1 citations · 2 across the 13 of their papers we have counts for
6 papers · 2 filters
RecGPT-Mobile-V2 Technical Report
Lingqing Zhang, Bin Zhang, Weipeng Huang +25
Personalized Query prediction maps implicit behavioral signals---clicks, favorites, purchases, and post-purchase exploration---to explicit retrieval intent. On-device deployment ma…
PILOT Technical Report
Jiuning Lin, Ruiquan Lan, Xiaodong Zhu +17
Existing agentic approaches for recommendation system optimization remain fundamentally reactive: they adjust parameters in response to observed metric changes but lack the ability…
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
MetaStrategy: Generative Ranking with Executable LLM Strategies
Chengyu Lai, Jiuning Lin, Zhibo Xiao +12
Industrial recommender systems rank heterogeneous content under coupled user, business, commercial, and experience objectives. Existing generative ranking methods typically constru…
RecGPT-Mobile: On-Device Large Language Models for User Intent Understanding in Taobao Feed Recommendation
Bin Zhang, Weipeng Huang, Dimin Wang +9
Predicting a user's next search query from recent interaction behaviors is a critical problem in modern e-commerce systems, particularly in scenarios where user intent evolves rapi…
CoNRec: Context-Discerning Negative Recommendation with LLMs
Xinda Chen, Jiawei Wu, Yishuang Liu +5
Understanding what users like is relatively straightforward; understanding what users dislike, however, remains a challenging and underexplored problem. Research into users' negati…