4 papers
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…
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-…
Large Reasoning Embedding Models: Towards Next-Generation Dense Retrieval Paradigm
Jianting Tang, Dongshuai Li, Tao Wen +3
In modern e-commerce search systems, dense retrieval has become an indispensable component. By computing similarities between query and item (product) embeddings, it efficiently se…
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation
Jia-Qi Yang, Chenglei Dai, Dan OU +5
With the advancement of multimedia internet, the impact of visual characteristics on the decision of users to click or not within the online retail industry is increasingly signifi…