Data Science on the Web for Better Food Decision MakingSpeaker: Christoph Trattner – Bergen, Norway
Topic(s): Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science
AbstractAccording to the World Health Organization around 80% of cases of heart disease, strokes and type 2 diabetes could be avoided if people were to implement a healthier diet. Computational data analytics approaches have been touted as a valuable asset in achieving the ambitious goal of understanding user behavior and being able to develop intelligent online systems, which can positively influence people’s food choices. In this talk, I will present our research on data science approaches to understand, predict and potentially change food decision making in an online context. First, I will show to what extent online food interactions can be linked to real-world health issues such as obesity on a large-scale. After that, I will show how people upload, bookmark or rate online recipes in large online food communities and how contextual factors and biases such as seasonality, temporality, social context or presentation of recipes have an impact on popularity and how they are perceived. Furthermore, I will reveal to what extent these factors and biases can be exploited to model and predict the users’ online food choices. To conclude, I will present some preliminary work aiming to nudge people towards food choices.
About this LectureNumber of Slides: 31
Duration: 30 minutes
Languages Available: English, German
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