Food Recommender Systems

The frequency with which people make food choices in everyday life means that recommender systems may have an enormous potential to influence end users’ lives. If recommenders work well they may offer users health or other benefits, although the evidence suggests that standard recommendation algorithms tend to reinforce current habits, which may have negative consequences. Providing suitable recommendations is extremely challenging since the decisions people make when choosing food are complex, multi-faceted and context dependent. The primary contributions of this chapter are three-fold. The first is to detail the many facets and complexities of the problem. We do this by summarising literature on how people make food choices when using digital systems, including recommender systems, search engines and online food portals. We, moreover, discuss the (sometimes conflicting) goals people have when choosing food and what this means for recommendation. The second contribution is to review the technologies that have been proposed to solve these challenges. We present research on both algorithmic and interface development, as well as theories from other fields such as theories on behaviour change that have been suggested to form the basis of adaptive goal and preference modelling. The third contribution is to provide an overview of the methods and resources available to study food recommendation as a research problem. We conclude by outlining open areas yet to be studied, which will drive research in this area in the years to come.