1 citations · 2 across the 13 of their papers we have counts for
10 papers · 1 filter
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…
ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
Dakai Zhai, Jiong Gao, Boya Du +4
Accurately predicting conversion rates (CVR) for low-activity users remains a fundamental challenge in large-scale e-commerce recommender systems. Existing approaches face three cr…
SaviorRec: Semantic-Behavior Alignment for Cold-Start Recommendation
Yining Yao, Ziwei Li, Shuwen Xiao +5
In recommendation systems, predicting Click-Through Rate (CTR) is crucial for accurately matching users with items. To improve recommendation performance for cold-start and long-ta…
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…
AliBoost: Ecological Boosting Framework in Alibaba Platform
Qijie Shen, Yuanchen Bei, Zihong Huang +8
Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. Th…