# A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling
## 4 Experiments
### Performance and Comparison  
The MFDNN recommendation model combines user-item explicit and implicit feedback. The recommendation model combines MF and DNN, which learn embedded features independently; however, the model only merges them in the final output layer through the activation function. In this experiment, we compared and analyzed results in all these aspects. The selected top-N value was taken as N = 10, and the dataset was MovieLens 1m. (1) Explicit and implicit feedback information  
In order to measure the recommendation performance achievable with explicit and implicit feedback information, the explicit and implicit feedback data were separated out for experiments. Specifically, we first removed the implicit feedback information, denoted by MFDNN+, and then we removed the explicit feedback information, denoted by MFDNN-. The experimental results for MFDNN+, MFDNN-, and MFDNN are shown in Figure 9. As shown in Figure 9, the most accurate recommendations were generated using the explicit and implicit feedback data. According to the HR value, MFDNN provided the highest performance. In the small batch of data before training, the performance of MFDNN- was better than that of MFDNN+, and subsequently the performance of MFDNN+ was better. According to the NDCG value, MFDNN had the best performance, followed by MFDNN- and, finally, MFDNN+. These experiments verified that the combination of explicit and implicit feedback data provided better recommendation performance. These results also illustrate the shortcomings of simply considering explicit and implicit feedback data individually. Explicit and implicit feedback data reflect the user's preferences or item feature information from a certain aspect. In order to generate accurate recommendations, it is necessary to fully consider the user's interests or preferences and the potential features of the item itself. Figure 8: The HR and NDCG values for different dropout rates. Figure 9: The HR and NDCG values achieved with MFDNN+, MFDNN-, and MFDNN. The performance was further verified by considering two bias factors in the explicit feedback information; the results were compared with those from the method that does not consider these two factors (MFDNN-). The experimental results are shown in Figure 10. As shown in Figure 10, when considering two bias factors, the performance of the recommendation model was improved. Although the performance improvement was not very large, the overall performance improved to some extent. Further, in practical applications, different users have different score preferences. For users who tend to give positive reviews even when they are not satisfied with an item, the score values will not be too low, and the ratings from this type of user are generally high. Conversely, users who are more stringent may give a lower rating even if they are somewhat satisfied with an item; thus, their score values will not be particularly high. In addition, if an item is inexpensive, its overall rating will be higher, and if the item is of poor quality, its overall rating will be lower. Based on the above analysis, it is meaningful to consider the explicit feedback score preference information, and in theory this can improve the performance of the recommendation algorithm. Although the model considers the bias factor, its performance improvement is not obvious. In subsequent research, it will be necessary to learn more suitable bias factors to improve the recommendation performance [28; 73]. (2) Recommendation model based on MF and DNN  
MF is a linear model that can mine linear user-item correlation features. On the other hand, DNN is a nonlinear model that can mine the potential nonlinear relationship characteristics of user-item data. In order to verify the recommendation performance of MFDNN, MF and DNN were separately trained as recommendation models. The HR and NDCG values for MF, DNN, and MFDNN are shown in Figure 11. As shown in Figure 11, MFDNN demonstrated the best performance.