Abstract
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.
Highlights
The concept of e-commerce is defined as the conduct of commercial transactions online
This study aims to show the improvements of the accuracy in recommendations using image-based Recommender systems (RS) for e-commerce
The paper proposes an image-based recommendation method based on convolutional neural networks using five pre-trained models
Summary
The concept of e-commerce is defined as the conduct of commercial transactions online. It refers to the sale, delivery and payment of goods and services online. It includes various technologies, including mobile commerce, electronic funds transfers, supply chain management, Internet marketing and online transaction processing (Wigand 1997, Turban et al 2002). Most of the e-commerce platforms use some technologies such as features of social networks (Sharif et al 2013), expert clouds (Navimipour et al 2015, Hazratzadeh and Jafari Navimipour 2016), mobile facilities, big-data mining results, and even machine learning concepts to satisfy their customers and gain stability in the business world. Deep learning methods are widely applied to e-commerce recommender systems (Da’u and Salim 2019, Shoja and Tabrizi 2019)
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