## 2 Theoretical Background  
Recommender systems are a technology used not only in e-commerce but in other areas as well. The technology employed can vary, and ranges from decision rules to artificial intelligence virtual assistants and personalized recommender systems, notifications, emails, or applications. There are several approaches, including collaborative filtering, context-aware filtering, and hybrid approaches. Collaborative filtering leverages the historical behavioral data of a user community to identify patterns of preferences [15]. They can operate in memory or offline and utilize linear methods, such as matrix factorization (MF) and graph-based methods, as well as nonlinear methods, such as deep neural networks (DNN), among others. Context-aware recommender systems incorporate both contextual information about the user and historical data about the products. In the field of recommender systems, there is a growing interest in understanding the interplay between various aspects, such as behavioral research, intelligent systems, decision support systems, e-commerce, augmented reality, deep learning, and virtual assistants. AI-driven techniques play a pivotal role in enhancing the capabilities of these aspects, leading to more effective recommender systems. For example, intelligent systems powered by AI can process large datasets to provide accurate and personalized recommendations based on user behavior, preferences, and decision-making processes. Augmented reality has the potential to enhance the user experience by providing a more immersive and interactive recommendation process. Deep learning algorithms, particularly those based on neural networks, have proven to be effective in handling large and complex datasets, making them well-suited for recommender systems. Virtual assistants, such as Amazon's Alexa and Google Assistant, are becoming increasingly popular, and provide users with personalized recommendations and information at their fingertips. Each of these aspects brings unique strengths and challenges to the field of recommender systems, and a comprehensive understanding of their interplay is crucial for the development of effective and innovative recommender systems. For example, while intelligent systems can provide highly accurate recommendations, they may not always align with the user's preferences and decision-making processes. On the other hand, behavioral research can provide valuable insights into user behavior and decision-making processes but may not always have the ability to handle large and complex datasets effectively. The Scopus database offers valuable insights into the topic of e-commerce, with 41,000 articles on \"e-commerce\" and 68,000 articles (many duplicates) on \"electronic commerce.\" This extensive collection of literature reveals that the widespread adoption of e-commerce can be attributed to advancements in the Internet, personal computers, big data, cloud computing, and AI, with AI playing an increasingly important role in driving innovation and growth in the e-commerce sector. Although the number of articles on the subject has fluctuated over time, with the largest surge between 2008-2011, the number of articles has increased again since 2019. Interestingly, the computer science field has nearly four times as many articles as the business field on this topic, reflecting the multidisciplinary nature of e-commerce research. To gain a general understanding of the field, we conducted a search using the Scopus database to investigate the co-occurrence of keywords in e-commerce research. Figure 1 depicts the co-occurrence of various keywords, including \"recommendation systems,\" \"electronic commerce,\" \"data mining,\" \"deep learning,\" \"machine learning,\" \"behavioral research,\" and \"collaborative filtering,\" among others. The list of articles considered for the analysis consisted of 2000 publications related to e-commerce published within the last five years.