(or latent factor) branch has shown its effectiveness on extremely sparse interaction matrices. The main idea is to project each user and item into a lower dimension space, then analyze the user-item interaction by dot product [6; 7; 8]. Several studies have shown that an appropriate initialization setting for matrix factorization can improve the speed and accuracy of matrix factorization models [9; 10; 11]. However, there are usually not enough transaction data to make accurate recommendations for a new user or item, which raises the cold-start problem in CF techniques. Therefore, hybrid methods are proposed to tackle this problem by combining both content-based and CF models [12; 13]. Generally, hybrid methods integrate user-item ratings and auxiliary information to generate unified systems. As in [12], user profiles, movie genres and their past interactions are integrated into one model to predict dyadic response in a generalized linear framework. [PERSON] et al. [13] proposed a hybrid approach that utilizes Singular Value Decomposition (SVD) to make television program recommendations to deal with the limitations of content-based and CF systems. One restriction of these works is the privacy of user profiles, which limits the shared personal information. In another work [7], after proving the advantage of SVD++ model, an integrated model between neighborhood-based and SVD++ is established to gain a better result by generating new representation for a user from the items rated by that user instead of using an explicit parameterization. A model named Factorization Machines (FM) combining matrix factorization and Support Vector Machine also uses both ratings and auxiliary information for predictions [14]. Studies on hybrid approaches for initialization problem in matrix factorization models have gained some attention recently. [PERSON] and [PERSON] [15] used the similarity between users and items to initialize feature vectors by taking advantage of available contextual information. Additionally, [PERSON] et al. [16] adopted the item's attributes information to initialize the item feature matrix in SVD++ model; however, this method just accounted for only initializing item features and the overall improvement is modest. The common point of these methods is that features such as movie genres are considered as good representations of items. Nonetheless, in practice, raw content-based information needs to be preprocessed and extracted carefully to fit into a specific RS, especially when the contents of products are texts, images or videos which are hard to exploit semantic and meaningful representations. While traditional methods have yielded promising results, the performances are still restricted by theirs linearity. For real-world data structure, deep learning can be a powerful approach to boost RSs owing to its outstanding capability to explore non-linear correlations between data features [17; 18]. Among a variety of deep learning approaches, CNN provides an efficient neural network and is considered as an excellent feature extractor [19]. To find the correlation between user and songs, a music RS [20] was proposed where CNN was used to extract song's latent features from the audio data. Additionally, CNN was trained to transform item description documents into an embedding space, which later can be incorporated into CF models [21]. In the interest of CNN and its advantages, this paper concentrates on applying 1D-CNN to enhance the ability to extract information of a classic autoencoder and provide scrupulous features to RSs. The result is followed by a novel method of integrating content-based information to matrix factorization models. Our empirical studies are conducted on the MovieLens 20 M dataset released in October 2016 [22]. Although the dataset includes a current copy of a movie Tag Genome based on user-contributed information which is often considered as content-related information, there are no statistics about user profiles.