Web-scale multi-task feature selection for behavioral targeting

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 1737-1741

  Ahmed, Amr ^b   Aly, Mohamed ^a   Das, Abhimanyu ^a   Smola, Alexander J ^b   Anastasakos, Tasos ^a  

^a YAHOO RESEARCH   (United States)

^b GOOGLE INC   (United States)

Author keywords

behavioral targeting; feature selection; large scale learning; sparsity

Indexed keywords

ADVERTISING CAMPAIGN; BEHAVIORAL TARGETING; CLOSED FORM SOLUTIONS; DISTRIBUTED INFERENCE; DISTRIBUTED PARAMETER ESTIMATION; FEATURE SELECTION METHODS; ITERATIVE THRESHOLDING; LARGE-SCALE LEARNING; MIXED-NORM; OPTIMIZATION ALGORITHMS; SPARSITY; SUBGRADIENT; TARGETING SYSTEMS;

ALGORITHMS; FEATURE EXTRACTION; KNOWLEDGE MANAGEMENT; PARAMETER ESTIMATION; PURCHASING;

ITERATIVE METHODS;

EID: 84871072633     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2398508     Document Type: Conference Paper

References (11)

1. Amr Ahmed, Mohamed Aly, Joseph Gonzalez, Shravan Narayanamurthy, and Alexander J. Smola. Scalable inference in latent variable models. In WSDM, 2012.
2. Amr Ahmed, Yucheng Low, Mohamed Aly, Vanja Josifovski, and Alexander J. Smola. Scalable distributed inference of dynamic user interests for behavioral targeting. In KDD, 2011.
3. M. Aly, A. Hatch, V. Josifovski, and V. K. Narayanan. Web-scale user modeling for targeting. In WWW, 2012.
4. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski. Optimization with sparsity-inducing penalties. CoRR, abs/1108.0775, 2011.
5. Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183-202, 2009.
6. Emmanuel Candes and Terence Tao. Decoding by linear programming. IEEE Trans. Info Theory, 51(12):4203-4215, 2005.
7. S. Ji and J. Yi. An accelerated gradient method for trace norm minimization. In ICML, 2009.
8. D. Karger, E. Lehman, T. Leighton, M. Levine, D. Lewin, and R. Panigrahy. Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the world wide web. In Symposium on the Theory of Computing STOC, pages 654-663, New York, May 1997. Association for Computing Machinery.
9. Guillaume Obozinski, Martin Wainwright, and Michael Jordan. High-dimensional union support recovery in multivariate regression. In NIPS, 2008.
10. S. Pandey, M. Aly, A. Bagherjeiran, A. Hatch, P. Ciccolo, A. Ratnaparkhi, and M. Zinkevich. Learning to terget: What works for behavioral targeting. In CIKM, 2011.
11. A.J. Smola and S. Narayanamurthy. An architecture for parallel topic models. In Very Large Databases (VLDB), 2010.