Online food recommendations: A complex problem?

Speaker:  Christoph Trattner – Bergen, Norway
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science


The problem of recommending food to people has recently become an active field of research. While there is a growing body of work investigating how online food recommender systems could potentially be designed to better meet the users’ preferences, to date less research has tried to understand the nature of online food choices and their complexity. How do people make their food choices online? To what extent can we model and predict this behaviour, and can we actually change it through recommender technology?

Why might we want to change behaviour? According to the World Health Organization around 80% of cases of heart disease, strokes and type 2 diabetes could be avoided if people would implement a healthier diet. Health-aware food recommender technologies have been touted as a valuable asset in achieving the ambitious goal of developing systems, which positively impact on the food choices people make. For example, they may help people to implement a healthier diet by suggesting healthier versions of a similar meal they typically like. In this talk, I will present our latest research on the online food recommender problem. I will reveal the complex nature of online food choices and how this knowledge can be used to build novel food recommender systems employing nudges. 

About this Lecture

Number of Slides:  60
Duration:  86 minutes
Languages Available:  English, German
Last Updated: 

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