Paper Info
Title
Predict Whom One Will Follow: Followee Recommendation in Microblogs
Abstract
Microblogging services such as Twitter and Tencent Weibo have enjoyed drastic popularity in the latest few years. Recommender is essential to those microblogs as a means to find items (users or other information sources such as organizations) that might interest a user to follow. It can greatly improve user experience as well as reduce the risk of information overload might be introduced by irrelevant followees. In this paper, we examine some of the most influential factors that user might consider in selecting followees, in the hope of recommending interesting items to match each user's preferences. We investigate a large scale microblog data extracted from Tencent Weibo and conduct the evaluation of recommendations based on the guideline proposed by the challenge of Track 1 in KDD Cup 2012. Statistical analysis of the log of user actions regarding to recommendations reflect only about 7% acceptance. Experimental results show the popularity of an item is more attractive to users than other features such as the matching of item category, keywords and the influence of user actions and current followees' acceptance.
Year
DOI
Venue
2012
10.1109/SocialInformatics.2012.74
SocialInformatics
Keywords
Field
DocType
drastic popularity,microblogging,current followees,statistical analysis,microbolgs,large scale microblog data,microblogging services,item category,kdd cup 2012,twitter,data analysis,recommendation,recommender systems,interesting item,user actions log,irrelevant followees,information overload,follower,tencent weibo,followees acceptance,user experience,user action,followee,social networking (online),information source,followee recommendation
Recommender system,World Wide Web,Information overload,Internet privacy,User experience design,Social media,Computer science,Microblogging,Popularity,Statistical analysis
Conference
ISBN
Citations 
PageRank 
978-1-4799-0234-7
8
0.63
References 
Authors
8
3
Name
Order
Citations
PageRank
Hao Wu1424.45
Vikram Sorathia2628.96
Viktor K. Prasanna37211762.74