# A Hybrid Group-Based Food Recommender Framework for Handling Overlapping Memberships
## 4 Performance Evaluation
### Results and Discussions  
The suggested group recommender model's effectiveness was assessed using a score for group satisfaction. We divided our dataset into 80% and 20% ratings, with 80% of the ratings used to train the model and 20% of the ratings used to assess the model. The suggested model's performance was evaluated using the group satisfaction metric, NDCG, RMSE, MAE, recall, and precision. While precision and recall were used to assess the generated group recommendations, RMSE and MAE showed the prediction accuracy of the model. By adjusting the group size, we could calculate the impact of group satisfaction. The comparison with existing models is shown in Table 12. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Epoch size & 20 \\ Batch size & 64 \\ Learning rate & Adjusted to 0.001 \\ Optimizer & RMSprop \\ Activation function & Sigmoid \\ Loss function & Binary cross entropy \\ Group size & (5, 10, 15, 20, 25, 30) \\ Top-\(k\) recommendations & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Values of parameters in the proposed model. To aggregate the preferences of the group members, the average aggregation approach was employed. Unlike the most pleasure technique [32], which relies on a veto power-like decision, it is consensus-based and considers the preferences of every group member. Lower RMSE and MAE values suggest that the projected ratings are reasonably close to the actual ratings. More relevant recommendations are indicated by higher precision and recall. It was noted that a user's tastes vary depending on the group. In Figure 3(a), we compare ITGR with an existing matrix factorization-based overlapping membership (MX) approach for the parameter RMSE, which signifies a higher accuracy level, indicating a closer alignment between the model's predicted ratings and the actual observed ratings. In the comparison depicted in Figure 3(b), the MAE values for ITGR and MX are 0.0656 and 0.0699, respectively. The superior performance of ITGR can be attributed to its training on a broader and more diverse dataset. This extensive training enables ITGR to generalize more effectively to new instances, capturing underlying patterns more accurately. Consequently, ITGR exhibited greater precision in predicting ratings compared to the MX model. The comparison in Figure 4(a) is based on precision, which was 0.6001, 0.4399, and 0.0721 for ITGR, MX, and TDLGC, respectively. Figure 4(b) details the comparison based on recall. Recall values for ITGR, MX, and TDLGC were 0.0793, 0.022, and 0.0691, respectively. Greater coverage and more pertinent recommendations resulted from a higher recall value. Since their estimation methods were affected by data sparsity, MX and TDLGC showed a low performance in terms of precision and recall. In contrast, since ITGR was trained on the DNCF framework, which made use of the latent variables of users and recipes, it was not greatly impacted by data sparsity. The F1-score comparison for ITGR, MX, and TDLGC is shown in Figure 5(a) and was 0.1402, 0.0419, and 0.0705 respectively. When k = 10, ITGR's precision was at its highest. The precision and recall suffered as the group size increased. Figure 5(b) illustrates how the proposed framework's precision, recall, and group satisfaction values changed as group size varied. The best performance was achieved with a group size of 10, which conforms to [PERSON] et al. 's [32] elbow technique calculation. Our scheme's performance deteriorated above a group size of 10, so the results are not included for group sizes greater than 15. Compared to the other parameters, recall levels were lower. The primary reason for this distinction can be attributed to Equation (14), where the numerator, representing the hits, yields lower values. However, the lack of a significant difference in NDCG values in  
Figure 4: Performance comparison based on: (**a**) RMSE and (**b**) MAE.