Understanding Book Popularity on Goodreads
| dc.contributor.author | Maity, Suman Kalyan | |
| dc.contributor.author | Kumar, Ayush | |
| dc.contributor.author | Mullick, Ankan | |
| dc.contributor.author | Choudhary, Vishnu | |
| dc.contributor.author | Mukherjee, Animesh | |
| dc.date.accessioned | 2023-03-17T22:48:41Z | |
| dc.date.available | 2023-03-17T22:48:41Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | 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. | en |
| dc.identifier.doi | 10.1145/3148330.3154512 | |
| dc.identifier.uri | https://dl.eusset.eu/handle/20.500.12015/4493 | |
| dc.language.iso | en | |
| dc.publisher | Association for Computing Machinery | |
| dc.relation.ispartof | Proceedings of the 2018 ACM International Conference on Supporting Group Work | |
| dc.subject | goodreads | |
| dc.subject | book popularity | |
| dc.subject | prediction | |
| dc.title | Understanding Book Popularity on Goodreads | en |
| dc.type | Text/Conference Paper | |
| gi.citation.startPage | 117–121 | |
| gi.citations.count | 4 | |
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| gi.citations.element | Maria Antoniak, Melanie Walsh, David Mimno (2021): Tags, Borders, and Catalogs, In: Proceedings of the ACM on Human-Computer Interaction CSCW1(5), doi:10.1145/3449103 | |
| gi.citations.element | Ryo Shiozaki (2024): Preservation Preferences for Books and Websites as Cultural Heritage: A Conjoint Analysis, In: Collection Management 4(49), doi:10.1080/01462679.2024.2422600 | |
| gi.conference.location | Sanibel Island, Florida, USA |