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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 ve 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. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, different type of algorithms is trained to make classifications or predictions, and to uncover key insights in this project. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. Machine learning algorithms build a model based on this project data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of datasets, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
1) Ingredients of foods: Most previous food recommender-systems [29], [30] rely primarily on historical ratings of users to draw upon food recommendations through a collaborative filtering approach that ignores food ingredients. This is due to the observation that a given food is usually preferred by an individual because it contains ingredients, he/she may like to eat. This may overlook some important aspects in the recommendation. For example, foods containing chicken wings may be a person's favorite food, while he/she may be allergic to some types of spices that can be used during the food preparation. Therefore, collaborative filtering recommender-systems may not be enough to account for such user's preferences and constraints. 2) Time factor: Traditional recommender-systems [19], [26]_[28] are based on the premise that users with similar preferences in the past will have similar tastes in the future. Accordingly, these recommender-systems use static data and ignore potential changes in user's food preferences, diet or life style that can occur over time in realistic scenarios. 3) Cold start users and cold start foods: Due to the fact that users often rate just a few foods, traditional collaborative filtering-based food recommender systems have difficulty recognizing active user neighbors or similar foods. Accordingly, collaborative Filtering-based food recommendation are only able to suggest foods to users who have rated enough foods. Cold start users, who have rated only few food items, are thereby ignored. Similarly, new food items (food cold start) that have not attracted yet enough ratings from users are ignored as well by such a collaborative filtering-based approach. 4) Users' community: Another issue, which is again ignored in existing recommender-systems, is the user's neighborhood or community aspect. Intuitively, community aspect can be utilized to predict the rating of unseen food item and the success likelihood of a given diet, extrapolating from active users' activities in the neighborhood. Typically, community aspect can be handled using clustering-based models. Nevertheless, it has been shown that such an approach also suffers from several other difficulties as well, which are somehow inherent to clustering techniques employed (e.g., optimal number of clusters, efficiency of similarity measures employed). Disadvantages _ Time factor in user rating is ignored. _ With the exception to [52], user communities are ignored, and the analysis of user-user interaction is rather light. _ Trust relationships among users, when available, are neglected. _ Only few works attempted to integrate user ratings and food content, which is a vital component of any food recommender-system.
1) Ingredients-aware food recommender-system: Unlike traditional collaborative-based food recommender systems, our model integrates both collaborative filtering-based model (user-based phase) and content-based model (food-based phase). As a result, a set of foods that both suit the user's preferences and utilize his/her previous ratings are recommended. 2) Time-aware food recommender-system: A novel time-aware similarity measure that takes into account changes in food preferences or diet over time is developed in this paper. This makes the proposal suitable to handle cases where users change his/her rating / preferences over time. 3) Trust-aware food recommender-system: A trust-aware food recommender-system is developed to overcome the cold start user and cold start foods problems of the traditional collaborative filtering-based food recommender-systems. Our proposed model builds a trust network of users based on trust (follower following) statements to predict user ratings efficiently. The trust network generation plays an important role in addressing the neighbor selection problem. Trust statements can be used to predict the rating of unseen items in food recommender-systems since there is a high correlation between users' trust and user ratings-based similarity measure. The user's trust network and the user ratings-based similarity are integrated in this study to address the data sparsity problem utilizing knowledge that is stored outside of the user's local neighborhood of similarity. 4) Community-aware food recommender-system: Contrary to previous works where users' communities are not considered in the food recommendation process, our model explicitly accounts for such aspects where the optimal number of users' clusters is determined automatically. Moreover, using a graphical like representation where edge weights are calculated according to user ratings-based similarity and trust network, the proposed method accommodates sparse datasets. Advantages ? The purpose Time-aware food recommender-system based on Deep and Learning and Graph Clustering (TDLGC) and Machine Learning Classifiers. ? (1) User based rating prediction, and (2) Food-based rating prediction. In the first phase, (i) by utilizing both the user rating and the follower-following network, the user-user similarity matrix as well as the users' trust network are generated.Then,(ii) based on the user similarities and the trust network, the given user set is mapped onto a weighted graph. ? In the next step, (iii) a novel time-aware graph clustering algorithm is proposed to cluster the users into different groups accordingly. Finally, (iv) utilizing users' clusters from previous step, user similarity and historical ratings, new user-based ratings are predicted.