Reading remains a preferred leisure activity fueling an exceptionally competitive publishing market: among more than three million books published each year, only a tiny fraction are read widely. It is largely unpredictable, however, which book will that be, and how many copies it will sell. Here we aim to unveil the features that affect the success of books by predicting a book’s sales prior to its publication. We do so by employing the Learning to Place machine learning approach, that can predicts sales for both fiction and nonfiction books as well as explaining the predictions by comparing and contrasting each book with similar ones. We analyze features contributing to the success of a book by feature importance analysis, finding that a strong driving factor of book sales across all genres is the publishing house. We also uncover differences between genres: for thrillers and mystery, the publishing history of an author (as measured by previous book sales) is highly important, while in literary fiction and religion, the author’s visibility plays a more central role. These observations provide insights into the driving forces behind success within the current publishing industry, as well as how individuals choose what books to read.

中文翻译：

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书籍取得成功：在出版之前预测书籍的销量

阅读仍然是休闲活动的首选，这为竞争异常激烈的出版市场提供了动力：每年出版的三百万多本书中，只有很少的一部分被广泛阅读。但是，这将是哪本书，以及将售出多少本书，在很大程度上是不可预测的。在这里，我们旨在通过在书籍出版之前预测书籍的销量来揭示影响书籍成功的功能。我们通过采用“学习放置”来实现机器学习方法，可以预测小说和非小说书籍的销售，以及通过将每本书与相似书籍进行比较和对比来解释这些预测。我们通过特征重要性分析来分析有助于书籍成功的特征，并发现出版业是所有类型图书销售的重要驱动因素。我们还发现了体裁之间的差异：对于惊险和神秘，作者的出版历史（以以前的图书销售量衡量）非常重要，而在文学小说和宗教中，作者的知名度则扮演着更重要的角色。这些观察结果提供了对当前出版业成功背后的推动力以及个人如何选择阅读哪些书籍的见解。