Towards Empathetic Conversational Recommender Systems (2409.10527v1)

Abstract: Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained LLMs to capture the dialogue context. Most CRS approaches, trained on benchmark datasets, assume that the standard items and responses in these benchmarks are optimal. However, they overlook that users may express negative emotions with the standard items and may not feel emotionally engaged by the standard responses. This issue leads to a tendency to replicate the logic of recommenders in the dataset instead of aligning with user needs. To remedy this misalignment, we introduce empathy within a CRS. With empathy we refer to a system's ability to capture and express emotions. We propose an empathetic conversational recommender (ECR) framework. ECR contains two main modules: emotion-aware item recommendation and emotion-aligned response generation. Specifically, we employ user emotions to refine user preference modeling for accurate recommendations. To generate human-like emotional responses, ECR applies retrieval-augmented prompts to fine-tune a pre-trained LLM aligning with emotions and mitigating hallucination. To address the challenge of insufficient supervision labels, we enlarge our empathetic data using emotion labels annotated by LLMs and emotional reviews collected from external resources. We propose novel evaluation metrics to capture user satisfaction in real-world CRS scenarios. Our experiments on the ReDial dataset validate the efficacy of our framework in enhancing recommendation accuracy and improving user satisfaction.

An Analytical Summary of "Towards Empathetic Conversational Recommender Systems"

The paper "Towards Empathetic Conversational Recommender Systems" presents a novel framework called the Empathetic Conversational Recommender (ECR) system. This system highlights an innovative approach to incorporating empathy within Conversational Recommender Systems (CRSs) to better align with user needs and improve both recommendation accuracy and user satisfaction.

Theoretical Contributions and Methodology

The authors identify the limitations of existing CRS approaches, which often disregard users' emotional responses and fail to engage them effectively. In this context, ECR is designed to leverage user emotions for enhanced preference modeling and to foster emotionally engaging dialogues.

ECR's architecture is delineated into two primary components:

1. Emotion-aware item recommendation: This module integrates user emotions with traditional entities found in dialogue contexts, refining user preference models to generate more precise recommendations. The framework incorporates both local and global emotion-aware entity representations to provide a comprehensive understanding of user preferences.
2. Emotion-aligned response generation: By employing retrieval-augmented prompts and leveraging emotional reviews from external resources, this component fine-tunes pre-trained LLMs to produce responses that are emotionally resonant, thereby mitigating the risk of hallucination. This method helps strike a balance between human-like emotional expression and factual consistency.

Empirical validation is provided through experiments conducted on the ReDial dataset, demonstrating that ECR significantly outperforms baseline models in recommendation accuracy and subjective user satisfaction metrics.

Numerical Results and Evaluation

The results indicate a substantial improvement in recommendation accuracy metrics such as Recall_True@10 (RT@10) and the Area Under the Curve (AUC). ECR shows an approximate improvement of 6.9% in the AUC over the best-performing baseline model, which underscores its efficacy in distinguishing between items with positive and negative user feedback.

In terms of response generation, ECR demonstrates superior performance across proposed subjective metrics such as emotional intensity, emotional persuasiveness, and lifelikeness, outperforming models even in the zero-shot settings of advanced LLMs like GPT-3.5-turbo and Llama 2-7B-Chat.

Practical and Theoretical Implications

Practically, the introduction of empathy within CRSs can fundamentally enhance user interaction quality by aligning recommendations closely with users' emotional states. This alignment not only improves user satisfaction but also offers potential benefits in increasing user engagement and retention for commercial systems.

Theoretically, this work posits a significant advancement in the field of empathetic computing within dialogue systems. The dual-module structure of ECR presents a blueprint for future research aiming to blend emotional intelligence with artificial intelligence effectively. As LLMs continue to evolve, there is room for this research to influence broader applications in AI, suggesting that empathy could be a vital component in progressing toward more human-centric AI systems.

The paper provides a compelling argument for integrating empathy into CRSs, moving beyond standard, non-emotional interactions. This shift is crucial in a digital environment where understanding and responding to human emotions can distinguish between a satisfactory and exemplary user experience.

Future explorations could involve the application of ECR to diverse domains beyond movie recommendations, testing scalability and adaptability while further refining the balance between emotional expression and factual accuracy. The exploration of multi-item recommendations while maintaining logical coherence in generated responses presents an exciting avenue for continued research.