Gradient Descent algorithm not converging

Question

I am trying to implement an algorithm for a Social Recommender System. The algorithm predicts the rating given to an item by a user, using the global reputation of the user and a similarity matrix. I am using matrix factorization to find the values of latent factors of user and item. I am using gradient descent algorithm. I have taken learning rate=0.0001, no of latent factors=10.

The value of cost function after each iteration is not following any pattern i.e. i am getting random values of cost function after each iteration.

What could be the possible reasons for this?

here is the algorithm

function [U,V,J] = algorithm(I,C,A,F,sim,n,m)

[U,V,k,delta,x1,x2]=parameters(n,m);

J=zeros(100,1);
count=1;

ntemp=zeros(n,k);
mtemp=zeros(m,k);

Jprev=Inf;
Jcurr=costFunc(U,V,I,C,A,sim,F,n,m,x1,x2);
J(count)=Jcurr;
count=count+1;

temp0=zeros(1,k);
temp1=zeros(1,k);

while(count<=100)

%Jprev-Jcurr>(10^(-6))   %convergence condition

for i=1:n
    for j=1:m
        temp0=temp0+(I(i,j)*C(i)*((U(i,:)*V(j,:)')-A(i,j)))*V(j,:);
    end
    for p=1:n
        if F(i,p)==1
            temp1=temp1+sim(i,p)*(U(i,:)-U(p,:));
        end
    end
    ntemp(i,:)=U(i,:)-delta*(temp0+(U(i,:)*x1)+(temp1*x2));
end

temp0=zeros(1,k);
temp1=zeros(1,k);

for i=1:m
    for j=1:n
        temp0=temp0+(I(j,i)*C(j)*((U(j,:)*V(i,:)')-A(j,i)))*U(j,:);
    end
    mtemp(i,:)=V(i,:)-delta*(temp0+(x1*V(i,:)));
end

U=ntemp;
V=mtemp;

Jprev=Jcurr;
Jcurr=costFunc(U,V,I,C,A,sim,F,n,m,x1,x2);

J(count)=Jcurr;
count=count+1;

end

end

here is the cost function

function [temp0,temp1,J] = costFunc(U,V,I,C,A,sim,F,n,m,x1,x2)

J=0;
temp0=0;
temp1=0;

for i=1:n
    for j=1:m
        temp1=temp1+I(i,j)*C(i)*((A(i,j)-U(i,:)*V(j,:)')^2);
    end
end

for i=1:n
    for j=i:n
        if F(i,j)==1
            temp0=temp0+sim(i,j)*norm(U(i,:)-U(j,:),'fro');
        end
    end
end

J=temp1+x2*temp0+x1*(norm(U,'fro')+norm(V,'fro'));

J=J/2;

end

here is the parameter function

function [U,V,k,delta,x1,x2] = parameters(n,m)

k=10;            %no of latent factors
U=rand(n,k);     %user latent factor matrix
V=rand(m,k);     %item latent factor matrix
delta=0.00001;   %learning rate
x1=0.001;        %regularization parameter
x2=0.001;        %regularization parameter for similarity

end