Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

TL;DR

This paper evaluates the ability of large language models to predict user ratings, comparing their performance to traditional collaborative filtering methods, and explores their potential with fine-tuning and limited data.

Contribution

It provides a comprehensive comparison of LLMs and collaborative filtering for user rating prediction, highlighting LLMs' data efficiency and potential with fine-tuning.

Findings

Zero-shot LLMs underperform compared to CF models with user data.

Fine-tuned LLMs achieve comparable or better results with less data.

LLMs show promise for user preference tasks when fine-tuned.

Abstract

Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their previous behavior remains an emerging and still unclear research question. Traditionally, Collaborative Filtering (CF) has been the most effective method for these tasks, predominantly relying on the extensive volume of rating data. In contrast, LLMs typically demand considerably less data while maintaining an exhaustive world knowledge about each item, such as movies or products. In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings. We investigate various LLMs in different sizes, ranging from 250M to 540B parameters and evaluate their performance in zero-shot, few-shot, and fine-tuning scenarios. We conduct comprehensive analysis to compare between LLMs and strong CF methods, and find that zero-shot LLMs lag behind traditional recommender models that have the access to user interaction data, indicating the importance of user interaction data. However, through fine-tuning, LLMs achieve comparable or even better performance with only a small fraction of the training data, demonstrating their potential through data efficiency.