# A Hybrid Group-Based Food Recommender Framework for Handling Overlapping Memberships
## 3 ITGR Framework  
Figure 1 illustrates the holistic architecture of our proposed ITGR framework. The proposed methodology is divided into two key phases: (a) Phase 1: group formation and (b) Phase 2: group recommendation. The group formation phase entails the selection of a suitable similarity metric to facilitate the creation of cohesive groups, serving as an initial step in any group-based RS. Subsequently, the group recommendation phase employs average aggregation techniques to recommend the top-\(k\) recipes to users within the group. This two-step process ensures the generation of effective and personalized food recommendations tailored to the preferences and needs of the user groups. In Phase 1, the dataset from allrecipes.com was utilized to gather ratings for various recipes. These recipes were then clustered based on similarities in their ingredients and user ratings, forming distinct groups. Clustering helps in identifying patterns and preferences within the data, enabling the creation of user groups with similar tastes. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs.** & **Considerations** & **Group Formation** & **Model** & **Limitations** \\ \hline \hline
[7] & Group-based purchasing & KNN, PCC & Collaborative filtering & User considered to be part of single group \\ \hline
[11] & Visual aided food recommendation & Hierarchical attention module & Neural network-based hierarchical attention network & Lacks consideration of users’ preferences regarding recipe ingredients \\ \hline
[16] & Personalized food recommendation & Cosine similarity & Gated recurrent network & Only spherical clusters can be formed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 1: Group recommender system. In Phase 2, the proposed ITGR model leveraged the group ratings to generate recommendations. The model predicted group ratings for different recipes and suggested the top-k recipes that best matched the group's preferences. By considering the collective preferences of the group, the ITGR model aimed to provide more accurate and satisfactory food recommendations, enhancing the overall user experience. This comprehensive approach not only improves the relevance of recommendations but also allows a collaborative environment where users can benefit from shared tastes and preferences within their groups. The ITGR framework's ability to adapt to group dynamics and deliver personalized recommendations makes it a valuable tool for food recommendation systems. The following are the steps performed in the proposed framework:  
Phase 1: Group Formation  
Step 1: Dataset Collection: The process began with collecting a dataset from allrecipes.com (accessed on 1 June 2024), which contained user ratings for various recipes. Step 2: Rating Extraction: The collected dataset included user ratings for different recipes. These ratings were extracted for further processing. Step 3: Nutrition Analysis: The dataset was analyzed for nutritional information, which helped in understanding the ingredients and nutritional value of each recipe. Step 4: Group Formation: The clustering results were utilized to form cohesive user groups with similar dietary preferences. Unlike existing food-based group recommender systems, the model exhibited a degree of generalization by allowing a user to be part of multiple groups. Phase 2: Group Recommendation  
Step 5: Group Ratings: The formed user groups provided collective ratings for different recipes, reflecting the preferences of the entire group. Step 6: ITGR Model Processing: The proposed ITGR model processed the group ratings to generate personalized recommendations for each group. Step 7: Recommendation Generation: The ITGR model predicted group ratings for various recipes and recommended the top-k recipes that best matched the group's preferences. Step 8: Predicted Group Ratings for Recipes: The final output consisted of the predicted group ratings for recipes, which were then presented as recommendations to the user groups.