/**
* Calculate similarity between two items, based on the global base SVD.
*
* @param idx1 The first item's ID.
* @param idx2 The second item's ID.
* @return The similarity value between two items idx1 and idx2.
*/
private static double getItemSimilarity(int idx1, int idx2) {
double sim;
if (idx1 <= idx2) {
sim = itemSimilarity.getValue(idx1, idx2);
} else {
sim = itemSimilarity.getValue(idx2, idx1);
}
if (sim == 0.0) {
SparseVector i_vec = baseline.getV().getColRef(idx1);
SparseVector j_vec = baseline.getV().getColRef(idx2);
sim =
1 - 2.0 / Math.PI * Math.acos(i_vec.innerProduct(j_vec) / (i_vec.norm() * j_vec.norm()));
if (Double.isNaN(sim)) {
sim = 0.0;
}
if (idx1 <= idx2) {
itemSimilarity.setValue(idx1, idx2, sim);
} else {
itemSimilarity.setValue(idx2, idx1, sim);
}
}
return sim;
}
/**
* Calculate similarity between two users, based on the global base SVD.
*
* @param idx1 The first user's ID.
* @param idx2 The second user's ID.
* @return The similarity value between two users idx1 and idx2.
*/
private static double getUserSimilarity(int idx1, int idx2) {
double sim;
if (idx1 <= idx2) {
sim = userSimilarity.getValue(idx1, idx2);
} else {
sim = userSimilarity.getValue(idx2, idx1);
}
if (sim == 0.0) {
SparseVector u_vec = baseline.getU().getRowRef(idx1);
SparseVector v_vec = baseline.getU().getRowRef(idx2);
sim =
1 - 2.0 / Math.PI * Math.acos(u_vec.innerProduct(v_vec) / (u_vec.norm() * v_vec.norm()));
if (Double.isNaN(sim)) {
sim = 0.0;
}
if (idx1 <= idx2) {
userSimilarity.setValue(idx1, idx2, sim);
} else {
userSimilarity.setValue(idx2, idx1, sim);
}
}
return sim;
}