6 citations · 14 across the 16 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…