3 citations · 5 across the 5 of their papers we have counts for
6 papers
Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval
Gaoming Zhang, Angqing Jiang, Jianchun Song +4
Generative Retrieval (GR) has emerged as a promising paradigm by mapping queries directly to Semantic IDs (SIDs) with powerful representation capabilities for candidate items. Howe…
Think-to-Personalize: Unifying Reasoning and Retrieval for User-Centric Personalized Dense Retrieval
Angqing Jiang, Gaoming Zhang, Jianchun Song +4
Dense retrieval has become a cornerstone of modern local-lifestyle e-commerce search by encoding queries and items into semantic embedding spaces. While recent advancements have tr…
SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search
Wen Zan, Yaopeng Han, Xiaotian Jiang +4
In e-commerce search, relevance between query and documents is an essential requirement for satisfying user experience. Different from traditional e-commerce platforms that offer p…
Contrastive Information Transfer for Pre-Ranking Systems
Yue Cao, XiaoJiang Zhou, Peihao Huang +3
Real-word search and recommender systems usually adopt a multi-stage ranking architecture, including matching, pre-ranking, ranking, and re-ranking. Previous works mainly focus on…
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System
Xiang Li, Xiaojiang Zhou, Yao Xiao +4
Industrial search and recommendation systems mostly follow the classic multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and re-ranking stages. To account…
Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction
Yue Cao, XiaoJiang Zhou, Jiaqi Feng +4
Rich user behavior data has been proven to be of great value for Click-Through Rate (CTR) prediction applications, especially in industrial recommender, search, or advertising syst…