Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems

Traditional recommender systems (RSs) generate suggestions by relying on user preferences and item characteristics. However, they do not to properly involve the user in the decision-making process. This gap is particularly evident in Conversational Recommender Systems (CRSs), where existing methods struggle to facilitate meaningful dialogue and dynamic user interactions.

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Publication Details

Published
2024-10-08
DOI
https://doi.org/10.1145/3640457.3688023
Citations
6
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
4.67
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article

Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems

Alessandro Petruzzelli
6 citations
Recommender Systems and Techniques
4.67
article

Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems

Alessandro Petruzzelli
article en
6 citations

Abstract

Traditional recommender systems (RSs) generate suggestions by relying on user preferences and item characteristics. However, they do not to properly involve the user in the decision-making process. This gap is particularly evident in Conversational Recommender Systems (CRSs), where existing methods struggle to facilitate meaningful dialogue and dynamic user interactions.

University of Bari Aldo Moro (IT)
Quality Education
Openalex Percentile: Top 5%
Recommender Systems and Techniques
4.67
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Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems — Alessandro Petruzzelli · (2024) | TGRS Research Map | TGRS