Making recommendations in a microblog to improve the impact of a focal user

Authors:
Shanchan Wu;Leanna Gong;William Rand;Louiqa Raschid
Affiliations:
University of Maryland, College Park, College Park , MD, USA;University of Maryland, College Park, College Park, MD, USA;University of Maryland, College Park, College Park, MD, USA;University of Maryland, College Park, College Park, MD, USA
Venue:
Proceedings of the sixth ACM conference on Recommender systems
Year:
2012

Citing 5
Cited 0

Maximizing the spread of influence through a social network

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks

CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Sampling from large graphs

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Walking in facebook: a case study of unbiased sampling of OSNs

INFOCOM'10 Proceedings of the 29th conference on Information communications
Scalable influence maximization for prevalent viral marketing in large-scale social networks

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

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.