Recommending Who to Follow on Twitter Based on Tweet Contents and Social Connections

• 作者: Evgenia Tsourougianni Nicholas Ampazis
• 来源期刊: 社交网络(英文)
• 学科: 工学
• 关键词: RECOMMENDER Systems SOCIAL Networks PERSONALIZATION
• 年，卷（期）: 2013,（4）
• 页码范围: 165-173
• 页数: 9页
• 分类号: TP39

摘要

In this paper, we examine methods that can provide accurate results in a form of a recommender system within a social networking framework. The social networking site of choice is Twitter, due to its interesting social graph connections and content characteristics. We built a recommender system which recommends potential users to follow by analyzing their tweets using the CRM114 regex engine as a basis for content classification. The evaluation of the recommender system was based on a dataset generated from real Twitter users created in late 2009.