Abstract
In the era of the information explosion, big data are always around us. Academic big data are defined as a large amount of data generated in the life cycle of all academic activities, which usually contains a large amount of academic information. Academic conferences can effectively promote academic exchanges among scholars. In recent years, academic conferences in various fields have been held around the world. However, with the increase in the number of academic conferences, the quality of conferences and the efficiency of hosting and participating in conferences are uneven. In today's fast-paced life, high-quality and efficient academic conferences have become the first choice of scholars. In this paper, a conference recommendation method based on a big data analysis of users' interests and preferences is proposed to help users choose high-quality academic conferences and to help organizers reduce conference costs and improve the conference operation efficiency. The method first divides the research fields of user-related academic conferences into three categories: the fields that users are interested in, the fields that users attend, and the research fields that users follow up. Then, the weights of these three categories are set, and the importance of each category recommendation related to the user is calculated. Finally, the conference recommendation index is calculated and several conferences with a high recommendation value are recommended to users. The experimental results show that the proposed conference recommendation method provides a convenient and fast service to conference participants and conference organizers. The developed big data platform can significantly improve the operation and participation efficiency of academic conferences, reduce the costs, and give full play to the role and value of academic conferences.