dc.description.abstract | Currently, although many recommended system applications are launched, usually these recommended applications are executed on the same platform. These single-platform recommendation systems face two challenges. The first problem is the lack of data that can be referenced and used in the recommendation system. For example, for the Facebook platform, the recommended material can only come from the Facebook community itself, not from Instagram. The second problem is the problem of insufficient number of users. For example, advertisers on Instagram can only send their ads to users on Instagram, not Facebook users. In response to these two problems, this paper proposes a cross-platform recommendation system from Facebook to Instagram. This has two advantages. First, the data of the two platforms can be integrated and complement each other, thereby greatly expanding the source and richness of recommended data. Second, Instagram advertisers can not only send ads to users on the same platform, but to Facebook users with the same preferences. This can help the system expand its customer base and help better target marketing. Finally, we use a series of experiments to prove the effectiveness of the entire method. Experimental results show that this method has a good effect on the similarity analysis of Facebook users and Instagram popular accounts, and the recommendation results also highly match the user′s preferences. | en_US |