Publications (6)
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search
Longbin Li, Chao Zhang, Sen Li +3
In e-commerce search, personalized retrieval is a crucial technique for improving user shopping experience. Recent works in this domain have achieved significant improvements by th…
ReaSeq: Unleashing World Knowledge via Reasoning for Sequential Modeling
Jiakai Tang, Chuan Wang, Gaoming Yang +31
Industrial recommender systems face two fundamental limitations under the log-driven paradigm: (1) knowledge poverty in ID-based item representations that causes brittle interest m…
RecGPT Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +51
Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, mo…
Multi-Behavior Sequential Modeling with Transition-Aware Graph Attention Network for E-Commerce Recommendation
Hanqi Jin, Gaoming Yang, Zhangming Chan +7
User interactions on e-commerce platforms are inherently diverse, involving behaviors such as clicking, favoriting, adding to cart, and purchasing. The transitions between these be…
Simple but Efficient: A Multi-Scenario Nearline Retrieval Framework for Recommendation on Taobao
Yingcai Ma, Ziyang Wang, Yuliang Yan +5
In recommendation systems, the matching stage is becoming increasingly critical, serving as the upper limit for the entire recommendation process. Recently, some studies have start…
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…