# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 1 Introduction  
Most e-commerce marketing success is due to the application of recommendation systems that personalize the customer experience in the e-commerce sector. The e-commerce recommendation system can assist platforms in providing the most relevant data to customers at the most suitable time, enhancing consumer loyalty, and providing a more comfortable purchasing experience. An example of a recommendation system is presented in Figure 1. Accuracy of the recommendation outcomes is a critical aspect in deciding whether the RSS succeed or failed [1]. Figure 1: General view of the recommendation system. In previous years, especially during the years of the pandemic (COVID-19), as well as at present, the popularity of internet shopping has increased, and this has resulted in an explosion of data and information on the World Wide Web [2]. This creates some difficulty for users when choosing the product that is useful to them and that matches their tastes, behaviors, and estimations, because there are hidden items that simply cannot be found. Therefore, there must be a connecting link or bridge to connect users and items by structuring similarities between users or between items or between items and users together. For this purpose, the multi-directional recommendation system was born, started to grow, and played an important role in many areas such as economics, learning, social media, information systems, etc., and designing a predictive model that can estimate prediction function is the main goal of such systems [3]. Currently, in the global e-commerce market, we can observe a gradual increase in the number of online stores that introduce advanced personalization mechanisms. Until recently, they were a novelty, available only to e-commerce giants such as Amazon.com, which was one of the first retailers to use personalized recommendation techniques in its store. In recent years the number of vendors who have started to develop offerings has increased significantly, therefore both large and smaller online stores can afford the personalization of their offers. Many reports and studies, including \"E-commerce in Poland 2010. E-shops.\", indicate that personalized recommendations are key to increasing sales. Voivici's research shows that in the United States more than half of online store owners (57%) offer recommendations that match the client's profile, and 25% intend to introduce them to their business. On the other hand, in Poland, only every third e-shop offers simple recommendations for products such as \"customers who bought also bought\". Personalized product recommendations bring great benefits, both for customers who get what they are looking for right away and for sellers who have a chance to increase revenue. In addition, long-term benefits are also significant, such as a higher degree of customer engagement, which will result in customer loyalty in the long run. Intelligent systems of personalized recommendations automatically tell us what we may like, try to adapt the content of the website to our preferences when we view it, and assess our preferences using many complex algorithms and techniques, including artificial intelligence. Personalized recommendation systems are not only used in online stores, they are also used wherever we want to increase parameters, such as sales, the user's stay on the website, their satisfaction with the right choices, and tailoring the offer individually to the needs of a specific customer. The latest research conducted by Gemius in 2010 shows that maintaining a regular, satisfied customer requires five times lower financial outlays than acquiring a new customer. Having a loyal, satisfied customer is worth its weight in gold, so it is worth trusting and investing in personalized recommendation systems. One of the most important factors for the success of e-stores is intelligent e-commerce support systems because they reliably and accurately help to recommend product ranges to specific users.