# A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling
## 1 Introduction
Explicit feedback data has the ability to express user preferences and behaviors more accurately; however, in real life, it is difficult to obtain representative and sufficient amount of explicit feedback information based on users [14]. At the extreme, feedback data can be very scarce and not easy to obtain in many application scenarios. Implicit information data is easy to obtain, and the amount of information is relatively large; however, its information cannot accurately express user preferences. Therefore, if we make full use of the advantages of the two types of data, we can achieve a good recommendation effect [8]. For the first time, [PERSON] and [PERSON] [15] proposed the theoretical idea of combining explicit feedback data with implicit feedback data. [PERSON] et al. [16] proposed a matrix factorization model, which used different weights for implicit feedback data and explicit feedback data for learning modeling. The main idea of the matrix factorization model is to treat purchased and viewed commodities as implicit feedback data and mark them as \"1\", to mark other types of commodities as \"0\" for processing, and then combine them with display feedback data, before combining explicit feedback and implicit feedback. [PERSON] [17] first clustered user sets and item sets to propose a learning model. [PERSON] [18] proposed a personalized ranking model that combined explicit and implicit feedback, which was implemented by optimizing the evaluation index ERR (Expected Reciprocal Rank). With the rapid development of deep learning, the application of deep learning in recommendation models is gradually increasing. [PERSON] et al. [19] proposed a friend recommendation model based on a Bayesian ranking deep neural network, which converted the recommendation problem into a ranking problem. The recommendation method based on deep learning has been successfully applied to label recommendation [20] and POI [21] recommendation, and different neural network structures have been proposed, such as multi-layer perceptron (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN) [6]. A deep neural network can effectively simulate nonlinearity in the data through nonlinear activation. Some are also used to transform recommendation problems into classification problems. Although deep learning has been widely used in recommendation methods and recommendation systems, the research on recommendation methods based on deep learning is still in the development stage [10]. Recently, some researchers have realized the importance of heterogeneous information for recommendation. Heterogeneous Information Networks (HINs) effectively integrate more information and form a new trend in the development of data mining. A large amount of user information can be obtained to make the content of the recommendation system more diverse, including academics, commodities, friends, music, services, etc. [22]. In addition to traditional recommendation methods, a large number of new recommendation methods have also been generated, such as social network-based recommendation methods, context awareness recommended methods of the internet [23], and location-based recommendations [24]. However, these recommendation methods based on heterogeneous information also have some challenges: (1) Massive amounts of HIN data hide the objects' comprehensive and detailed information. Hence, mining and analyzing valuable information for HIN recommendations is a key challenge. (2) The rapid expansion of HINs generates increasing amounts of data, such as a wide variety of user features. How to take advantage of these features to build a unified top-N recommendation model is a substantial problem. (3) It is difficult to combine and measure all of the features of objects to produce HIN recommendations. Considering all of the features may require a significant amount of time and cause an over-fitting problem.