B. B. Bederson and A. J. Quinn, Web workers unite! addressing challenges of online laborers, CHI, pp.97-106, 2011.

A. Kittur, The future of crowd work, 2013.

S. B. Roy, Crowds, not drones: Modeling human factors in interactive crowdsourcing, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00923542

S. Amer-yahia and S. B. Roy, Human factors in crowdsourcing, Proceedings of the VLDB Endowment, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02000561

N. Kaufmann, T. Schulze, and D. Veit, More than fun and money. worker motivation in crowdsourcing-a study on mechanical turk, AMCIS, 2011.

D. Chandler and A. Kapelner, Breaking monotony with meaning: Motivation in crowdsourcing markets, CoRR, 2012.

J. J. Horton and L. B. Chilton, The labor economics of paid crowdsourcing, ACM EC, pp.209-218, 2010.

D. B. Martin, Being a turker, 2014.

J. Rogstadius, An assessment of intrinsic and extrinsic motivation on task performance in crowdsourcing markets, ICWSM, 2011.

K. Hata, A glimpse far into the future: Understanding long-term crowd worker quality, 2017.

P. Dai, And now for something completely different: Improving crowdsourcing workflows with micro-diversions, ACM CSCW, 2015.

D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, 2009.

J. Pilourdault, Motivation-aware task assignment in crowdsourcing, EDBT, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01498801

Y. Zheng, QASCA: A quality-aware task assignment system for crowdsourcing applications, SIGMOD, 2015.

C. Ho and J. W. Vaughan, Online task assignment in crowdsourcing markets, AAAI, 2012.

C. Ho, Adaptive task assignment for crowdsourced classification, ICML, 2013.

S. B. Roy, Task assignment optimization in knowledge-intensive crowdsourcing, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02000596

D. Margaritis and S. Thrun, Bayesian network induction via local neighborhoods, Advances in neural information processing systems, 2000.

S. Biswas, Combating the cold start user problem in model based collaborative filtering, CoRR, 2017.

A. Albert, Regression and the Moore-Penrose pseudoinverse, 1972.

C. Dismuke, Methods and Designs for Outcomes Research, 2006.

S. E. Fienberg, An iterative procedure for estimation in contingency tables, The Annals of Mathematical Statistics, 1970.

S. E. Fienberg and M. M. Meyer, Iterative proportional fitting, Encyclopedia of Statistical Sciences, 1983.

D. Mottin, A probabilistic optimization framework for the emptyanswer problem, Proceedings of the VLDB Endowment, 2013.

M. R. Garey, Optimal binary identification procedures, SIAM Journal on Applied Mathematics, vol.23, issue.2, pp.173-186, 1972.

F. Pukelsheim, Optimal design of experiments, 2006.

F. De-hoog and R. Mattheij, Subset selection for matrices, Linear Algebra and its Applications, 2007.

H. Avron and C. Boutsidis, Faster subset selection for matrices and applications, SIAM Journal on Matrix Analysis and Applications, 2013.

R. S. Niculescu, Bayesian network learning with parameter constraints, Journal of Machine Learning Research, 2006.

J. L. Mead and R. A. Renaut, Least squares problems with inequality constraints as quadratic constraints, Linear Algebra and its Applications, vol.432, issue.8, pp.1936-1949, 2010.

P. B. Stark and R. L. Parker, Bounded-variable least-squares: an algorithm and applications, Computational Statistics, 1995.

G. Forman, An extensive empirical study of feature selection metrics for text classification, Journal of machine learning research, vol.3, pp.1289-1305, 2003.

S. Guo, So who won?: dynamic max discovery with the crowd, SIGMOD, 2012.

V. Polychronopoulos, Human-powered top-k lists, WebDB, pp.25-30, 2013.

S. B. Davidson, Top-k and clustering with noisy comparisons, 2014.

B. Groz and T. Milo, Skyline queries with noisy comparisons, PODS, pp.185-198, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01146568

H. Rahman, A probabilistic framework for estimating pairwise distances through crowdsourcing, EDBT, 2017.

A. D. Shaw, Designing incentives for inexpert human raters, 2011.

N. Kaufmann, More than fun and money. worker motivation in crowdsourcing-A study on mechanical turk, AMCIS, 2011.

Y. Gao, Finish them!: Pricing algorithms for human computation, 2014.

J. Fan, icrowd: An adaptive crowdsourcing framework, SIGMOD, 2015.

C. H. Lin, Signals in the silence: Models of implicit feedback in a recommendation system for crowdsourcing, AAAI, 2014.

H. Rahman, Feature based task recommendation in crowdsourcing with implicit observations, HCOMP, 2016.

T. Kohonen, The self-organizing map, Neurocomputing, vol.21, issue.1, pp.1-6, 1998.