# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 1 Introduction
An important barrier is the fact that not every store owner has specialist knowledge that will make the recommendation system high-quality and bring the expected results. By introducing a low-quality recommendation system, we will be recommending products to the customer that are off-limits. The cost of building a dedicated system for the store, which would use methods similar to those used by, for example, Amazon.com, will be associated with very large expenses on research and development, which is a significant problem for owners of most online stores. Systems may be an alternative that offer high-quality personalized recommendations in the form of SaaS (Software as a Service). An example of such a system is the Quartic system, a product introduced by the Polish company Data Force Group. These recommendations can be formed using different artificial intelligence techniques and approaches, particularly the technology of deep learning, that may be used to model this type of system. Nowadays, the abundance of online shops and the large numbers of products makes exploring all products almost impossible. Clients face significant difficulty in exploring products that are irrelevant to their needs. Recommendation systems have been developed to calculate such probabilities or to determine the extent to which a product and a user match each other. A smart referral system is a powerful selling tool for any e-commerce platform [3]. As early as 2006, 35% of Amazon's revenue should have been generated by product recommendations. Netflix held a $1 million prize money competition to improve its referral system in the same year, prompting many researchers to address the issue and develop new recommendation algorithms. In e-commerce, typical data records consist of historical customer transactions, so here the focus is on the forecast of purchase probabilities instead of valuation points. However, studies of such sparse implicit ratings have been reported, but rarely performed due to the lack of publicly available e-commerce records. As an outcome, RS has become a functional field of research, and significant advances were made as far as expanding the forecast and positioning into the precision of recommendations is concerned [4]. Recommendation systems are dependent mostly on user tendencies and past cooperation [5]. The proposed models are, for the most part, arranged into cooperative sifting and content-based recommendation frameworks based on the kinds of information available [6]. Deep learning has gained huge promotion and, over the past decade, an accomplishment of intense learning in numerous application areas. The scholarly world and businesses have been rushing to implement profound answers about how to incorporate a more extensive scope of uses based on its ability to unveil numerous unpredictable undertakings [7]. The proposed method differs from other similar work proposed by [6,8] [NO_PRINTED_FORM]. The proposed work analyzes sentiment analysis and deep learning approaches specific to e-commerce systems. The goal of this review is to identify approaches used in building e-commerce recommendation systems. Other studies focused on cross-domain recommendation systems to figure out why each technique based on deep learning is used in recommendation systems. It also attempts to provide awareness of the solutions that are based on deep learning that have been provided to contemporary difficulties in the e-commerce field.