Nonnegative Matrix Factorization and Recommendor Systems

R nmf example.r

# r code to support post:http://econometricsense.blogspot.com/2012/10/nonnegative-matrix-factorization-and.html

#   ------------------------------------------------------------------
#  | PROGRAM NAME: R nmf example
#  | DATE: 10/20/12
#  | CREATED BY:  MATT BOGARD
#  | PROJECT FILE: /Users/wkuuser/Desktop/Briefcase/R Programs             
#  |----------------------------------------------------------------
#  | PURPOSE: very basic example of a recommendor system based on               
#  | non-negative matrix factorization
#  |
#  | 
#  |------------------------------------------------------------------
 
 
library(NMF)
 
# X ~ WH' 
# X is an n x p matrix
# W = n x r  user feature matrix
# H = r x p  movie feature matrix
 
# get ratings for 5 users on 4 movies
x1 <- c(5,4,1,1)
x2 <- c(4,5,1,1)
x3 <- c(1,1,5,5)
x4 <- c(1,1,4,5)
x5 <- c(1,1,5,4)
 
R <- as.matrix(rbind(x1,x2,x3,x4,x5)) # n = 5 rows p = 4 columns 
 
set.seed(12345)
 
res <- nmf(R, 4,"lee") # lee & seung method
 
V.hat <- fitted(res) 
print(V.hat) # estimated target matrix
 
w <- basis(res) #  W  user feature matrix matrix
dim(w) # n x r (n= 5  r = 4)
print(w) 
 
h <- coef(res) # H  movie feature matrix
dim(h) #  r x p (r = 4 p = 4)
print(h) 
 
# recommendor system via clustering based on vectors in H
movies <- data.frame(t(h))
features <- cbind(movies$X1,movies$X2)
plot(features)
title("Movie Feature Plot")