MATURE-Food: Food Recommender System for MAndatory FeaTURE Choices A system for enabling Digital Health

In order to deduce more appropriate and useful recommendation, it is imperative to amalgamate additional information such as the features of the user and the item along with the usual user-item correlation based on rating. In this research, a content-based recommender system, MATURE is further developed, which relies on the additional information related to the user-item to recommend the items for a user who has current mandatory needs to accommodate as opposed to the past preferences. The MATURE-driven recommendations mandatorily include and satisfy the user's mandatory requirements at a given time. This distinguishes it from the existing algorithms, where recommendations are primarily based on the past preferences and fail to satisfy current mandatory requirements of the user, which are opposite to the user's past preferences. Arguably, MATURE is first recommender system, which promisingly undertakes the given mandatory requirements while recommending the most appropriate item set with pertinent justification of the recommendation. This paper expands the applicability of MATURE and focuses on its application as a food recommender system with mandatory nutrient values, required to be consumed daily.