Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
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.
Code (1)
Tasks
Conversational RecommendationRecommendation SystemsSimilar Papers 제목 키워드 기반
Low-resource Personal Attribute Prediction from Conversation
Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extracting personal attribute knowledge from…
AttributePredictiontext-classificationText ClassificationA Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009
With the big popularity and success of Judea Pearl's original causality book, this review covers the main topics updated in the second edition in 2009 and illustrates an easy-to-follow causal inference strategy in a fore…
Causal InferenceTime SeriesTime Series ForecastingPEARL: Personalized Streaming Video Understanding Model
Human cognition of new concepts is inherently a streaming process: we continuously recognize new objects or identities and update our memories over time. However, current multimodal personalization methods are largely li…
Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers
Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communication. However, a barrier to effective ass…
Language ModelingLanguage ModellingLarge Language ModelRetrievalPEaRL: Personalized Privacy of Human-Centric Systems using Early-Exit Reinforcement Learning
In the evolving landscape of human-centric systems, personalized privacy solutions are becoming increasingly crucial due to the dynamic nature of human interactions. Traditional static privacy models often fail to meet t…
Reinforcement Learning (RL)