Digitalization is transforming the food shopping landscape, allowing for innovative interventions to guide consumers on how and what to buy in real-time. One size fits all interventions have limited success, so tailored interventions are opted. Food shoppers can receive personalized real-time (i.e., at the moment of purchase) feedback in the form of product suggestions (swap offers). However, most studies so far evaluate feedback mechanism based on what you intend to purchase based on personal preferences or past purchases but are not tailored by integrating healthiness and sustainability aspects. Consumers often experience the difficult trade-off between the importance of enjoying the present and the long-term importance of health and sustainability. Up to now, it remains unclear how food recommender systems can optimally facilitate consumer decision making in this respect. Moreover, current system designers assume that consumers will like the recommendation, but hardly test this assumption in a real-life setting. As such, interventions that provide personalized feedback on intended purchases seem promising but research on how to optimally design and implement personalized feedback for healthier and sustainable options is scarce. By combining social and technical knowledge, we aim to understand how to best develop and implement personalized feedback based on various data sources and computational algorithms. In the end, we aim to develop a personalized food recommendation approach for healthy and sustainable choices which is expected to be more effective in improving the healthiness and sustainability of consumer food choices than a one-size fits all approach.
|Effective start/end date||15/02/21 → …|
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