Multimodal recommendation systems get clearer explanations for choices
Toward Explainability in Multimodal Recommendation
Information Retrieval
Summary
Recommender systems that use multiple types of data, like images and text, help make better suggestions but can be hard to understand how they decide. The authors organize current explanation methods into two main types based on when the explanation is created, and create clear frameworks for each. They also review how explanation quality is measured and suggest future directions to help build recommendation systems that users can trust and understand better.
What this means in practice
- •For recommendation system engineers: Design clearer explanation modules for multimodal recommender systems to improve user trust and transparency in decision making.
- •For user experience teams: Develop evaluation criteria that balance objective and subjective measures to assess how users perceive recommendation explanations.
A survey. It maps existing work.
Authors
Chen Liang, Jinghui Li, Xiuze Zhou, Zongyue Wang, Yuanguo Lin, Han Yu
Abstract
Multimodal recommender systems effectively alleviate data sparsity and cold-start problems by integrating heterogeneous information, yet complex cross-modal representations and model decision-making processes also introduce new interpretability challenges. Despite the rapid growth of research on the interpretability of multimodal recommendation in recent years, relevant methods and evaluation criteria remain relatively fragmented, lacking a unified conceptual framework. To address this gap, this paper categorizes existing methods into modality explanation and post-hoc explanation based on the timing and manner of explanation generation, and further constructs a progressive system for each category. Building on this foundation, this paper synthesizes four categories of evaluation metrics from both objective quantitative and subjective perceptual perspectives, and reviews relevant datasets along the dimensions of application domain and data source. Through a comprehensive analysis of existing research, we further propose future research directions. This paper aims to establish a unified analytical perspective across multimodal representation, recommendation decision-making, and user preferences, providing a research foundation for building transparent and trustworthy multimodal recommender systems.