Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

Recommender systems (RS) are designed to eliminate the information overload problem in today's e-commerce platforms and other data-centric online services. They help users explore and exploit the system's information environment utilizing implicit and explicit data from internal e-commerce systems and user interactions. Today's product catalogues include pictures to provide visual detail at a glance. This approach can effectively convert potential buyers into customers. Since most e-commerce stores use product images to promote, arouse users' visual desires and encourage them to buy products, this paper develops an image-based RS using deep learning techniques. To perform the research, we use five convolutional neural network (CNN) models to extract the features of the products' images. Then, the system uses the features to calculate the similarity between images. The selected CNN models are VGG16, VGG19, ResNet50, Inception V3 and Xception. We also analysed four versions of the MovieLens dataset to demonstrate the accuracy improvement of the recommendations, including 100k, 1M, 10M and 20M. Results of the experiment showed a significant increase in accuracy compared with traditional approaches. Also, we express many related open issues including use of multiple images per item, different similarity metrics, other CNN models, and the hybridization of image-based and different RS techniques for future studies. This method also provides more accurate product recommendations on e-commerce platforms than traditional methods.