综述可解释推荐系统中内容、展示与评估的HCI方法
Display Content, Display Methods and Evaluation Methods of the HCI in Explainable Recommender Systems: A Survey
- 从生命周期视角整合算法与交互技术
- 首次提出视频化解释在推荐中的应用路径
- 适合关注AI可解释性与人机交互的研究者
可解释推荐系统（XRS）旨在为用户提供可理解的推荐理由，是人工智能的重要研究方向。近年来，研究逐渐聚焦于算法、展示与评估方法，但现有文献多关注算法层面，对人机交互（HCI）层面关注不足。现有综述缺乏统一分类体系，且对短视频推荐等新兴领域重视不够。本文综合现有文献，提出统一框架：1）从生命周期视角系统梳理XRS的技术与方法，应对模型与解释技术多样性带来的挑战；2）首次强调多媒体，特别是基于视频的解释形式，分析其潜力、技术路径与挑战；3）结构化梳理定性与定量评估方法。研究成果为XRS的系统设计、进展与测试提供重要参考。
原文摘要 · Abstract (English)
Explainable Recommender Systems (XRS) aim to provide users with understandable reasons for the recommendations generated by these systems, representing a crucial research direction in artificial intelligence (AI). Recent research has increasingly focused on the algorithms, display, and evaluation methodologies of XRS. While current research and reviews primarily emphasize the algorithmic aspects, with fewer studies addressing the Human-Computer Interaction (HCI) layer of XRS. Additionally, existing reviews lack a unified taxonomy for XRS and there is insufficient attention given to the emerging area of short video recommendations. In this study, we synthesize existing literature and surveys on XRS, presenting a unified framework for its research and development. The main contributions are as follows: 1) We adopt a lifecycle perspective to systematically summarize the technologies and methods used in XRS, addressing challenges posed by the diversity and complexity of algorithmic models and explanation techniques. 2) For the first time, we highlight the application of multimedia, particularly video-based explanations, along with its potential, technical pathways, and challenges in XRS. 3) We provide a structured overview of evaluation methods from both qualitative and quantitative dimensions. These findings provide valuable insights for the systematic design, progress, and testing of XRS.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。