Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
Abstract
Large Language Models (LLMs) have shown remarkable progress in generating human-quality text and engaging in complex reasoning. This presents a unique opportunity to revolutionize conversational recommender systems by enabling them to generate rich, engaging and personalized narratives that go beyond recommendations. However, the lack of suitable datasets limits research in this area. This paper addresses this challenge by making two key contributions.
First, we introduce REGEN Reviews Enhanced with GEnerative Narratives, a new dataset extending the Amazon Product Reviews with rich user narratives. Furthermore, we perform an extensive automated evaluation of the dataset using a rater LLM. Second, the paper introduces a fusion architecture (CF model with an LLM) which serves as a baseline for REGEN. To the best of our knowledge, this represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives. We demonstrate that LLMs can effectively learn from simple fusion architectures utilizing interaction-based CF embeddings, and this can be further enhanced using the metadata and personalization data associated with items. Our experiments show that combining CF and content embeddings leads to improvements of 4-12% in key language metrics compared to using either type of embedding individually. We also provide an analysis to interpret their contributions to this new generative task.
First, we introduce REGEN Reviews Enhanced with GEnerative Narratives, a new dataset extending the Amazon Product Reviews with rich user narratives. Furthermore, we perform an extensive automated evaluation of the dataset using a rater LLM. Second, the paper introduces a fusion architecture (CF model with an LLM) which serves as a baseline for REGEN. To the best of our knowledge, this represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives. We demonstrate that LLMs can effectively learn from simple fusion architectures utilizing interaction-based CF embeddings, and this can be further enhanced using the metadata and personalization data associated with items. Our experiments show that combining CF and content embeddings leads to improvements of 4-12% in key language metrics compared to using either type of embedding individually. We also provide an analysis to interpret their contributions to this new generative task.
