# A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling
## 1 Introduction
(4) Transmission delays, energy saving issues, data redundancy, and inaccuracy of data transmission during data transmission are also issues that need to be resolved [25]. As a result, selecting the most relevant one from the recommendation results among all the features of objects in HINs is challenging. Based on user-item history information, rating prediction models predict the specific rating for an item given by a user [26]. In practical applications, merchants are concerned about whether users will buy an item, which means telling whether a user will watch a movie is more consequential than predicting the rating value that the user may give after watching the movie. This study mainly considers a bipartite network, a special type of heterogeneous information network, for generating top-N recommendations [27]. Existing user-item data recommendation methods mostly consider user-item implicit feedback and ignore the user preference characteristics behind the explicit data. Here, the explicit feedback data refers to the user-item rating information, and the implicit feedback refers to whether user-item interaction information exists or not. To obtain the user's preference information more comprehensively, this study considers both the explicit and implicit feedback from user-item interactions for mining the users' potential preferences and the underlying features of items [28]. In order to improve the performance of the recommendation algorithm of the heterogeneous information network, the recommendation model is constructed by fusing matrix factorization (MF) [29] and a deep neural network (DNN) [19], which, respectively, obtain the explicit and implicit feedback prediction results. The explicit and implicit feedback prediction results are combined to generate the top-N recommendations [30; 31; 32]. Two bias factors are introduced to consider the characteristicsof the user-item data in explicit feedback information. More specifically, we first consider both explicit and implicit feedback data; the explicit and implicit feedback information is separately trained as input to better mine the potential information behind the user-item rating meta-path information. Here, explicit feedback data refers to user-item ratings, and includes both meta-path attribute information and the characteristics of objects; implicit feedback refers to the user-item relationship data. Then, the MF and DNN models are merged to form the relationship between the user-item rating meta-path and the attribute value information. As they learn embedded features independently, MF and DNN can fully consider the linear and nonlinear user-item features, and respectively train the explicit and implicit feedback data in order to obtain the corresponding output. Subsequently, by combining the explicit and implicit feedback prediction results, the top-N items are recommended to the target user. Finally, we use the MovieLens dataset to verify the model and apply leave-one-out to further evaluate the model. The performance of the method is evaluated using the hit ratio (HR) and normalized discounted cumulative gain (NDCG) evaluation metrics. The proposed method was found to outperform the traditional recommendation model and state-of-the-art recommendation methods. The contributions of this paper are summarized as follows. * We exploit both explicit and implicit feedback information to obtain the user's preference information and the underlying characteristics of the item based on the meta-path selection results. Additionally, in order to obtain explicit feedback information, two bias factors are introduced according to the individual characteristics of the user-item information. * We fuse MF and DNN to mine the potential features of users and items from both linear and nonlinear perspectives. MF and DNN learning are independently embedded to better capture user preference information and the potential feature information of items.