## 5 Discussion
### AI-Based Techniques in Recommender Systems for e-commerce
#### 5.1.2 Augmented Reality, Deep Learning, Virtual Assistants  
Studies conducted in the field of augmented reality or virtual assistants are closely linked to the concept of intelligent agents. In the past decade, researchers have focused on developing personalized interaction techniques. Researchers [37] developed a deep neural network ranking model that utilizes user-specific features and their interactions, and dynamically re-ranks recommendations based on real-time user feedback. Other studies [38; 39] used a deep learning neural network to recommend movies. Other authors [40; 41; 42] combined neural networks with other AI techniques, using a sentiment score computation based on the combination of a support vector machine and artificial neural network, along with frequency computation on specific nutrition features, to recommend products. Some of the latest developments in the field of deep neural networks include deep reinforcement learning (DRL), automated machine learning (AutoML), and graph neural networks (GNNs) [11]. Studies involving image processing have also been conducted, with researchers exploring ways to recommend relevant images based on clothing and footwear dataset quality queries [43]. Additionally, researchers have investigated the potential for extracting meaningful information from images and applying it to online recommendation systems to enhance the online shopping experience [44; 45]. Eye-tracking experiments at supermarket shelves [46] were used to study virtual reality, while [47] explores deep learning, causality, and active exploration with bandits, and highlights business considerations and implementation challenges. Furthermore, recent discussions on graph embedding techniques for extracting the semantics of explicable recommendations can be found in [48].  
To provide a more organized and structured overview of the various studies in the field of augmented reality, deep learning, and virtual assistants, we present Table 8 below. The table includes information on the techniques and approaches employed in these studies, as well as the main outcomes and contributions of each research work. This table serves as a useful reference for readers who are interested in gaining an overview of the different methods used in this field and their potential benefits for improving the accuracy and personalization of online recommendations.  
The studies from this field aim to recommend relevant images to customers based on their queries, in order to improve the customer's shopping experience. The studies also explore the use of deep learning, eye-tracking experiments, graph-embedding techniques, and other methods to extract meaningful information from images. These studies demonstrate the potential benefits of using these techniques to improve the accuracy and personalization of online recommendations; however, the authors also mention the challenges that come with their implementation in a business context.  
The practical implications of our research can be significant in various fields, including decision-making, policymaking, and product development. Our findings can provide valuable insights and support evidence-based decision making by offering a clearer understanding of the subject under study.