Goodreads has launched the Readers Choice Awards since 2009 where users are able to nominate/vote books of their choice, released in the given year. In this work, we question if the number of votes that a book would receive (aka the popularity of the book) can be predicted based on the characteristics of various entities on Goodreads. We are successful in predicting the popularity of the books with high prediction accuracy (correlation coefficient ~0.61) and low RMSE (~1.25). User engagement and author's prestige are found to be crucial factors for book popularity.

翻译：自2009年以来,好读会推出了读者选择奖,用户可以在其中提名/投票他们选择的书籍,并在当年发行。在这项工作中,我们质疑,根据好读会得到的书籍(以及书的受欢迎程度)各实体的特征,能否预测出该书的得票数。我们成功地预测了高预测准确度(关系系数~0.61)和低RMSE(~1.25)的书籍受欢迎程度,用户的参与和作者的声望是书受欢迎的关键因素。