Overweight and obesity are common in developed countries and informative campaigns about the importance of a healthy diet undertaken by the national governments and international agencies have only met limited effect. Recently, food recommendation systems have been proposed by a number of researchers, in order to help users in balancing their diet.
Current recommender systems are, however, severely limited as they fail to model the user choices in a sophisticated way, do not provide enough diversification in their recommendations and do not account for temporal patterns. It is important to cope with these aspects in order to make food recommendation systems effective in practice; since a recommendation is useful only if the user makes an effort in changing his eating habits.
Hence, the goal of this internship is to develop new techniques for food recommendation systems focusing on dynamic and sequential aspects.
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