Code for Recommender Systems

Two ways to eliminate extra zero ones

idx = find(R(i, :)==1) Thetatemp = Theta(idx,:) Ytemp = Y(i,idx)

Xgrad(i,:) = (X(i,:)∗ThetaTtemp −Ytemp)∗Thetatemp.

R .* M sum(sum(R.*M))

function [J, grad] = cofiCostFunc(params, Y, R, num_users, num_movies, ...
num_features, lambda)

%COFICOSTFUNC Collaborative filtering cost function
%   [J, grad] = COFICOSTFUNC(params, Y, R, num_users, num_movies, ...
%   num_features, lambda) returns the cost and gradient for the
%   collaborative filtering problem.
%

% Unfold the U and W matrices from params
X = reshape(params(1:num_movies*num_features), num_movies, num_features);
Theta = reshape(params(num_movies*num_features+1:end), ...
num_users, num_features);

% You need to return the following values correctly
J = 0;
X_grad = zeros(size(X));
Theta_grad = zeros(size(Theta));

% Instructions: Compute the cost function and gradient for collaborative
%               filtering. Concretely, you should first implement the cost
%               function (without regularization) and make sure it is
%               matches our costs. After that, you should implement the
%               gradient and use the checkCostFunction routine to check
%               that the gradient is correct. Finally, you should implement
%               regularization.
%
% Notes: X - num_movies  x num_features matrix of movie features
%        Theta - num_users  x num_features matrix of user features
%        Y - num_movies x num_users matrix of user ratings of movies
%        R - num_movies x num_users matrix, where R(i, j) = 1 if the
%            i-th movie was rated by the j-th user
%
% You should set the following variables correctly:
%
%        X_grad - num_movies x num_features matrix, containing the
%                 partial derivatives w.r.t. to each element of X
%        Theta_grad - num_users x num_features matrix, containing the
%                     partial derivatives w.r.t. to each element of Theta

J=1/2*sum(sum(R.*((X*Theta'-Y).^2)))+lambda/2*sum(sum(X.^2))+lambda/2*sum(sum(Theta.^2));
X_grad=(R.*(X*Theta'-Y))*Theta+lambda*X;
Theta_grad=(R.*(X*Theta'-Y))'*X+lambda*Theta;

grad = [X_grad(:); Theta_grad(:)];

end