## 3 Proposed System
### Detail Implementation Flow
#### 3.3.2 Phase Two
Nevertheless, the library achieves its peak performance when it reaches a certain threshold of user interactions or when it is configured with optimal parameters, which are determined by the specific application context or user requirements. Among the Cornac models, NeuMF has the best precision scores of 0.4240, 0.3728, 0.3412, and 0.3166. The MLP model ranks second in the Cornac library, with performance metrics of 0.4150, 0.3674, 0.3350, and 0.3104. The performance of MMMF is the lowest with values of 0.1162, 0.1133, 0.1030, and 0.0922. Both GMF and VAECF exhibit reasonable results, though VAECF performs better. While our proposed models do not exceed the performance of the Surprise library, they do surpass the models of the Cornac library. We firmly assert that our models provide a diverse range of learning experiences according to their ability to learn dynamically, as opposed to the static nature of the models in the Surprise collection. Many performance results reflect a decrease with each subsequent increase in k, which is a prevalent pattern observed in recommendation systems. Figure 4: Comparison results from our proposed model with benchmark models. Figure 5 introduces additional metrics for evaluating our performance, including Precision, Recall, and the F1 score. According to the results shown in Figure 4, OURS (using Adam and SGD) yields the best results among the variations of OURS (including Adadelta, Adamax, RMSprop). Thus, we illustrate the performance of recommendation systems optimized with Adam and SGD across various k values, based on three different metrics: Precision, Recall, and F1 Score. The Precision graph indicates that the performance of both optimizers decreases slightly as k increases. SGD starts off stronger at k = 5, but Adam shows more consistency across all k values. The Recall graph shows more variability. Adam starts lower than SGD at k = 5, surpasses it at k = 10, then falls below again at k = 15, with both lines finally converging at k = 20. To find the balance between precision and recall, we turn to the F1 score graph, which combines both metrics. This graph shows a consistent increase as k increases. Both Adam and SGD perform similarly at each k value, though Adam starts off slightly better at k = 5 and finishes similarly at k = 20. This visualization suggests that the performance of the recommendation system varies depending on the chosen metric and the value of k. Such an analysis is important to determine which optimizer to use depending on whether the goal is to maximize precision, recall, or a balance of both (F1 score). Precision measures how many of those recommended items are actually relevant or interesting to the user. For example, if a system recommends 10 movies to a user (k = 10), and 9 out of these 10 are movies the user would like, then the precision at 10 is 0.9. Recall measures how many of the relevant items you managed to find with your recommendation. A high recall means that the system retrieved most of the relevant items but does not care about how many irrelevant items were also recommended. For example, if there are 20 movies that the user would like, and the system recommends 10 movies, out of which 5 are relevant, then the recall at 10 is 0.25. Typically, there is a trade-off between precision and recall. If we recommend many items (increasing k), it is more likely to include all the relevant ones (increasing recall), but it is also likely to include more irrelevant ones (decreasing precision). Conversely, if we recommend fewer items, we might miss some relevant ones (decreasing recall) but increase the likelihood that the recommended items are relevant (increasing precision). ## 5 Ablation Study

To enhance the significance and benefits of our proposed recommendation framework, this section introduces an ablation study. We conducted four ablation experiments against our current model setup. In the first ablation experiment, we removed the dropout layers from the networks.