A novel time aware food recommender system based on deep learning and graph clustering

Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet.This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous systems, such as ignoring food ingredients, time factor, cold start users, cold start food items and community aspects.The proposed method involves two phases: food content-based recommendation and user-based recommendation.Graph clustering is used in the first phase, and a deep-learning based approach is used in the second phase to cluster both users and food items.Besides a holistic-like approach is employed to account for time and user-community related issues in a way that improves the quality of the recommendation provided to the user.We compared our model with a set of state-of-the-art recommender-systems using five distinct performance metrics: Precision, Recall, F1, AUC and NDCG.Experiments using dataset extracted from "Allrecipes.com"demonstrated that the developed food recommender-system performed best.