# A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling
## 2 Related Work  
An increasing number of researchers have focused on HIN recommendations with different types of objects or relations [33]. Since HINs were first proposed in [34], many HIN recommendation methods have been proposed. In these works, similarity measurements are vitally important and fundamental, and the most popular method is path-based. For example, a meta-path associated with top-N similarity measurement was proposed in [34; 35] proposed a recommendation method based on personalized semantics to predict users' ratings of items, and [36] proposed symmetric measurements on arbitrary meta-paths. Random-walk-based methods are usually used to mine the paths, weigh the paths, and compute the closeness or relevance between two nodes in a HIN [37]. Random walks in the connected components of the graph assume the properties of Markov Chains (steady-state distribution, irreducibility, etc.) [38; 39]. However, these traditional HIN techniques ignore the value of link attributes; as a result, the meta-path cannot accurately capture the relationship between objects [40]. More recently, other strategies have been proposed to alleviate this shortcoming. A unified and flexible personalized sorting framework, MFPR, was proposed in [41]; this framework combines explicit feedback with multiple implicit feedback. In [42], a unified model fusing generalized matrix factorization and multilayer perception was proposed. In [43], a collaborative filtering recommendation method in view of heterogeneous relations was proposed. Among the works described above, we must compare MFDNN against the model proposed in [44], because it is not only a state-of-the-art HIN recommendation method but also very similar to our model. DeepMF [44] performs click-through rate (CTR) prediction by combining the recommendation ability of factorization machines with the feature learning ability of deep learning, and simultaneously learns the low-order and high-order feature interactions from the original features of the input. Compared with DeepMF, MFDNN has three main differences: (1) In MFDNN, MF and DNN learn embedded features separately, while DeepMF shares the same raw input feature vector. (2) The input layer of MFDNN combines user-item explicit and implicit feedback, while the input layer of DeepMF is a one-shot encoding of each feature field (e.g., gender, and location). (3) The output of MFDNN is a top-N recommendation list, while DeepMF aims to predict the value of ratings. There are many other HIN recommendation algorithms, including collaborative filtering [45] and content-based recommendation [46]. These traditional methods were extensively used in the early phases of HIN. However, the former is not applicable to high-dimensional data, and there is a cold-start problem [47], while the latter takes fewer attributes into consideration. Deep neural networks (DNN) have demonstrated breakthroughs in data mining, e.g., voice recognition [48], image labeling [49; 50], and text classification [51; 52; 53]. Deep learning-based methods, which can learn a large-scale nonlinear network structure and obtain deep feature representations of users and items, have proven effective in recommendation tasks [54; 55; 56]. Convolutional neural networks have a powerful ability to learn feature representations and have the potential to learn sophisticated feature interactions [57; 58]. BayDNN was proposed in [19] as a Bayesian personalized ranking deep neural network model for social network friend recommendations; in this model, the recommendation problem is regarded as a ranking problem. The method described in [59] adopts a simple pre-training strategy using a four-layer neural network for link prediction. In [21], a deep content-aware point-of-interest (POI) recommendation (DCPR) algorithm was proposed; broad learning from multiple sources of information is utilized to solve the problem.