# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 1 Introduction
In general, the work done by [20] has mentioned several specific benefits of deep learning. It allows for the modeling of non-linearity in data using a variety of non-linear activation functions. Deep neural networks (DNNs) enable models to incorporate a variety of information while reducing the time required for feature generation. In this article, we look at the advantages of deep learning architecture and provide a new approach for improving RS performance. We present such a dual DL and embedding-based latent factor model for recommender systems, (shortened as DELCR). To improve recommendation performance, DELCR employs embedding and deep neural network architectures to determine the latent factors. The model combines known deep learning and latent factor models to extract deep abstractions and non-linear feature representations of the data for rating prediction. The core idea is to map the dense user and item vectors generated by embedding techniques into two fully connected deep neural network (DNN) architectures. We used the concept from the work cited in [8]. For the proposed model, we embed the interaction rating matrix to obtain a dense vector that overcomes the sparsity problem, and we developed the model to predict the rating score. In general, in most existing deep learning-based recommender system designs, input feature vectors are concatenated and projected into a common DNN architecture to learn the abstraction of the important features. According to our argument, the approach is insufficiently resilient to extract the required information from the data. Because users have naturally complex abstractions, we must understand these phenomena separately to obtain the appropriate information. We argue that such a strategy is insufficiently robust to learn the required features from the data. Existing approaches did not employ separate architectures for these two different elements. However, the proposed model transforms the dense feature vector obtained from embedding techniques into separate deep neural networks. In addition, we will integrate the inner product into the network's output layers for the sake of obtaining the interaction rating results. The newly designed model, DELCR, efficiently learns the sparse data set in the embedding layer and the non-linear feature of the data in their corresponding DNN structure, which results in the quality of the recommendation. Thus, we can infer that the use of these two separate architectures will make a significant difference in the performance of the model. The main contributions of our work are as follows:  
* We develop a dual DNN structure to extract the non-linear representation of users' and items' latent factors in their respective spaces separately. * We construct a robust DL and embedding-based latent factor model. The method effectively alleviates sparsity problems by combining deep learning and embedding techniques. * We conduct an extensive experiment on two real-world datasets to predict the rating score. The result demonstrates that DELCR improves the performance of the recommendation to the state-of-the-art level. The remainder of the paper is organized as follows: Section 2 includes preliminary work, while Section 3 includes related work. Section 4 presents the proposed model. Then, the experimental result is described in Section 5. Finally, the conclusion is mentioned in Section 6.