On the predictability of the popularity of online recipes

Trattner, Christoph; Moesslang, Dominik; Elsweiler, David

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

Cited by 31 publications

(27 citation statements)

References 64 publications

(84 reference statements)

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“…We collected 25,508 recipe images from , 35,501 from and 72,899 images from Kochbar.de . Recipes from were crawled from the website during the period 22–26 October 2018, whereas the images and recipes from and Kochbar.de were re-used from our past work [ 15 ]. These are amongst the most popular recipe sharing websites in China, the US and Germany, respectively.…”

Section: Methodsmentioning

confidence: 99%

Visual Cultural Biases in Food Classification

Zhang

Elsweiler

Trattner

2020

Foods

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This article investigates how visual biases influence the choices made by people and machines in the context of online food. To this end the paper investigates three research questions and shows (i) to what extent machines are able to classify images, (ii) how this compares to human performance on the same task and (iii) which factors are involved in the decision making of both humans and machines. The research reveals that algorithms significantly outperform human labellers on this task with a range of biases being present in the decision-making process. The results are important as they have a range of implications for research, such as recommender technology and crowdsourcing, as is discussed in the article.

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Section: Methodsmentioning

confidence: 99%

Visual Cultural Biases in Food Classification

Zhang

Elsweiler

Trattner

2020

Foods

Self Cite

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“…Conversely, as for main courses (54.0% (popular) vs 32.5% (holistic)) and desserts (54.5% vs 36.1%) popular recipes were preferred (t(190) = -3.27, p < 0.01 and t(190) = -2.70, p < 0.01 respectively). These mixed results suggested that the holistic user model did not entirely outperform the popular baseline, which, nonetheless, is hard to beat in food recommendation scenarios [47], and a more thorough analysis of personal factors that influence users' choice is needed.…”

Section: Evaluation Of the Effectiveness Of The Holistic Food Rementioning

confidence: 99%

“…This suggests that the set of information we encoded in user profiles as well as the knowledge we encoded in the reasoning component of the pipeline need to be refined and improved. However, as already shown by Trattner et al [47], it is necessary to reemphasize that popularity-based baseline is often hard to beat, especially in the food recommendation scenario. However, the underlying motivations of user choices (such as taste, health, and easiness to prepare the recipe) do signal that a holistic user model can appeal to users who wish to purse healthy food choices.…”

Section: Evaluation Of the Effectiveness Of The Holistic Food Rementioning

confidence: 99%

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

et al. 2020

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Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conflicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be "holistic", i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: first, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best fit the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of "holistic" RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles.

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“…Brambilla analyzed the temporal features of social media response to live events [32]. Trattner studied the popularity of recipes on two large and well visited online recipe portals (Allrecipes.com, USA and Kochbar.de, Germany) [33].…”

Section: Spatio-temporal Patterns Of Popularity Dynamicsmentioning

confidence: 99%

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

2019

J Big Data

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BackgroundWith the rapid development of mobile Internet technology, human society has entered an era of high interconnection between human and information. Especially the emergence of online social networks, such as Weibo in China, LinkedIn, Twitter, Face-book and so on, because of its high interaction and strong group participation, makes many related information converging and merging, leading to the emergence of hot topics on the Internet. These hot topics are the aggregation of information about current events (e.g., natural disasters, sports news, celebrity news), which have geotags-timestamp information. These metadata (geotags and timestamp) are embedded within the content of the message, so that the analysis of an event can be performed by applying a spacetime classification. In addition, the hot topic is the sprout of the network public opinion. Network public opinion has an important impact on the stability of the country and society. Therefore, the popularity of hot topics is studied from the spatial and temporal dimension can better understand the dissemination of public opinion. This paper uses the data of Sina Weibo, one of the largest micro-blogging system in China (https ://weibo .com/), to study the following two questions: (1) study the classification problem of hot topic on Weibo, cluster the popularity time series by clustering algorithm;(2) study the spatial distribution characteristics of hot topic on Weibo based on location label data. AbstractIn this paper, a quantitative temporal and spatial analysis of the dynamics of hot topics popularity in Micro-blogging system was provided. Firstly, the popularity time series of 1167 hot topics were counted and calculated by Excel. Secondly, based on MAT-LAB software,the popularity time series were clustered into six clusters by K-spectral centroid (K-SC) clustering algorithm. Thirdly, we analyzed temporal patterns and spatial patterns of popularity dynamics of topics by statistical methods. The results show that temporal popularity of micro-blogging topics is rapidly dying, and the distribution of popularity is subject to the power law form. In addition, most of the Micro-blogging topics are global topic. Our results can provide a literature reference for studying the influence of online hot topics and the evolution of public opinion. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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On the predictability of the popularity of online recipes

Cited by 31 publications

References 64 publications

Visual Cultural Biases in Food Classification

Visual Cultural Biases in Food Classification

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

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