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On explaining recommendations with Large Language Models: a review.

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Large Language Models (LLMs) show promise for explainable recommender systems. However, research on using LLMs for recommendation explanations is still in its early stages, with few studies currently available.

Keywords:
LLMSexplainable AIexplainable recommendationexplanationslarge language modelsrecommender systems

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Information Retrieval

Background:

  • Large Language Models (LLMs) like ChatGPT offer new avenues for enhancing recommender systems.
  • Explainability is crucial for user trust and transparency in recommendations.

Purpose of the Study:

  • To systematically review the literature on using LLMs for generating explanations in recommender systems.
  • To identify current methodologies, challenges, and future research directions in this emerging field.

Main Methods:

  • A comprehensive literature search was conducted in the ACM Guide to Computing Literature.
  • Publications from November 2022 to November 2024 were analyzed.
  • Inclusion criteria were applied to a pool of 232 articles, resulting in six relevant studies.

Main Results:

  • The application of LLMs in explainable recommender systems is nascent, with limited published research.
  • The reviewed studies highlight the potential of LLMs to improve the quality and transparency of recommendation explanations.

Conclusions:

  • Despite the early stage of research, LLMs hold significant potential for advancing explainable AI in recommender systems.
  • Further research is encouraged to develop more transparent and user-centric recommendation explanation solutions.