Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Positional language models for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Computational community interest for ranking
Proceedings of the 18th ACM conference on Information and knowledge management
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Extracting User Interests from Search Query Logs: A Clustering Approach
DEXA '10 Proceedings of the 2010 Workshops on Database and Expert Systems Applications
Breaking News Detection and Tracking in Twitter
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
LikeMiner: a system for mining the power of 'like' in social media networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Happiness is assortative in online social networks
Artificial Life
Ranking news events by influence decay and information fusion for media and users
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the 22nd international conference on World Wide Web companion
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Users frequently express their information needs by means of short and general queries that are difficult for ranking algorithms to interpret correctly. However, users' social contexts can offer important additional information about their information needs which can be leveraged by ranking algorithms to provide augmented, personalized results. Existing methods mostly rely on users' individual behavioral data such as clickstream and log data, but as a result suffer from data sparsity and privacy issues. Here, we propose a Community Tweets Voting Model (CTVM) to re-rank Google and Yahoo news search results on the basis of open, large-scale Twitter community data. Experimental results show that CTVM outperforms baseline rankings from Google and Yahoo for certain online communities. We propose an application scenario of CTVM and provide an agenda for further research.