Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Small Worlds: The Dynamics of Networks between Order and Randomness
Small Worlds: The Dynamics of Networks between Order and Randomness
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Propagation Models for Trust and Distrust in Social Networks
Information Systems Frontiers
Lightweight Distributed Trust Propagation
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
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
A Space and Time Efficient Algorithm for SimRank Computation
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Mining Twitter in the Cloud: A Case Study
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Combining provenance with trust in social networks for semantic web content filtering
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
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Social recommender systems aim to alleviate the information overload problem on social network sites. The social network structure is often an important input to these recommender systems. Typically, this structure cannot be inferred directly from declared relationships among users. The goal of our work is to extract an underlying hidden and sparse network which more strongly represents the actual interactions among users. We study how to leverage Twitter activities like micro-blogging and the network structure to find a simple, efficient, but accurate method to infer and expand this hidden network. We measure and compare the performance of several different modeling strategies using a crawled data set from Twitter. Our results reveal that the structural similarity in the network generated by users' retweeting behavior outweighs the other discussed methods.