Challenges in personalized authority flow based ranking of social media

As the social interaction of Internet users increases, so does the need to effectively rank social media. We study the challenges of personalized ranking of blog posts. Web search techniques are inadequate since social media lack many of the characteristics of the Web such as rich document content and an extensive hyperlink graph. Further, user behavior in social media has moved beyond keyword based search and must support users who follow a particular blog or theme. In this research, we extend a social media dataset to exploit the associations between authors, blog posts, and categories (topics) of the posts. We then apply personalized authority flow based ranking algorithms based on the random surfer model. We evaluate our personalization approaches through an extensive study on a range of virtual users whose preferences are defined based on intuitive criteria. Our evaluation shows that the accuracy of our personalized recommendations ranges from good to very good for a majority of users, and outperforms reasonable baseline approaches.