bibtext: @inproceedings{
  jeong2024factual,
  title={Factual and Tailored Recommendation Endorsements using Language Models and Reinforcement Learning},
  author={Jihwan Jeong and Yinlam Chow and Guy Tennenholtz and ChihWei Hsu and Mohammad Ghavamzadeh and Craig Boutilier},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=xI8C7sfN1H}
  }
abstract: Recommender systems (RSs) play a central role in matching candidate items to users based on their preferences. While traditional RSs rely on user feed-back signals, conversational RSs interact with users in natural language. In this work, we develop P4LM, an _aPpealing, Precise, Preference-comprehensive and Prioritized_ language model which endorses recommended items by emphasizing specific item characteristics and their coverage to a user’s preferences. P4LM uses an _embedding_ representation of a user’s preferences to generate responses that are appealing, factually-grounded and tailored to the user’s preferences. P4LM employs a joint reward function to measure precision, appeal, preference coverage and prioritization of preferences, which are used as AI-based feedback in a reinforcement learning-based language model framework. On the MovieLens 25M and Amazon Product Review datasets, P4LM delivers more appealing and tailored endorsements to users, as determined by auto-critic and rater evaluations.
title: Factual and Tailored Recommendation Endorsements using Language Models and Reinforcement Learning
authors: Jihwan Jeong Yinlam Chow Guy Tennenholtz ChihWei Hsu Mohammad Ghavamzadeh Craig Boutilier