519 citations · 998 across the 35 of their papers we have counts for
35 papers
LLM-Based User Personas for Recommendations at Scale
Haoting Wang, Haokai Lu, Zheyun Feng +14
Large Language Models (LLMs) offer unprecedented potential for enhancing recommendation systems through their world knowledge and reasoning capabilities. However, existing approach…
Serendipitous Recommendation with Multimodal LLM
Haoting Wang, Jianling Wang, Hao Li +9
Conventional recommendation systems succeed in identifying relevant content but often fail to provide users with surprising or novel items. Multimodal Large Language Models (MLLMs)…
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems
Jianling Wang, Yifan Liu, Yinghao Sun +11
Exploration, the act of broadening user experiences beyond their established preferences, is challenging in large-scale recommendation systems due to feedback loops and limited sig…
Conversational Planning for Personal Plans
Konstantina Christakopoulou, Iris Qu, John Canny +4
The language generation and reasoning capabilities of large language models (LLMs) have enabled conversational systems with impressive performance in a variety of tasks, from code…
Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations
Yuyan Wang, Pan Li, Minmin Chen
Recommender systems are central to digital platforms, yet they face a fundamental trade-off between accuracy and explainability. Black-box models achieve strong performance but lac…
EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration
Allen Nie, Yi Su, Bo Chang +4
Despite their success in many domains, large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. This is crucial as many r…