Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
This addresses a data gap for researchers in conversational recommender systems, though it is incremental as it builds on existing dataset creation methods.
The authors tackled the lack of specific user preferences and explanations in conversational recommendation datasets by creating PEARL, a large-scale dataset synthesized with persona- and knowledge-augmented LLM simulators, resulting in over 57k dialogues with more specific preferences and relevant recommendations compared to prior datasets.
Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with persona- and knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that utterances in PEARL include more specific user preferences, show expertise in the target domain, and provide recommendations more relevant to the dialogue context than those in prior datasets.