Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Ranking Approaches for Microblog Search
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Twitter displays the tweets a user received in a reversed chronological order, which is not always the best choice. As Twitter is full of messages of very different qualities, many informative or relevant tweets might be flooded or displayed at the bottom while some nonsense buzzes might be ranked higher. In this work, we present a supervised learning method for personalized tweets reordering based on user interests. User activities on Twitter, in terms of tweeting, retweeting, and replying, are leveraged to obtain the training data for reordering models. Through exploring a rich set of social and personalized features, we model the relevance of tweets by minimizing the pairwise loss of relevant and irrelevant tweets. The tweets are then reordered according to the predicted relevance scores. Experimental results with real twitter user activities demonstrated the effectiveness of our method. The new method achieved above 30% accuracy gain compared with the default ordering in twitter based on time.