Display Content, Display Methods, and Evaluation Methods of the HCI in Explainable Recommender Systems: A Survey

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; and (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.