# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 1 Introduction  
Nowadays, consumers have a large variety of possibilities, such as books, restaurants, movies, and other entertainment options, thanks to the rapid growth of internet services, which has resulted in a problem of information overload [1]. Recommender Systems (RSs) are algorithms that contribute to the resolution of the problem of information explosion. They are smart algorithms that use the past preferences of a user to suggest a similar product to other users [2]. Therefore, using efficient and accurate RSs is not questionable. In a personalized recommendation system, recommendations are based on past interactions between the user and the item, either explicit or implicit feedback interactions called collaborative filtering (CF). This type of recommendation is often used since it effectively captures users' preferences. However, the collaborative filtering technique has data sparsity problems [3]. Additionally, due to their inability to use the item content, CF algorithms have a limited scope for producing explanations for the recommendations [4]. To overcome these, researchers have incorporated additional attributes into collaborative filtering. Ref. [5], for example, integrates user and item attributes, whereas [6] used contexts as additional information to improve performance. According to collaborative filtering methods, users may be curious about items selected by people with comparable interaction habits to them. To implement CF-based methods, we need interaction data from various sources, such as users and items, and then we form a user-item interaction matrix. Memory- and model-based approaches are two commonly used collaborative filtering-based techniques [1]. Memory-based (also called neighborhood-based) CF makes predictions based on the nearest neighborhoods. Neighborhood-based methods are further classified into two types: user-based methods and item-based methods [7]. User-based CF will give a recommendation based on the user's past interaction history. On the other hand, item-based CF learns the relationships between items and recommends comparable items to a user based on their interaction history. In model-based CF, the latent factor model (LFM) is one of its variants, which captures the user and item's latent representations from high-dimensional data [8]. The matrix factorization (MF) technique is one of the successful and efficient methods in LFM. This method decomposes high-dimensional data into low-rank data [9], and the method projects the latent factors into a shared latent space. Then, it predicts users' preferences for items in this space by taking the inner product between user latent and item latent vectors [10; 11]. To improve the efficiency of matrix factorization, several academics have conducted a range of studies. For example, the work given in Ref. [12] uses additional features like item content to address the problem of sparsity, whereas [13; 14] combine, social matrix factorization with social relations to boost recommendation quality. Despite its high quality, it has low computational performance for large amount of data, scalability problems for sparse data, and information extraction problems for non-linear data. When there is a lack of rating data, matrix factorization frequently results in poor recommendation quality. Contrarily, a large amount of data will make computing more difficult. As a result, it is necessary to look for various alternative models to increase the quality of recommendations. Deep learning (DL), a new discipline, has achieved astounding success in several domains, especially in natural language processing [15; 16; 17]. Recently, recommendation systems have benefited from the deployment of DL, which provides new possibilities for enhancing the effectiveness of RSs. One benefit of deep learning is its ability to handle non-linear data representations. It can boost computational power and handle massive amount of data [18]. It can also aid in solving sparsity problems, which results in higher recommendation quality and performance [19].