Scholarlink

<p>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 enhance both the relevance and interpretability of recommendations. The Bayesian network captures causal dependencies among user-item interactions, while the LLM injects contextual semantics from user reviews and product descriptions. Our method was evaluated on a dataset of 1.2&#xa0;million interactions and showed significant improvements over baseline models, with gains of 84.44% in precision, 88.37% in recall, and 89.36% in NDCG. A statistical t-test confirmed the significance of these improvements (<i>p</i> &lt; 0.05). We further provide an error analysis and discuss the implications of using causal modeling for scalable, transparent, and GDPR-compliant recommender systems. Our results underscore the potential of causal representation learning to improve personalization and decision-making in recommender systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月20日

查看全文
  • Serge Stéphane Aman,
  • Tiemoman Kone,
  • Behou Gerald N’guessan,
  • Kouadio Prosper Kimou

摘要

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 enhance both the relevance and interpretability of recommendations. The Bayesian network captures causal dependencies among user-item interactions, while the LLM injects contextual semantics from user reviews and product descriptions. Our method was evaluated on a dataset of 1.2 million interactions and showed significant improvements over baseline models, with gains of 84.44% in precision, 88.37% in recall, and 89.36% in NDCG. A statistical t-test confirmed the significance of these improvements (p < 0.05). We further provide an error analysis and discuss the implications of using causal modeling for scalable, transparent, and GDPR-compliant recommender systems. Our results underscore the potential of causal representation learning to improve personalization and decision-making in recommender systems.