Investigating and predicting online food recipe upload behavior |
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| Affiliation: | 1. University of Bergen, Norway;2. Norwegian University of Science and Technology, Norway;3. University of Helsinki;1. Department of Computer Science, University of Vigo, ESEI, Campus As Lagoas, 32004 Ourense, Spain;2. The Biomedical Research Centre (CINBIO), Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain;3. SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain;4. Centre of Biological Engineering (CEB), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;1. Faculty of Information Technology, Hung Yen University of Technology and Education, Hung Yen, Vietnam;2. School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan;3. Hanoi University of Science and Technology, Hanoi, Vietnam;4. AI Academy Vietnam, 489 Hoang Quoc Viet Rd, Hanoi, Vietnam;1. School of Computer Science, South China Normal University, Guangzhou, China;2. Department of SEEM, The Chinese University of Hong Kong, Hong Kong;3. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;5. School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, China |
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| Abstract: | Studying online food behavior has recently become an active field of research. While there is a growing body of studies that investigate, for example, how online recipes are consumed, in the form of views or ratings, little effort has been devoted yet to understand how they are created. In order to contribute to this lack of knowledge in the area, we present in this paper the results of a large-scale study of nearly 200k users posting over 400k recipes in the online recipe platform Kochbar.de. The main objective of this study is (i) to reveal to what extent recipe upload patterns can be explained by socio-demographic features and (ii) to what extent they can be predicted. To do so, we investigate the utility of several features such as user history, social connections of the users, temporal aspects as well as geographic embedding of the users. Statistical analysis confirms that recipe uploads can be explained by socio-demographic features. Extensive simulations show that among all features investigated, the social signal, in the form of friendship connections to other users, appears to be the strongest one and henceforth is the best to predict what type of recipe will be uploaded and what ingredients will be used in the future. The research conducted in this work contributes to a better understanding in online food behavior and is relevant for researchers working on online social information systems and engineers interested in predictive modeling and recommender systems. |
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