Introduction to the Special Issue on Causality Representation Learning in LLMs-Driven Recommender Systems

Lina Yao; Julian McAuley; Yongfeng Zhang; Kun Zhang

doi:10.1145/3747838

July 8, 2025ACM transactions on office information systemsOpen Access

Introduction to the Special Issue on Causality Representation Learning in LLMs-Driven Recommender Systems

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Key Points

Trustworthiness is a vital aspect of recommender systems, emphasizing the need for robust evaluation frameworks.
The editorial discusses recent regulations affecting the development of trustworthy recommender systems.
Causality representation learning is essential to enhance the reliability of recommendations made by LLMs.
Exploring multiple facets of trustworthiness can significantly improve user acceptance of recommender systems.

Implication

This editorial highlights trustworthiness aspects in recommender systems, indicating recent regulations impacting the field.

Abstract

This editorial introduces the Special Issue on Trustworthy Recommender Systems, hosted by the ACM Transactions on Recommender Systems in 2024. We provide an overview on the multifaceted aspects of trustworthiness and point to recent regulations that ...

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Cite This Study

Yao et al. (2025) studied this question.

synapsesocial.com/papers/68d44f7b31b076d99fa56df4 https://doi.org/10.1145/3747838

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Introduction to the Special Issue on Causality Representation Learning in LLMs-Driven Recommender Systems

Key Points

Implication

Abstract

Discussion

Cite This Study

Introduction to the Special Issue on Causality Representation Learning in LLMs-Driven Recommender Systems

Key Points

Implication

Abstract

Discussion

Cite This Study

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