## 6 Conclusions  
This systematic literature review aimed to provide a comprehensive understanding of the current state and future potential of AI-based recommender systems for e-commerce. We addressed various research questions, including the most-used AI-based techniques in recommender systems for e-commerce, their effectiveness, benefits and drawbacks, a comparison with traditional recommender systems, and the direction of research in this field. Our analysis revealed that the most common AI-based techniques employed in recommender systems include machine learning, deep learning, augmented reality, and virtual assistants. These techniques enable the improved relevance, personalization, and accuracy of recommendations. However, the effectiveness of AI-based recommender systems may vary depending on the specific application, user profiles, and recommendation contexts. The benefits of AI-based recommender systems for e-commerce include improved decision-making, reduced shopping duration and effort, increased sales, and the ability to overcome data sparsity and cold-start issues. However, our study did not provide enough evidence to determine the drawbacks of AI-based recommender systems, highlighting the need for further research in this area. While traditional recommender systems are still commonly used, advancements in AI-based recommender systems show promising results. Future research should focus on understanding the contexts in which AI-based recommender systems outperform traditional systems. The research direction in the field of AI-based recommender systems for e-commerce is moving towards more efficient, sophisticated, and accurate systems that can adapt to the changing needs and preferences of customers. Emerging research areas include sentiment analysis, embeddings, and the development of frameworks to reduce complexity and expedite the development of state-of-the-art recommender systems. AI-based recommender systems have the potential to significantly improve the e-commerce landscape. However, more research is needed to better understand their effectiveness, drawbacks, and optimal applications. By continuing to explore this rapidly evolving field, we can harness the full potential of AI-based recommender systems to enhance e-commerce experiences for both consumers and businesses. The use of AI and machine learning (ML) is often perceived as a \"black box\" approach; however, when combined with data science, predictive modeling, graph databases, semantic web, and natural language processing (NLP), these techniques have the potential to provide more transparency and explanatory power to the end-user. Contributions:  
1. We conducted a systematic literature review, providing a comprehensive understanding of the current state of AI-based recommender systems in e-commerce;
2. We analyzed the most commonly used AI-based techniques, their effectiveness, benefits, and drawbacks, offering insights into the practical implications of these systems;
3. We compared AI-based recommender systems to traditional systems, highlighting the need for further research to determine their optimal applications. Future Directions:  
1. Drawbacks of AI-based recommender systems: While our study identified various benefits of AI-based recommender systems, we found limited evidence regarding their drawbacks. Future research should explore potential challenges, limitations, and negative consequences associated with the adoption of AI-based recommender systems in e-commerce;
2. Integration with other technologies: AI-based recommender systems can be combined with other emerging technologies, such as blockchain, Internet of Things (IoT), and 5G networks, to create innovative solutions for e-commerce. Future research should explore these synergies and their potential impact on the e-commerce landscape. From the reviewed studies, it is evident that while RSs have evolved from collaborative filtering to AI, the core objective remains unchanged: to provide personalized recommendations and support to users.