## 5 Discussion  
Recent research in movie recommendation systems has made significant strides, exploring various domains such as IoT scenarios [36], multi-task learning [37], and deep learning [38]. While these studies offer substantial advances, they frequently overlook the importance of temporal dynamics in user-item interactions. TKGRS fills this research gap by seamlessly integrating a temporal decay factor alongside GCNs and matrix factorization.  
The TKGRS makes a transformative contribution to the field of recommendation system. By uniquely incorporating temporal dynamics through a decay factor, it adds a dynamic layer to the process of recommendation. This feature, validated through RMSE and MAE metrics on the MovieLens and Douban datasets, surpassed existing models and provided a more robust alternative for research into recommendation systems. TKGRS not only outperformed the others in terms of the RMSE and MAE metrics but also stood strong when qualitatively compared with existing state-of-the-art models. The algorithm uniquely incorporates temporal dynamics through a decay factor, adding a dynamic layer to the recommendation process.  
Despite its strengths, TKGRS has limitations. The computational complexity associated with GCNs might limit its scalability, especially for large datasets. Future research could focus on enhancing the algorithm's robustness against adversarial attacks and exploring  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Pros** & **Cons** \\ \hline \multirow{4}{*}{GHRS} & The hybrid approach combines multiple features, providing robust recommendations & Lacks a temporal component, thereby not accounting for users’ recent behavior \\ \hline \multirow{3}{*}{GLocal-K} & Focuses on both local and global patterns, providing balanced recommendations & Not as sophisticated in capturing complex relationships \\ \hline \multirow{2}{*}{MG-GAT} & Captures complex relationships through multiple graphs & Does not account for temporal dynamics \\ \hline \multirow{3}{*}{SparseFC} & Requires fewer parameters than traditional fully connected layers, making it computationally efficient & The performance is highly dependent on the choice of the kernel function, which may require expertise to select appropriately \\ \hline \multirow{3}{*}{CF-NADE} & Specifically designed for collaborative filtering tasks where data sparsity is a common issue, providing a more nuanced model & The algorithm’s time complexity can be high, especially when the hidden representation’s dimensions and the number of possible ratings are large \\ \hline \multirow{4}{*}{TKGRS} & Incorporates a time decay factor, thus adding a temporal dimension. Utilizes graph convolutional networks (GCNs) for a sophisticated understanding of the complex relationships between users and items & The complexity may be higher due to the incorporation of GCNs, which could make it slower for larger datasets \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between different algorithms.  
its scalability. Moreover, while the choice of hyperparameters has been optimized, it has not been exhaustively explored, leaving room for potential improvements. Studies could also focus on addressing data sparsity issues and extending its applicability across diverse sectors such as healthcare, e-commerce, and smart cities.  
TKGRS not only outperformed the others in terms of RMSE and MAE metrics but also stood strong when qualitatively compared with existing state-of-the-art models, such as those discussed in the survey on knowledge graph-based recommendation systems by [39]. Unlike these models, TKGRS addresses the challenges of users' dynamic preference, offering a more comprehensive solution. In conclude, TKGRS provides a more holistic solution that takes the temporal changes in users' behavior into account, thereby standing strong both quantitatively and qualitatively when compared with existing models.