48 citations · 50 across the 7 of their papers we have counts for
7 papers
Personalized Turn-Level User Conversation Satisfaction Benchmark
Zhefan Wang, Zhiqiang Guo, Weizhi Ma +3
User satisfaction with AI assistants is highly personalized: the same response may satisfy one user but disappoint another depending on what each user expects and what they have as…
Generative Sequential Recommendation via Hierarchical Behavior Modeling
Zhefan Wang, Guokai Yan, Jinbei Yu +5
Recommender systems in multi-behavior domains, such as advertising and e-commerce, aim to guide users toward high-value but inherently sparse conversions. Leveraging auxiliary beha…
Human vs. Agent in Task-Oriented Conversations
Zhefan Wang, Ning Geng, Zhiqiang Guo +2
Task-oriented conversational systems are essential for efficiently addressing diverse user needs, yet their development requires substantial amounts of high-quality conversational…
StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning
Yuanqing Yu, Zhefan Wang, Weizhi Ma +4
Despite their powerful text generation capabilities, large language models (LLMs) still struggle to effectively utilize external tools to solve complex tasks, a challenge known as…
To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models
Zhefan Wang, Weizhi Ma, Min Zhang
Most current recommender systems primarily focus on what to recommend, assuming users always require personalized recommendations. However, with the widely spread of ChatGPT and ot…
MACRec: a Multi-Agent Collaboration Framework for Recommendation
Zhefan Wang, Yuanqing Yu, Wendi Zheng +2
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabiliti…
Intent-aware Ranking Ensemble for Personalized Recommendation
Jiayu Li, Peijie Sun, Zhefan Wang +5
Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a s…