Traditional genre-based approaches for book recommendations face challenges due to the vague definition of genres. To overcome this, we propose a novel task called Book Author Prediction, where we predict the author of a book based on user-generated reviews’ writing style. To this aim, we first introduce the ‘Literary Voices Corpus’ (LVC), a dataset of Italian book reviews, and use it to train and test machine learning models. Our study contributes valuable insights for developing user-centric systems that recommend leisure readings based on individual readers’ interests and writing styles.
Unmasking the Wordsmith: Revealing Author Identity through Reader Reviews
Chiara Alzetta;Felice Dell’Orletta;Chiara Fazzone;Alessio Miaschi;Giulia Venturi
2023
Abstract
Traditional genre-based approaches for book recommendations face challenges due to the vague definition of genres. To overcome this, we propose a novel task called Book Author Prediction, where we predict the author of a book based on user-generated reviews’ writing style. To this aim, we first introduce the ‘Literary Voices Corpus’ (LVC), a dataset of Italian book reviews, and use it to train and test machine learning models. Our study contributes valuable insights for developing user-centric systems that recommend leisure readings based on individual readers’ interests and writing styles.File in questo prodotto:
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