Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems

TL;DR

CRAG introduces a novel method combining large language models with collaborative filtering to improve conversational recommender systems, especially for recent movies, demonstrating superior performance and coverage.

Contribution

First to integrate state-of-the-art LLMs with collaborative filtering for conversational recommendations, enhancing accuracy and item coverage.

Findings

CRAG outperforms several CRS baselines in experiments.

Improves recommendation accuracy for recently released movies.

Demonstrates superior item coverage on benchmark datasets.

Abstract

Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their superior understanding of context-aware user preferences, they typically struggle to leverage behavioral data, which have proven to be important for classical collaborative filtering (CF)-based approaches. For this reason, we propose CRAG, Collaborative Retrieval Augmented Generation for LLM-based CRS. To the best of our knowledge, CRAG is the first approach that combines state-of-the-art LLMs with CF for conversational recommendations. Our experiments on two publicly available movie conversational recommendation datasets, i.e., a refined Reddit dataset (which we name Reddit-v2) as well as the Redial dataset, demonstrate the superior item coverage and recommendation performance of CRAG, compared to several CRS baselines. Moreover, we observe that the improvements are mainly due to better recommendation accuracy on recently released movies. The code and data are available at https://github.com/yaochenzhu/CRAG.