Towards Empathetic Conversational Recommender Systems
ACM Conference on Recommender Systems (RecSys), pp. 84–93
Abstract
Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language models 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.
Authors 10
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Affiliation as printed
School of Computer Science and Technology, Shandong University, China
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Affiliation as printed
Tencent, China
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Shandong University · University of Amsterdam
Affiliation as printed
Shandong University; University of Amsterdam, China
Shandong University
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Affiliation as printed
School of Computer Science and Technology, Shandong University, China
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Affiliation as printed
School of Computer Science and Technology, Shandong University, China
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Affiliation as printed
Tencent, China
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Affiliation as printed
Tencent, China
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Affiliation as printed
Tencent, China
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Affiliation as printed
University of Amsterdam, Netherlands
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Zhaochun Ren Aachen
Affiliation as printed
Leiden University, Netherlands
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