Zou, Renkai Yang +3
Pre-search query recommendation, widely known as HintQ on Taobao's homepage, plays a vital role in intent capture and demand discovery, yet traditional methods suffer from shallow…
UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking
Liren Yu, Caiyuan Li, Feiyi Dong +5
Recent advances in Large Language Models (LLMs) have inspired a surge of scaling research in industrial search, advertising, and recommendation systems. However, existing approache…
Synthetic Data Powers Product Retrieval for Long-tail Knowledge-Intensive Queries in E-commerce Search
Gui Ling, Weiyuan Li, Yue Jiang +6
Product retrieval is the backbone of e-commerce search: for each user query, it identifies a high-recall candidate set from billions of items, laying the foundation for high-qualit…
Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework
Ying Song, Yijing Wang, Hui Yang +12
Evaluating platform-level interventions in search-based two-sided marketplaces is fundamentally challenged by systemic effects such as spillovers and network interference. While wi…
Retrieval-GRPO: A Multi-Objective Reinforcement Learning Framework for Dense Retrieval in Taobao Search
Xingxian Liu, Dongshuai Li, Jiahui Wan +7
Dense retrieval, as the core component of e-commerce search engines, maps user queries and items into a unified semantic space through pre-trained embedding models to enable large-…
Learning to Trust: Dynamic Utilization of Retrieval-Augmented Generation for E-commerce Search Relevance
Tingqiao Xu, Shaowei Yao, Chenhe Dong +5
Accurately estimating query-item relevance is vital for e-commerce ranking and conversion. While Large Language Models (LLMs) excel at reasoning, they often lack specialized knowle…
TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance
Jianhui Yang, Yiming Jin, Pengkun Jiao +6
Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex…
SHE: Stepwise Hybrid Examination Reinforcement Learning Framework for E-commerce Search Relevance
Pengkun Jiao, Yiming Jin, Jianhui Yang +6
Query-product relevance prediction is vital for AI-driven e-commerce, yet current LLM-based approaches face a dilemma: SFT and DPO struggle with long-tail generalization due to coa…
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training fra…
TaoSR1: The Thinking Model for E-commerce Relevance Search
Chenhe Dong, Shaowei Yao, Pengkun Jiao +7
Query-product relevance prediction is a core task in e-commerce search. BERT-based models excel at semantic matching but lack complex reasoning capabilities. While Large Language M…
Harness Local Rewards for Global Benefits: Effective Text-to-Video Generation Alignment with Patch-level Reward Models
Shuting Wang, Haihong Tang, Zhicheng Dou +1
The emergence of diffusion models (DMs) has significantly improved the quality of text-to-video generation models (VGMs). However, current VGM optimization primarily emphasizes the…