@conference {19170,
	title = {Making Recommendations in a Microblog to Improve the Impact of a Focal User},
	booktitle = {6th ACM Conference on Recommender Systems (RecSys)},
	year = {2012},
	abstract = {We present a microblog recommendation system that can help monitor users, track conversations, and potentially improve diffusion impact. Given a Twitter network of active users and their followers, and historical activity of tweets, retweets and mentions, we build upon a prediction tool to predict the Top K users who will retweet or mention a focal user, in the future [10]. We develop personalized recommendations for each focal user. We identify characteristics of focal users such as the size of the follower network, or the level of sentiment averaged over all tweets; both have an impact on the quality of personalized recommendations. We use (high) betweenness centrality as a proxy of attractive users to target when making recommendations. Our recommendations successfully identify a greater fraction of users with higher betweenness centrality, in comparison to the overall distribution of betweenness centrality of the ground truth users for some focal user. },
	author = {Wu,Shanchan and Gong, Leanna and Rand, William and Raschid, Louiqa}
}
