6 citations · 14 across the 16 of their papers we have counts for
18 papers
Progressive Learning from Content to Relevance to Personalization for CTR Prediction
Zhiguang Yang, Yulin Yang, Fuyu Lv +1
Multimodal click-through rate (CTR) prediction is central to search systems, where ranking depends on item content, query relevance, and user preference. Existing approaches incorp…
Sep-Latent: Separator Tokens as Natural Compression Anchors for Implicit Reasoning
Yinlong Xu, Jianfeng Huang, Zhengdong Zhou +5
Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning through explicit Chain-of-Thought (CoT), but the associated computational overhead and token…
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…
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…
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…
HHFT: Hierarchical Heterogeneous Feature Transformer for Recommendation Systems
Liren Yu, Wenming Zhang, Silu Zhou +3
We propose HHFT (Hierarchical Heterogeneous Feature Transformer), a Transformer-based architecture tailored for industrial CTR prediction. HHFT addresses the limitations of DNN thr…
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…
Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce
Zhe Lin, Jiwei Tan, Dan Ou +3
Text relevance or text matching of query and product is an essential technique for the e-commerce search system to ensure that the displayed products can match the intent of the qu…
Large Language Model based Long-tail Query Rewriting in Taobao Search
Wenjun Peng, Guiyang Li, Yue Jiang +6
In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue. Along this line, que…
Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System
Zhixuan Zhang, Yuheng Huang, Dan Ou +4
E-commerce search systems such as Taobao Search, the largest e-commerce searching system in China, aim at providing users with the most preferred items (e.g., products). Due to the…