## 4 Experiments  
Movie recommendation systems are indispensable tools in today's digital world, particularly in the era of information overload. While traditional recommendation methods such as collaborative filtering and content-based filtering have their merits, they lack the sophistication to adapt to the dynamic and evolving nature of users' preferences and interactions with items. The present study aimed to address these limitations by evaluating the TKGRS, an innovative algorithm that synergistically integrates matrix factorization, GCNs, and temporal decay factors. The experiments utilized three well-established movie datasets: MovieLens 100K, MovieLens 1M, and the Douban Conversation Corpus. The MovieLens 100K and 1 Mdatasets consist of 100,000 and 1 million movie ratings by users, respectively. These datasets stand as seminal benchmarks in the movie recommendation systems domain. In contrast, the Douban Conversation Corpus was purpose-built for retrieval-based chatbots, and encompasses 1 million session-response pairs in the training set, 50,000 in the validation set, and 10,000 in the test set. Each dataset offers unique challenges and complexities, making them ideal for evaluating the TKGRS algorithm, which notably incorporates a temporal decay factor to adapt to dynamic user-item interactions. Table 1 outlines the selected hyperparameters, determined through extensive preliminary testing to optimize the performance. The datasets were divided into 80% for training and 20% for the validation subset. We used the Adam optimizer for training, which spanned 30 epochs. An early-stopping criterion was also implemented to prevent overfitting. Next, with the dataset MovieLens 100K dataset as an example, the training and validation's RMSE can be shown as in Figure 2. As depicted in Figure 2, the training and validation RMSE for the MovieLens 100K dataset showed consistent improvement over epochs, validating the effectiveness of the TKGRS algorithm. We benchmarked TKGRS against the existing state-of-the-art models, i.e., LFM-SPE [28] GHRS [29], Glocal-K [30], MG-GAT [31], T-ULVD [32], JK-DMC [33], SparseFC [34], and CF-NADE [35], as shown in Table 2. As indicated in Table 2, TKGRS not only validated the efficacy of integrating GCNs and temporal decay factors but also set a new performance standard. In addition, we also used the \(t\)-test to compare the difference in the mean RMSE \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of factors & 50 \\ Hidden layers & 50 \\ Dropout rate & 0.2 \\ Batch size & 4096 \\ Learning rate & \(1\times 10^{-3}\) \\ Weight decay & \(1\times 10^{-5}\) \\ Number of epochs & 30 \\ Optimizer & Adam \\ Loss Function & RMSE, MAE \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters. Figure 2: The training and validation epochs for MovieLens 100K. between the best benchmark and the proposed algorithms with 10 runs. The P-values were all below the significance level, i.e., 0.05, and hence confirmed that TKGRS performs best, according to the RMSE criterion. TKGRS demonstrated competitive performance when compared with state-of-the-art models. Its true potential, however, lies in its capacity to integrate GCNs and temporal decay factors effectively. This opens up avenues for further enhancements, such as incorporating more advanced graph algorithms and exploring additional optimization techniques. Table 3 presents a theoretical assessment of each algorithm's advantages and disadvantages.