Revisiting the relevance of traditional genres: a network analysis of fiction readers' preferences
This provides insights for book recommendation systems and publishers by offering a new classification tool, though it is incremental as it builds on existing network analysis methods.
The study tackled the problem of how well traditional fiction genres represent readers' preferences by analyzing Goodreads data, finding that network communities based on reading or enjoyment differ and that variance is best explained by maturity and realism factors.
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.