Two algorithms for nearest-neighbor search in high dimensions
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
INFORMS Journal on Computing
Disorder inequality: a combinatorial approach to nearest neighbor search
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Combinatorial algorithms for nearest neighbors, near-duplicates and small-world design
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Combinatorial Framework for Similarity Search
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Response prediction using collaborative filtering with hierarchies and side-information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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How could one estimate the total number of clicks a new advertisement could potentially receive in the current market? This question, called the click volume estimation problem is investigated in this paper. This constitutes a new research direction for advertising engines. We propose a model of computing an estimation of the click volume. A key component of our solution is the application of linear regression to a large (but sparse) data set. We propose an iterative method in order to achieve a fast approximation of the solution. We prove that our algorithm always converges to optimal parameters of linear regression. To the best of our knowledge, it is the first time when linear regression is considered in such a large scale context