1.6k citations · 1.6k across the 11 of their papers we have counts for
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Interactive Recommendation Agent with Active User Commands
Jiakai Tang, Yujie Luo, Xunke Xi +12
Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to captu…
RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
Luyu Chen, Quanyu Dai, Zeyu Zhang +6
Conversational recommender systems (CRS) enhance user experience through multi-turn interactions, yet evaluating CRS remains challenging. User simulators can provide comprehensive…
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent
Xueyang Feng, Jingsen Zhang, Jiakai Tang +6
Recent advancements in Large Language Models (LLMs) have significantly propelled the development of Conversational Recommendation Agents (CRAs). However, these agents often generat…
Explainable Recommendation with Simulated Human Feedback
Jiakai Tang, Jingsen Zhang, Zihang Tian +3
Recent advancements in explainable recommendation have greatly bolstered user experience by elucidating the decision-making rationale. However, the existing methods actually fail t…
Improving Retrospective Language Agents via Joint Policy Gradient Optimization
Xueyang Feng, Bo Lan, Quanyu Dai +5
In recent research advancements within the community, large language models (LLMs) have sparked great interest in creating autonomous agents. However, current prompt-based agents o…
Enhancing Recommendation Explanations through User-Centric Refinement
Jingsen Zhang, Zihang Tian, Xueyang Feng +1
Generating natural language explanations for recommendations has become increasingly important in recommender systems. Traditional approaches typically treat user reviews as ground…