## 1 Introduction  
Nowadays, recommendation systems play a critical role in promoting sales and services in many online applications. For instance, 80 percent of movies watched on Netflix came from recommendations, and 60 percent of video clicks came from home page recommendations on YouTube [1, 2]. Typically, recommendation systems can be classified into content-based (CB), collaborative filtering (CF), and hybrid approaches. Among them, CF is the most popular approach since it does not need to analyze the items' content. Instead, it relies on the relationship between users and items, typically encoded in a rating preference matrix. Although the CF approach has been successfully applied in many domains, it suffers from a cold start (CS) problem [3]. The CS problem indicates that many users have not rated new items not to be correctly linked with similar items [4]. When little or no preference information is available, the recommendation accuracy drops significantly. To solve this problem, many researchers adopt variant auxiliary data such as text descriptions, images, or videos to derive ratings of CF items [5, 6, 7]. However, most of these auxiliary data are high dimension so that the time for evaluating item similarity is much longer. In general, matrix factorization (MF) is one of the most popular methods to implement the collaborative filtering (CF) concept [8, 9]. One strength of MF is that it can incorporate implicit feedback that is not directly given but can be derived by analyzing the user behavior. MF algorithms work by decomposing the user-item matrix into the product of two lower dimensionality rectangular matrices. Much research effort has been devoted to enhancing MF, such as integrating it with neighbor-based models [8], combining it with topic models of item content [10], and extending it to factorization machines for generic modeling of features [11]. Despite the effectiveness of MF for collaborative filtering, it is well-known that its performance can be hindered by the simple choice of the interaction function--inner product [12]. This research integrates neural networks and a collaborative filtering method for CS item recommendations to solve the above difficulties. The proposed recommendation system includes two components: the denoising autoencoder (DAE)-based CS item rating (DACR) generator, and the neural network-based collaborative filtering (NNCF) predictor. The DACR generator is to derive the CS item ratings from similar non-CS items using auxiliary textual information. In the generator, the DAE, a neural network-based dimension reduction method, is applied to extract content features from item vectors. With the compact content feature vector, the rating of a CS item can be derived efficiently. The NNCF predictor is designed to deal with a sparse preference prediction problem. In the NNCF predictor, one hot-encoding is applied to convert the representation of users and items into binary sparse vectors. These long sparse vectors are then projected to a dense latent vector in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user-item matrix learning. When the target user is specified, the trained NNCF predictor will return the ratings of all items. The major contributions of this study are summarized below. First, we take the benefits of neural networks and a collaborative filtering approach for solving the CS item recommendation problem. To the best of our knowledge, it has been rarely studied before. Second, we apply the DAE, a neural network-based dimension reduction method, to extract the content features from vectors in the proposed DACR generator. The experiments show the proposed DACR generator can overcome the sparsity and redundancy of the high dimension vector and reduce much computational time when evaluating item similarity. Third, we perform experiments on real-world datasets and demonstrate the effectiveness of the proposed recommendation system. The remainder of this paper is organized as follows.