On the predictability of the popularity of online recipes

Christoph Trattner,David Elsweiler,Dominik Moesslang

doi:10.1140/epjds/s13688-018-0149-5

Christoph Trattner, David Elsweiler + Show 1 more

Open Access

https://doi.org/10.1140/epjds/s13688-018-0149-5

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Abstract

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.

Highlights

The traces users leave behind when interacting with items online, combined with properties of the items themselves can be used to predict how popular individual items will become with users of a service
To what extent is the popularity of online recipes predictable and which are the most useful predictive features for this prediction task? we review appropriate background literature, which serves to motivate the investigation of the above research questions
4 Methodology we describe the methodology applied in 4 distinct sub-sections: Sect. 4.1 explains how we isolate and pre-process the recipes from the collection; Sect. 4.2 explains how we measure popularity; Sect. 4.3 outlines the features investigated and, ; Sects. 4.4 and 4.5 present the statistical approaches used to compare and model predictions, respectively

Summary

Introduction

The traces users leave behind when interacting with items online, combined with properties of the items themselves can be used to predict how popular individual items will become with users of a service. This concept—known as popularity prediction—has been studied in diverse contexts including with social media content [1], online news articles [2], and posted videos [3, 4]. We turn our focus to the unit of study in this work by summarizing research from the fields of information retrieval and recommender systems relating to online recipes. Deciding which sources to attend to is a daily struggle for users [17, 18] and is open to numerous biases including subconscious human, as well as algorithmic biases [19]

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