Conclusion
REGEN provides a dataset with consistent user preferences, recommendations, and generated narratives, enabling the study of LLM capabilities in conversational recommendation. We evaluated REGEN using LUMEN, an LLM-based model for joint recommendation and narrative generation, demonstrating its utility, along with sequential recommender models. We believe REGEN serves as a fundamental resource for studying the capabilities of conversational recommender models, a crucial step towards personalized multi-turn systems.
REGEN advances conversational recommendation by integrating language as a fundamental element, enhancing how recommenders interpret and respond to user preferences. This approach fosters research into multi-turn interactions, where systems can engage in extended dialogues to refine recommendations based on evolving user feedback.
The dataset also encourages the development of more sophisticated models and training methodologies. It supports exploration into scaling model capacity, utilizing advanced training techniques, and adapting the methodology across different domains beyond Amazon reviews, such as travel, education, and music.
Ultimately, REGEN sets a new direction for recommender systems, emphasizing comprehension and interaction, which paves the way for more intuitive, supportive, and human-like recommendation experiences.