The rapid growth of online service platforms has significantly influenced the way users conduct daily activities. In response to the requirements of frequent online activities on huge information, recommendation has become one of the best ways for organizations, governments, and individuals to understand their users and promote their products or services. Effective recommendation of online items and online consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by business successes, academic research in this field has also been active for many years. However, there are still many research challenges in this area, such as the discovery of contexts, the sequential user behavior influence, the explainability of online system, the user interaction of system, and the big data management of online services. The highly dynamic network data on online platforms make these challenges even more critical. This special section focuses on the new recommendation solutions using AI and big-data techniques. We would like to invite authors to submit papers on all aspects of online recommendation techniques.
The list of possible topics includes, but is not limited to:
Authors can submit their manuscripts via ScholarOne Manuscripts. Reviewing will be single-blind. We will follow policies for plagiarism, submission confidentiality, reviewer anonymity, and prior and concurrent paper submission based on the publisher of TKDE.
For questions or more information, please contact the guest editors:
