# A Hybrid Group-Based Food Recommender Framework for Handling Overlapping Memberships
###### Abstract  
Recommender systems (RSs) play a pivotal role in mitigating information overload by aiding individuals or groups in discovering relevant and personalized information. An individual's food preferences may vary when dining with friends compared to dining with family. Most of the existing group RSs generally assume users to be associated with a single group. However, in real-world scenarios, a user can be part of multiple groups due to overlapping/diverse preferences. This raises several challenges for traditional RSs due to the inherent complexity of group memberships, degrading the effectiveness and accuracy of the recommendations. Computing user to group membership degrees is a complex task, and conventional methods often fall short in accurately capturing the varied preferences of individuals. To address these challenges, we propose an integrated two-stage group recommendation (ITGR) framework that considers users' simultaneous memberships in multiple groups with conflicting preferences. We employ fuzzy C-means clustering along with collaborative filtering to provide a more flexible and precise approach to membership assignment. Group formation is carried out using similarity thresholds followed by deep neural collaborative filtering (DNCF) to generate the top-\(k\) items for each group. Experiments are conducted using a large-scale recipes' dataset, and the results demonstrate that the proposed model outperforms traditional approaches in terms of group satisfaction, normalized discounted cumulative gain (NDCG), precision, recall, and F1-measure. group recommender; food recommendation; collaborative filtering; fuzzy C-means +
Footnote †: journal: _Applied Sciences_  
Food recommendation systems have become very useful tools to help people find tasty and suitable food choices [2]. These systems serve a multitude of purposes, spanning from suggesting recipes and meal plans to endorsing restaurants or food delivery services. Through the utilization of user data and machine learning algorithms, these systems continuously learn and adjust to offer precise and relevant recommendations. The ultimate objective is to enhance the dining experience, economize time, and aid users in discovering fresh and enjoyable food alternatives. Several works have been proposed in the literature to address food the recommendation problem for individuals [3; 4; 5]. As everyone's tastes and personal preferences vary, this makes it a complex problem to solve, as it is difficult to generalize RSs for different individuals [6]. The complexity of the recommendation problem increases manifold when we plan to make recommendations for a group of individuals interconnected on the basis of some common features. Most of the existing studies make a simplistic assumption that a user belongs to a single group type [4]. It is common for individuals to be associated with multiple groups concurrently in real-world situations, be it friends, family, or colleagues, where the interests are closely aligned in some cases and while, in others, they are unlikely to converge [2]. Group-based food RSs face a far more challenging scenario since group members may differ greatly from one another with conflicting preferences, such as personality traits, attitude, or behavior [7]. Therefore, a system must appropriately balance each individual's preferences and provide the group with a list of foods that almost satisfy every member of the group [8]. [PERSON] et al. [4] introduced a food RS that incorporates community awareness to address the limitations of existing systems. However, the proposed work neglected cold start users, the time factor, food ingredients, and community features. When people negotiate with one another in a group, human personality traits including competition, compromise, avoidance, and accommodation are of the utmost importance and play a significant role in the selection of food products [9].