We investigate how well traditional fiction genres like Fantasy, Thriller, and Literature represent readers' preferences. Using user data from Goodreads we construct a book network where two books are strongly linked if the same people tend to read or enjoy them both. We then partition this network into communities of similar books and assign each a list of subjects from The Open Library to serve as a proxy for traditional genres. Our analysis reveals that the network communities correspond to existing combinations of traditional genres, but that the exact communities differ depending on whether we consider books that people read or books that people enjoy. In addition, we apply principal component analysis to the data and find that the variance in the book communities is best explained by two factors: the maturity/childishness and realism/fantastical nature of the books. We propose using this maturity-realism plane as a coarse classification tool for stories.

翻译：我们用古德雷德的用户数据构建了一个书网,如果同一批人愿意阅读或享受这两个书本,两本书就会紧密相连。我们然后将这个网络分成类似的书群,并从开放图书馆中分配每个主题清单,作为传统书族的代名词。我们的分析显示,网络社区与传统族系的现有组合相对应,但确切的社区则不同,这取决于我们是否认为人们阅读的书本或人们喜欢的书本。此外,我们对数据进行主要组成部分分析,发现书群的差异最好由两个因素来解释:书的成熟/童年和现实主义/幻想性质。我们建议使用这个成熟-现实主义的平面作为故事的粗略分类工具。</s>