2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
Inverse Learning with Extremely Sparse Feedback for Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +8
Modern personalized recommendation services often rely on user feedback, either explicit or implicit, to improve the quality of services. Explicit feedback refers to behaviors like…
Mixed Attention Network for Cross-domain Sequential Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +8
In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, es…