REAL TIME ADVERTISING ON SOCIAL NETWORKS

Abstract

The availability of social networking platforms (SNPs) has led to a rapid growth of social media marketing (SMM) deployments. The advantages of SMM over traditional marketing strategies are two folds. First, user preferences can be quickly extracted from user profiles and activities on SNPs and relevant advertisements are then effectively posted to targeted users. Second, SNPs provide extremely convenient ways to share information between friends or followers. Thus viral marketing approaches would be more effective on SNPs as it facilitates ease of sharing. Although many efforts have been made to address utilizing each of the aforementioned benefits of SMM to assist effective online advertisements, few works have been conducted to combine the merits from both perspectives to enable more intelligent SMM approaches. Thus, this thesis is devoted to design innovative solutions which leverage both user profiles and sharing behaviors for more advanced SMM strategies.