Finding enjoyable fiction books can be challenging, partly because stories are multi-faceted and one's own literary taste might be difficult to ascertain. Here, we introduce the ISAAC method (Introspection-Support, AI-Annotation, and Curation), a pipeline which supports fiction readers in gaining awareness of their literary preferences and finding enjoyable books. ISAAC consists of four steps: a user supplies book ratings, an AI agent researches and annotates the provided books, patterns in book enjoyment are reviewed by the user, and the AI agent recommends new books. In this proof-of-concept self-study, the authors test whether ISAAC can highlight idiosyncratic patterns in their book enjoyment, spark a deeper reflection about their literary tastes, and make accurate, personalized recommendations of enjoyable books and underexplored literary niches. Results highlight substantial advantages of ISAAC over existing methods such as an integration of automation and intuition, accurate and customizable annotations, and explainable book recommendations. Observed disadvantages are that ISAAC's outputs can elicit false self-narratives (if statistical patterns are taken at face value), that books cannot be annotated if their online documentation is lacking, and that people who are new to reading have to rely on assumed book ratings or movie ratings to power the ISAAC pipeline. We discuss additional opportunities of ISAAC-style book annotations for the study of literary trends, and the scientific classification of books and readers.

翻译：寻找令人愉悦的小说书籍可能具有挑战性，部分原因在于故事是多方面的，且个人的文学品味可能难以确定。本文介绍ISAAC方法（内省支持、AI标注与策展），这是一种支持小说读者了解自身文学偏好并找到喜爱书籍的流程。ISAAC包含四个步骤：用户提供书籍评分，AI代理研究并标注所提供书籍，用户审阅书籍喜好的模式，AI代理推荐新书籍。在这项概念验证性自研中，作者测试了ISAAC是否能突显其书籍喜好中的独特模式，激发对其文学品味的深入反思，并做出准确、个性化的推荐，包括令人愉悦的书籍和未被充分探索的文学领域。结果突显了ISAAC相较于现有方法（如自动化与直觉的整合、准确且可定制的标注、可解释的书籍推荐）的显著优势。观察到的缺点是：ISAAC的输出可能引发错误的自我叙事（如果统计模式被表面化理解），缺乏在线文档的书籍无法被标注，以及阅读新手必须依赖假设的书籍评分或电影评分来驱动ISAAC流程。我们讨论了ISAAC式书籍标注在研究文学趋势以及书籍与读者的科学分类方面的额外机遇。