Artificial intelligence and recommender systems in e-commerce. Trends and research agenda

• Data mining is crucial for extracting and transforming relevant data in recommender systems and AI applications in e-commerce. • The tool facilitates the study of behavioral patterns, user identification, trend identification, and hidden relationships in large datasets. • Sentiment analysis is vital in recommendation systems and AI in e-commerce for identifying hidden user preferences and satisfaction/dissatisfaction patterns. • RNNs are of significant interest in recommender systems and AI for their ability to model data streams, such as purchase history and browsing behavior. Combining recommendation systems and AI in e-commerce can improve the user experience and decision-making. This study uses a method called bibliometrics to look at how these systems and artificial intelligence are changing. Of the 120 documents, 91 were analyzed. This shows a growth of 97.16% in the topic. The most influential authors were Paraschakis and Nilsson, with three publications and 43 citations. The magazine Electronic Commerce Research has four publications and 60 citations. China is the top country for citations, with 120, followed by India with 25 publications. The results show that research increased in 2021 and 2022. This shows a shift towards sentiment analysis and convolutional neural networks. The identification of new keywords, such as content-based image retrieval and knowledge graph, shows promising areas for future research. This study provides a solid foundation for future research in e-commerce recommender systems. Source: own elaboration.