recommender systems may have a positive or negative effect on sellers, depending on the precision of the recommender system, and [PERSON] [7] found that recommender systems may have a positive or negative effect on sellers, depending on the targeting precision of the recommender system. However, some authors [8] found that recommender systems have a positive effect on sales, while others [9] found that recommender systems may have a positive or negative effect on manufacturers, depending on the precision of the recommender system. Thus, the impact of AI-based recommender systems on the competitive landscape of electronic commerce remains unclear. Further research is required to establish the influence of such systems on the competitiveness of the e-commerce sector.  
Users are also evolving. They are becoming proactive and well-informed, and they prioritize privacy. As a result, it may require effort from sellers to establish trust with these users. Currently, blockchain technology is utilized to address trust and security issues [10], while deep learning is disrupting the recommendation process [11]. The focus of the research has shifted over time, having started with segmentation, clustering, and classification, then moving on to content-collaborative-filtering and user modeling. The focus is now on exploring neural graph-collaborative-filtering.  
The aim of this systematic literature review was to examine the use of artificial intelligence (AI) techniques in the design and implementation of recommender systems (RSs) for e-commerce. This systematic literature review aimed to provide a comprehensive overview of the current state of AI-based recommender systems in e-commerce. The AI-driven techniques covered in our review refer to the use of artificial intelligence methods and algorithms such as data science; machine learning methods such as clustering, classification, and association rule mining; deep learning approaches, including deep neural networks, convolutional neural networks, and recurrent neural networks; and augmented reality and virtual assistant technologies, such as chatbots and virtual shopping assistants used to enhance the accuracy and personalization of recommendations in e-commerce platforms.  
To the best of our knowledge, this type of research has not been conducted to date. One recent study that reviewed AI-based techniques for recommender systems mentioned that the use of advanced AI techniques, such as fuzzy techniques [12], transfer learning, and genetic algorithms, is expected to improve the accuracy of recommender systems [13]. Recently, authors [14] pointed out that AI in e-commerce mainly focuses on RSs, and that the main research themes are sentiment analysis, optimization, trust, and personalization. Although sentiment analysis is an AI technique, there is a lack of research on the AI techniques utilized for optimization, trust, and personalization in RSs. Our focus was on studying AI-based techniques for recommender systems in e-commerce. In particular, the present study aimed to not only analyze the AI techniques used in recommender systems, but also to evaluate their effectiveness. While many articles have been published on the topic, few have rigorously examined the performance of these techniques. Therefore, this study sought to fill this gap by conducting a comprehensive evaluation of various AI-based recommender-system approaches. We believe that such a study will provide valuable insights into current user needs and help to predict future trends in this field.