Approximating matchings in parallel
Information Processing Letters
A simple randomized parallel algorithm for maximal f-matchings
Information Processing Letters
Beyond the flow decomposition barrier
Journal of the ACM (JACM)
Constrained multi-object auctions and b-matching
Information Processing Letters
Approximation algorithms
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Improved distributed approximate matching
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
Graph construction and b-matching for semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Graph Twiddling in a MapReduce World
Computing in Science and Engineering
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Proceedings of the 19th international conference on World wide web
Fast algorithms for finding matchings in lopsided bipartite graphs with applications to display ads
Proceedings of the 11th ACM conference on Electronic commerce
Distributed fractional packing and maximum weighted b-matching via tail-recursive duality
DISC'09 Proceedings of the 23rd international conference on Distributed computing
Algorithmica - Special Issue: Matching Under Preferences; Guest Editors: David F. Manlove, Robert W. Irving and Kazuo Iwama
Data-Intensive Text Processing with MapReduce
Data-Intensive Text Processing with MapReduce
A model of computation for MapReduce
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Document Similarity Self-Join with MapReduce
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
B-Matching for spectral clustering
ECML'06 Proceedings of the 17th European conference on Machine Learning
Densest subgraph in streaming and MapReduce
Proceedings of the VLDB Endowment
Computing n-gram statistics in MapReduce
Proceedings of the 16th International Conference on Extending Database Technology
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
The family of mapreduce and large-scale data processing systems
ACM Computing Surveys (CSUR)
A distributed algorithm for large-scale generalized matching
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Matching problems are ubiquitous. They occur in economic markets, labor markets, internet advertising, and elsewhere. In this paper we focus on an application of matching for social media. Our goal is to distribute content from information suppliers to information consumers. We seek to maximize the overall relevance of the matched content from suppliers to consumers while regulating the overall activity, e.g., ensuring that no consumer is overwhelmed with data and that all suppliers have chances to deliver their content. We propose two matching algorithms, GreedyMR and StackMR, geared for the MapReduce paradigm. Both algorithms have provable approximation guarantees, and in practice they produce high-quality solutions. While both algorithms scale extremely well, we can show that Stack-MR requires only a poly-logarithmic number of MapReduce steps, making it an attractive option for applications with very large datasets. We experimentally show the trade-offs between quality and efficiency of our solutions on two large datasets coming from real-world social-media web sites.