AAAI 2026technical0 citations

RecToM: A Benchmark for Evaluating Machine Theory of Mind in LLM-based Conversational Recommender Systems

Mengfan Li, Xuanhua Shi, Yang Deng

Abstract

Large Language models (LLMs) are revolutionizing the conversational recommender systems (CRS) through their impressive capabilities in instruction comprehension, reasoning, and human interaction. A core factor underlying effective dialogue is the ability to infer and reason about others

BibTeX
@inproceedings{aaai2026_rectomabenchmark,
  title = {RecToM: A Benchmark for Evaluating Machine Theory of Mind in LLM-based Conversational Recommender Systems},
  author = {Mengfan Li and Xuanhua Shi and Yang Deng},
  booktitle = {AAAI 2026},
  year = {2026}
}
RecToM: A Benchmark for Evaluating Machine Theory of Mind in LLM-based Conversational Recommender Systems · AAAI 2026