In a world with increasing access to raw data, recommender systems can pare down information to help people make choices on a variety of subjects with greater ease. Libraries contain vast amounts of information and use classification schemes to sort it. However, fiction classification is a continuing issue in libraries. This is especially true in academic libraries where fiction might be used for recreational or scholarly purposes. In this paper, the idea that an academic library recommender system might solve some of the problems of fiction classification is discussed. A qualitative evaluation is performed on six book recommender systems. Recommendations given by each system for a single novel are analyzed based upon information gathered from a close reading, book reviews, formal critiques, academic papers, and university syllabi. It is hoped that this study will be of use to academic librarians and creators of recommender systems
