Association Rules Mining Among Interests and Applications for Users on Social Networks

Abstract

Interest is an important concept in psychology and pedagogy and is widely studied in many fields. Especially in recent years, the widespread use of many interest-based recommendation systems has greatly promoted research on interest modeling and mining on social networks. However, the existing studies have rarely tried to explore the relationships among interests and their application value, and most similar studies analyze user behavior data. In this paper, we propose and verify two hypotheses about the interests of social network users. We then use association rules to mine users' interests from LinkedIn users' profiles. Finally, based on the interest association rules and user interest distribution on Twitter, we design an approach to mine interests for Twitter users and conduct two experiments to systematically demonstrate the approach's effectiveness. According to our research, we found that there are a large number of association rules between human interests. These rules play a considerable role in our method of interest mining. Our research work not only provides new ideas for interest mining but also reveals the internal relationship between interest and its application value. The research work has certain theoretical and practical value.