Guest Editorial Special Issue on Online Recommendation Using AI and Big Data Techniques

Lei Chen, Xiangmin Zhou, Xiaochun Yang, Timos Sellis

Abstract: The rapid growth of online service platforms has greatly influenced the way users conduct daily activities. In response to the requirements of frequent online activities, recommendation has become one of the best ways for the organizations, governments and individuals to understand their users and promote their services. Effective recommendation of online items has become critical in domains such as e-commerce and online media. Driven by the business success, academic research in this field has been active for many years. However, there are still many research challenges, such as context discovery, sequential user behavior influence, explainability and user interaction of system, big service data management. Especially, the highly dynamic online network data make these challenges even critical. Due to the high pressing interest and challenges in this area, this special issue is devoted to this topic, and focuses on the new solutions using AI and Big Data techniques.