The invention discloses a
pairing type activity recommendation method based on internet
big data label analysis, belongs to the technical field of activity recommendation, and aims to solve the problems that a lot of time is spent in finding favorite activities, and when the name of the activities is tense, the activities to be applied are frequently found, but the name of the activities is full, and the activity recommendation efficiency is high. Or the
exposure rate of some activities is not enough, so that the number of registered people is small, and the activities cannot be normally carried out. Through large-scale accurate positioning of users, full resources of operators are improved, working efficiency and product conversion rate are improved, corresponding activity contents can be effectively matched according to
label contents, inconvenience of one-way searching is reduced, which activities are loved by which people can be counted and analyzed, dominant labels can be autonomously defined, and the user experience is improved. According to the method, activity selection can be effectively and visually carried out, so that activity matching is carried out on users meeting conditions, the problem that
bidirectional search of the users and the activities is difficult is solved, and activity matching accuracy is guaranteed.