1 Introduction  
1.1 Reducing response burden in surveys  
1.2 Optimal design and active learning  
1.3 Active learning for matrix factorization  
1.4 Computerized adaptive testing  
2 Active matrix completion  
2.1 Matrix completion methods  
2.2 Probabilistic matrix factorization  
2.3 Active learning formulation  
3 Data and evaluation methods  
3.1 Datasets  
3.2 Simulating the active strategy  
4 Results  
4.1 Does the active strategy impute more efficiently?  
4.2 Which questions does the active strategy prefer?  
5 Side information  
6 Ordered logit response model  
6.1 Probabilistic matrix factorization model  
6.2 Inference  
6.3 Active learning formulation  
6.4 Results  
7 Order effects  
8 Experimental comparison  
8.1 Impact of active ordering on non response  
9 Discussion and future work