## 2 Related Works  
As introduced in the previous section, several researchers have investigated the usage of deep learning in the large-scale distribution field. In [5], the authors proposed a supervised deep pipeline for out-of-stock (OOS) detection based on visual assessment of the retail shelves. Intelligent downstream classifiers are used to detect predictive labels of the OOS cases. As a classification approach, the authors proposed a support vector machines method. The collected experimental results showed an accuracy of 84.5% for OOS and a sensitivity of 86.6% for label detection. In [6], an interesting approach has been proposed. The authors implemented an efficient pipeline for solving OOS problems inside commercial refrigerators. Through visual features extracted by cameras inside the refrigerators, deep Faster R-CNN and Single Shot Multibox (SSD) models were used as object detectors. After object detection, the K-mean clustering algorithm was used to group objects on the same shelves. The distance between objects on the same shelf was used as a metric for assessing the OOS. In [7], the authors proposed a solution for managing new commercial products. This solution will help businesses: automatically assign the new products to the right category while performing sales predictions even with almost no transactional history. A semantic-based deep learning pipeline will be used to assess product demand forecasting. The interesting results are detailed in [7]. In [8], an interesting shopping management system was proposed. The authors of [8] described a novel shopping recommendation system based on deep learning, combined with an ad hoc designed recommendation algorithm. The system extracts the charac teristics of users and commercial products through an artificial intelligence (AI)-based approach, proposing a coupled recommendation system based on user characteristics and product similarity. The results collected in [8] confirmed the effectiveness of the implemented approach. In [9], the authors presented a deep learning-based model for product purchase time forecast. Experimental results confirmed the robustness of the designed predictive pipeline. In [10], a novel approach was introduced. In [10], [PERSON] et al. proposed a smart shopping cart with self-checkout, called iCart, to improve customer experience at retail stores. The method is based on the usage of cloud computing and deep learning architectures. In iCart, the video events of the market were captured and sent to the cloud-based analyzer, which performs classification and segmentation of the video frames, extracting the shopping details to be analyzed. The system has been evaluated using real-world checkout video, showing an accuracy of 97% in shopping product detection. Further research efforts have been deployed to the development of such solutions suitable to reconstruct customer sentiment or to improve customer shopping methods. In [11], the authors proposed a novel approach to shopping style. The proposed approach needs the customer to install a mobile app. Customer behavior is tracked both at the entrance and at the exit of the market shop. For more details, see [11]. In [12], a sentiment weighted multi-classification method was proposed. The authors implemented an approach in which the sentiment is represented by vocabulary, entity and nodes. Directed weighted links represent the sentiment similarity between two nodes of entities with attributes and are determined by the direct correlation calculation between them. The paths are all connected. Each different path is a different sentiment expression, which represents a different customer sentiment expression. An interesting evaluation of transformer-based architecture BERT has been made by the author in order to analyze the vocabulary. Experiments and analysis confirmed that the proposed approach can classify the review sentiments with good accuracy and high efficiency. The authors applied their approach in the e-commerce scenario, but that method can be extended in other shopping scenarios.