10 citations · 10 across the 8 of their papers we have counts for
8 papers
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 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…
Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation
Jianxing Ma, Zhibo Xiao, Luwei Yang +5
To cater to users' desire for an immersive browsing experience, numerous e-commerce platforms provide various recommendation scenarios, with a focus on Trigger-Induced Recommendati…
On Practical Diversified Recommendation with Controllable Category Diversity Framework
Tao Zhang, Luwei Yang, Zhibo Xiao +2
Recommender systems have made significant strides in various industries, primarily driven by extensive efforts to enhance recommendation accuracy. However, this pursuit of accuracy…
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
Zhibo Xiao, Luwei Yang, Tao Zhang +3
The recommendation has been playing a key role in many industries, e.g., e-commerce, streaming media, social media, etc. Recently, a new recommendation scenario, called Trigger-Ind…