https://doi.org/10.1140/epjds/s13688-018-0149-5
Regular article
On the predictability of the popularity of online recipes
1
Department of Information Science and Media Studies, University of Bergen, Bergen, Norway
2
Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
3
Chair of Information Science, University of Regensburg, Regensburg, Germany
* e-mail: christoph.trattner@uib.no
Received:
26
January
2018
Accepted:
27
June
2018
Published online:
5
July
2018
Popularity prediction has been studied in diverse online contexts with demonstrable practical, sociological and technical benefit. Here, we add to the popularity prediction literature by studying the popularity of recipes on two large and well visited online recipe portals (Allrecipes.com, USA and Kochbar.de, Germany). Our analyses show differences between the platforms in terms of how the recipes are interacted with and categorized, as well as in the content of the food and its nutritional properties. For both datasets, we were able to show correlations between recipe features and proxies for popularity, which allow popularity of dishes to be predicted with some accuracy. The trends were more prominent in the Kochbar.de dataset, which was mirrored in the results of the prediction task experiments.
Key words: Online recipes / Food / Popularity
© The Author(s), 2018
