传统体裁的相关性再探：小说读者偏好的网络分析
Revisiting the relevance of traditional genres: a network analysis of fiction readers' preferences
摘要：
我们调查如何以及传统的小说类型，如幻想，惊悚片，文学代表读者的喜好。利用Goodreads的用户数据，我们构建了一个图书网络，如果同一个人倾向于阅读或喜欢两本书，那么两本书就紧密相连。然后，我们将这个网络划分为类似书籍的社区，并为每个社区分配一个来自开放图书馆的主题列表，作为传统类型的代理。我们的分析表明，网络社区对应于传统类型的现有组合，但确切的社区取决于我们是否考虑人们阅读的书籍或人们喜欢的书籍。此外，我们应用主成分分析的数据，并发现在图书社区的方差是最好的解释两个因素：成熟/幼稚和现实主义/幻想性质的书籍。我们建议使用这个成熟现实主义平面作为故事的粗略分类工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
