Take the 2-minute tour ×  I've seen some machine learning
questions on here so I figured I would post a related question:
Suppose I have a dataset where athletes participate at running
competitions of 10 km and 20 km with hilly courses i.e. every
competition has its own difficulty.  The finishing times from
users are almost inverse normally distributed for every
competition.  One can write this problem as a matrix:
Comp1 Comp2 Comp3 User1  20min  ??   10min  User2  25min 20min
12min  User3  30min 25min ??  User4  30min ??    ??  I would
like to complete the matrix above which has the size 1000x20 and
a sparseness of 8 % (!).  There should be a very easy way to
complete this matrix, since I can calculate parameters for every
user (ability) and parameters for every competition (mu, lambda
of distributions). Moreover the correlation between the
competitions are very high.  I can take advantage of the
rankings User1 < User2 < User3 and Item3 << Item2 < Item1  Could
you maybe give me a hint which methods