# A Hybrid Group-Based Food Recommender Framework for Handling Overlapping Memberships
## 2 Related Work
### Group-Based Recommendation  
A group-based RS aims to minimize the disparity among group members and promotes group members' satisfaction. In the literature as well as in industry, a limited amount of work has been performed on group RSs for food items. Group-based recommendations are usually more challenging than single-user recommendations, as the RS must strike a balance among conflicting preferences of members. Generally, group-based RSs are divided into two categories: (a) memory-based (e.g., [2; 7; 10; 13]) and (b) model-based (e.g., [4; 5; 9; 16]). Memory-based models rely on past or historical data to calculate similarity measures to match similar people or items together. Cosine similarity [10], PCC [7; 13], and Jaccard coefficient are the most popular techniques used in memory-based approaches,while model-based techniques use machine learning (ML) models to predict correlation among features. Deep learning [4; 5; 16], nonparametric, and matrix factorization [9] are examples of model-based techniques. ML-based techniques are highly preferable when we have a sparse dataset. The authors in [22] offered a customized group-based business model in which users form groups, share products within their communities, and participate in other groups as participants. The authors addressed three key issues: users' social influence, feedback complexity in group purchases, and situations when a user plays various roles in a group (both as an initiator and participant). They developed a group-based buying graph convolutional network (GBGCN) where they constructed a directed heterogeneous graph of the behavioral data of users and a social network. The authors predicted the top-\(k\) items based on initiators' influence in a social network and the preferences of participants and initiators. The work presented in [23] put forward a personality-aware group-based movie recommendation that aids groups in successfully meeting the expectation of group members. According to this work, items can be efficiently recommended while utilizing the big-five personality traits, such as agreeableness, conscientiousness, openness, emotional stability, and extraversion. The authors in [24] introduced a food recommendation system for users' groups, leveraging a deep social community detection approach. By modeling users' interactions and preferences as a graph, the system detected social communities using deep learning techniques to enhance recommendation accuracy. The proposed model incorporated trust network generation and group rating aggregation, addressing the unique challenges of group-based recommendations. Experimental results demonstrate the model's effectiveness in providing personalized and health-oriented food suggestions, significantly outperforming traditional single-user recommendation systems. Similarly, the work in [25] proposed a novel group recommender system utilizing genre preferences to reduce clustering costs. By defining a genre preference vector and using it for clustering, the system significantly lowered the time complexity compared to traditional item preference-based methods. Experimental results showed that this genre-based approach not only enhanced clustering efficiency but also improved recommendation accuracy, especially in datasets with fewer users per group. In addition, the work in [26] presented a novel hybrid approach to mitigate the cold start problem in context-aware recommender systems by combining community-created knowledge, ontologies, association rule mining, and an innovative scoring function based on probability metrics. This method aimed to enhance the accuracy and relevance of recommendations for new users by leveraging both predefined domain knowledge and dynamically mined association rules. The approach was designed to improve the performance of location-based services (LBS) by offering more precise and contextualized information to users. Table 1 presents a summary of the state-of-the-art works in food-based RSs.