/**
* Vertex update function.
*/
void update(CE_Graph_vertex<VertexDataType, EdgeDataType> &vertex, CE_Graph_context &gcontext) {
if ( vertex.num_outedges() > 0){
vertex_data & user = latent_factors_inmem[vertex.id()];
memset(&user.weight[0], 0, sizeof(double)*D);
for(int e=0; e < vertex.num_outedges(); e++) {
vertex_data & movie = latent_factors_inmem[vertex.edge(e)->vertex_id()];
user.weight += movie.weight;
}
// sqrt(|N(u)|)
float usrNorm = double(1.0/sqrt(vertex.num_outedges()));
//sqrt(|N(u)| * sum_j y_j
user.weight *= usrNorm;
vec step = zeros(D);
// main algorithm, see Koren's paper, just below below equation (16)
for(int e=0; e < vertex.num_outedges(); e++) {
vertex_data & movie = latent_factors_inmem[vertex.edge(e)->vertex_id()];
float observation = vertex.edge(e)->get_data();
double estScore;
rmse_vec[omp_get_thread_num()] += svdpp_predict(user, movie,observation, estScore);
// e_ui = r_ui - \hat{r_ui}
float err = observation - estScore;
assert(!std::isnan(rmse_vec[omp_get_thread_num()]));
vec itmFctr = movie.pvec;
vec usrFctr = user.pvec;
//q_i = q_i + gamma2 *(e_ui*(p_u + sqrt(N(U))\sum_j y_j) - gamma7 *q_i)
for (int j=0; j< D; j++)
movie.pvec[j] += svdpp.itmFctrStep*(err*(usrFctr[j] + user.weight[j]) - svdpp.itmFctrReg*itmFctr[j]);
//p_u = p_u + gamma2 *(e_ui*q_i -gamma7 *p_u)
for (int j=0; j< D; j++)
user.pvec[j] += svdpp.usrFctrStep*(err *itmFctr[j] - svdpp.usrFctrReg*usrFctr[j]);
step += err*itmFctr;
//b_i = b_i + gamma1*(e_ui - gmma6 * b_i)
movie.bias += svdpp.itmBiasStep*(err-svdpp.itmBiasReg* movie.bias);
//b_u = b_u + gamma1*(e_ui - gamma6 * b_u)
user.bias += svdpp.usrBiasStep*(err-svdpp.usrBiasReg* user.bias);
}
step *= float(svdpp.itmFctr2Step*usrNorm);
//gamma7
double mult = svdpp.itmFctr2Step*svdpp.itmFctr2Reg;
for(int e=0; e < vertex.num_edges(); e++) {
vertex_data& movie = latent_factors_inmem[vertex.edge(e)->vertex_id()];
//y_j = y_j + gamma2*sqrt|N(u)| * q_i - gamma7 * y_j
movie.weight += step - mult * movie.weight;
}
}
}
/**
* Vertex update function - computes the least square step
*/
void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
//compute only for user nodes
if (vertex.id() >= std::min(M,(uint)end_user) || vertex.id() < (uint)start_user)
return;
vertex_data & vdata = latent_factors_inmem[vertex.id()];
int howmany = (int)(N*knn_sample_percent);
assert(howmany > 0 );
if (vertex.num_outedges() == 0){
mymutex.lock();
users_without_ratings++;
mymutex.unlock();
}
vec distances = zeros(howmany);
ivec indices = ivec::Zero(howmany);
for (int i=0; i< howmany; i++){
indices[i]= -1;
}
std::vector<bool> curratings;
curratings.resize(N);
for(int e=0; e < vertex.num_edges(); e++) {
//no need to calculate this rating since it is given in the training data reference
assert(vertex.edge(e)->vertex_id() - M >= 0 && vertex.edge(e)->vertex_id() - M < N);
curratings[vertex.edge(e)->vertex_id() - M] = true;
}
if (knn_sample_percent == 1.0){
for (uint i=M; i< M+N; i++){
if (curratings[i-M])
continue;
vertex_data & other = latent_factors_inmem[i];
double dist;
if (algo == SVDPP)
svdpp_predict(vdata, other, 0, dist);
else if (algo == BIASSGD)
biassgd_predict(vdata, other, 0, dist);
else if (algo == RBM)
rbm_predict(vdata, other, 0, dist);
else assert(false);
indices[i-M] = i-M;
distances[i-M] = dist + 1e-10;
}
}
else for (int i=0; i<howmany; i++){
int random_other = ::randi(M, M+N-1);
vertex_data & other = latent_factors_inmem[random_other];
double dist;
if (algo == SVDPP)
svdpp_predict(vdata, other, 0, dist);
else if (algo == BIASSGD)
biassgd_predict(vdata, other, 0, dist);
else if (algo == RBM)
rbm_predict(vdata, other, 0, dist);
else assert(false);
indices[i] = random_other-M;
distances[i] = dist;
}
vec out_dist(num_ratings);
ivec indices_sorted = reverse_sort_index2(distances, indices, out_dist, num_ratings);
assert(indices_sorted.size() <= num_ratings);
assert(out_dist.size() <= num_ratings);
vdata.ids = indices_sorted;
vdata.ratings = out_dist;
if (debug)
printf("Closest is: %d with distance %g\n", (int)vdata.ids[0], vdata.ratings[0]);
if (vertex.id() % 1000 == 0)
printf("Computing recommendations for user %d at time: %g\n", vertex.id()+1, mytimer.current_time());
}
