Learning to represent causality in recommender systems driven by large language models (LLMs)

Learning to represent causality in recommender systems driven by large language models (LLMs)

Abstract Current recommender systems mainly rely on correlation-based models, which limit their ability to uncover true causal relationships between user preferences and item suggestions. In this paper, we propose a hybrid model that combines a Bayesian network with a large language model (LLM) to e...

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Bibliographic Details
Main Authors: Serge Stéphane Aman, Tiemoman Kone, Behou Gerald N’guessan, Kouadio Prosper Kimou
Format: Article
Language:English
Published: Springer 2025-08-01
Series:Discover Applied Sciences
Subjects:
Recommender systems
Large language models (LLMs)
Causal representation
Bayesian network
Machine learning
Artificial intelligence
Online Access:https://doi.org/10.1007/s42452-025-07551-8
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