## 7 Conclusions  
In this paper, we introduced a graph-CNN-based model for online product recommendation. The proposed model incorporates the user-item click interaction as a probability distribution and uses this measure to find the similarity among nodes. We calculate the similarity between the nodes using divergence methods and use the results for clustering the nodes based on their interaction similarity. This way, we understand and sample the neighboring nodes beforehand and generate session graphs based on this knowledge. Afterward, these subgraphs are used as input for the GCNN model. With the simple GCNN model, the system finds it difficult to achieve more than 80% prediction accuracy. Additionally, without finetuning the model's structure, it becomes computationally rich and complex. That affects the performance of the model in terms of low prediction rates and model overfitting. Different variations of GCNN have been proposed previously to handle the performance-related issues of the model. We aimed to explore the effect of finding users' similarity based on their interactions with items such as clicks and then use this knowledge to make clusters. This way, we do not have to make extensive changes in the GCNN model. We achieved comparatively better computational complexity results and time than with our previously proposed model, i.e., OpGCN [30]. During the cycle, records with under three ticks are disposed of alongside the records that show some bizarre examples. In particular, we first cycle the log information documents; the irregular information records are changed over to client click arrangements. These click groupings of customers are changed over to unknown customers' sessions. At that point, coordinated charts are built from these sessions alluded to as session graphs. To sample the neighbors, we performed similarity measures to find the similarities between two nodes based on their products' interactions. For this, we estimate the probability distribution of each node and use divergence techniques to measure the distance between these nodes. Based on their similarity scores, we set a threshold and define our criteria for a neighbor's selection. If the similarity score does not fit the requirements, those nodes are not considered neighbors and are classified into different clusters. These clusters are then converted to subgraphs, and these subgraphs are used as input to the GCNN model. To summarize, this research work contributes in the following ways: (1) It helps to elucidate the challenges the GCNN model might face while handling large datasets such as complexity and memory-related difficulties; (2) we propose a framework that helps GCNN models to simplify their tasks; (3) we introduced probability distribution based-similarity \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Data** & **Description** & **Description** & **Description** & **Description** & **Description** & **Description** & **Description** & **Description** \\ \hline \multirow{4}{*}{YOOCHOOSE} & Recall@20 & 17.19 & 28.31 & 45.16 & 31.19 & 50.64 & 69.48 & 70.18 \\  & F-Score & 0.617 & 0.669 & 0.712 & 0.613 & 0.718 & 0.841 & 0.892 \\  & MRR@20 & 8.11 & 19.21 & 17.59 & 27.83 & 37.15 & 38.12 & 40.55 \\  & Testing Accuracy & 0.65 & 0.69 & 0.71 & 0.61 & 0.77 & 0.76 & 0.81 \\ \hline \multirow{4}{*}{DIGINETICA} & Recall@20 & 20.13 & 27.44 & 46.98 & 31.26 & 48.13 & 65.42 & 69.87 \\  & F-Score & 0.691 & 0.627 & 0.698 & 0.713 & 0.785 & 0.794 & 0.847 \\  & MRR@20 & 9.19 & 20.18 & 17.11 & 25.56 & 34.13 & 37.40 & 39.98 \\  & Testing Accuracy & 0.68 & 0.72 & 0.77 & 0.74 & 0.82 & 0.86 & 0.93 \\ \hline \multirow{4}{*}{Our Data} & Recall@20 & 20.21 & 29.82 & 45.38 & 38.56 & 50.18 & 69.34 & 78.91 \\  & F-Score & 0.647 & 0.624 & 0.791 & 0.792 & 0.869 & 0.856 & 0.914 \\ \cline{1-1}  & MRR@20 & 8.02 & 19.34 & 20.45 & 28.01 & 39.11 & 37.14 & 39.17 \\ \cline{1-1}  & Testing Accuracy & 0.61 & 0.72 & 0.75 & 0.75 & 0.81 & 0.88 & 0.97 \\ \hline \hline \end{tabular}
\end{table}