Abstrakt
Recommender systems are now part of our daily life more than ever and most users are confronted with some form of recommendation on a daily basis. As users of such systems, we don't need to actively seek for new content, but let it be comfortably recommended to us instead.
One of the important parts of our lives that is yet to be covered in this way is the domain of cooking. A traditional dilemma of a person, who is currently in the process of shopping for food is "What else should I buy, so that I can cook something new?" In another words, the person either has to look for novel recipes upfront (which does not have to correspond with available ingredients in the shop), or buy ingredients intuitively (which does not have to correspond with recipes).
The main objective of this paper is to bind cooking and shopping activities together via a mobile recipes recommendation application. The application responds on the content of a user's shopping list and strives for calibration of recommended recipes.
In an online user study, we also show that calibrated recommendations outperform both diversity enhanced and plain similarity-based recommendations.