6 papers
KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation
Jiangxia Cao, Hao Peng, Wenlong Xu +97
Generative recommendation, has been attracted a surge of attentions in industrial and academic research community, towards to build more smart system to build next-generation recom…
VirtualMLE: A Virtual ML Engineer that Optimizes Sequential Recommenders
Shiteng Cao, Jingwen Liu, Junda She +1
Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning, reflection, and tool utilization, unlocking new paradigms for automating…
TwiSTAR:Think Fast, Think Slow, Then Act,Generative Recommendation with Adaptive Reasoning
Shiteng Cao, Kaian Jiang, Yunlong Gong +1
Generative recommendation with Semantic IDs (SIDs) has emerged as a promising paradigm, yet existing methods apply a fixed inference strategy, either fast direct generation or slow…
Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Model
Shiteng Cao, Xiaochong Lan, Yuwei Du +4
Local life service recommendation is distinct from general recommendation scenarios due to its strong living need-driven nature. Fundamentally, accurately identifying a user's imme…
Coarse-to-Fine Long-term Interest Modeling for Generative Recommendation
Shiteng Cao, Junda She, Bin Zeng +9
Leveraging long-term user behavioral patterns is a key trajectory for enhancing the accuracy of modern recommender systems. While generative recommender systems have emerged as a t…
The Thinking Spectrum: An Empirical Study of Tunable Reasoning in LLMs through Model Merging
Xiaochong Lan, Yu Zheng, Shiteng Cao +1
The growing demand for large language models (LLMs) with tunable reasoning capabilities in many real-world applications highlights a critical need for methods that can efficiently…