This is one of application for EM-algorithm to resolve user cluster from user-item matirx model. From user-item matrix, the algorithm calculates each user's Expectation and likelihood maximized in them on latent classes. Got convergence, we get to know the best class for each user.
see example.html for detail. A Typical protocol is following.
// First ; set number of classes
emalgorithm.initNUMCLASS(NUMCLASS);
// Second ; create user-item table
emalgorithm.add('u1','X','1');
emalgorithm.add('u2','Y','1');
emalgorithm.add('u3','Z','1');
// Third ; initialize variables
emalgorithm.initPIcollection();
emalgorithm.initW()
emalgorithm.initE()
// run
likelihood,loop = emalgorithm.exec(100,1-e100);
In simple example as input,no doubt each user will be assigned for which class.
#user,item,value
A,X,o
B,Y,o
C,Z,o
click Load botton,and Compute All execute and output result
L=-3.295836866004329,loop=16
user distribution
0:A
1:B
2:C
L is likelihood,loop is number of iteration. following example, each latent class information.
probabilitis of all items
[class=0]
showing class:0
W=0.3333333333333333
item value probability
X null 3.502263707236601e-182
X o 1
Y null 1
Y o 3.245522044729311e-182
Z null 1
Z o 2.567416625072901e-183
[class=1]
showing class:1
W=0.3333333333333333
item value probability
X null 1
X o 4.281058298224786e-137
Y null 9.452803266684445e-137
Y o 1
Z null 1
Z o 5.17174496845966e-137
[class=2]
showing class:2
W=0.3333333333333333
item value probability
X null 1
X o 1.0678236090073944e-45
Y null 1
Y o 8.518031807275037e-44
Z null 8.624814168175777e-44
Z o 1
clicking Compute one iteration bring a result for execution of Estep,Mstep once.
input to add some user and item, and you can get result you expect intuitively.
This code is simply implemented. More the number of items bring underflow probability.