public virtual float Predict(Feedback feedback)
{
int userId = UsersMap.ToInternalID(feedback.User.Id);
int itemId = ItemsMap.ToInternalID(feedback.Item.Id);
List <Tuple <int, float> > features = new List <Tuple <int, float> >();
if (!IgnoreFeaturesOnPrediction)
{
features = feedback.GetAllAttributes().Select(a => FeatureBuilder.TranslateAttribute(a)).NormalizeSumToOne().ToList();
}
bool newUser = (userId > MaxUserID);
bool newItem = (itemId > MaxItemID);
float userAttrsTerm = 0, itemAttrsTerm = 0;
foreach (var feat in features)
{
// if feat_index is greater than MaxFeatureId it means that the feature is new in test set so its factors has not been learnt
if (feat.Item1 < NumTrainFeaturs)
{
float x_z = feat.Item2;
userAttrsTerm += newUser ? 0 : x_z *MatrixExtensions.RowScalarProduct(feature_factors, feat.Item1, user_factors, userId);
itemAttrsTerm += newItem ? 0 : x_z *MatrixExtensions.RowScalarProduct(feature_factors, feat.Item1, item_factors, itemId);
}
}
float itemBias = newItem ? 0 : item_bias[itemId];
float userItemTerm = (newUser || newItem) ? 0 : MatrixExtensions.RowScalarProduct(user_factors, userId, item_factors, itemId);
return(itemBias + userItemTerm + userAttrsTerm + itemAttrsTerm);
}
public override float Predict(int user_id, int item_id)
{
string userIdOrg = UsersMap.ToOriginalID(user_id);
string itemIdOrg = ItemsMap.ToOriginalID(item_id);
List <Tuple <int, float> > features = new List <Tuple <int, float> >();
if (!IgnoreFeaturesOnPrediction && Split.Container.FeedbacksDic.ContainsKey(userIdOrg, itemIdOrg))
{
var feedback = Split.Container.FeedbacksDic[userIdOrg, itemIdOrg];
features = feedback.GetAllAttributes().Select(a => FeatureBuilder.TranslateAttribute(a)).NormalizeSumToOne(Normalize).ToList();
}
bool newUser = (user_id > MaxUserID);
bool newItem = (item_id > MaxItemID);
float userAttrsTerm = 0, itemAttrsTerm = 0;
foreach (var feat in features)
{
// if feat_index is greater than MaxFeatureId it means that the feature is new in test set so its factors has not been learnt
if (feat.Item1 < NumTrainFeaturs)
{
float x_z = feat.Item2;
userAttrsTerm += newUser ? 0 : x_z *MatrixExtensions.RowScalarProduct(feature_factors, feat.Item1, user_factors, user_id);
itemAttrsTerm += newItem ? 0 : x_z *MatrixExtensions.RowScalarProduct(feature_factors, feat.Item1, item_factors, item_id);
}
}
float itemBias = newItem ? 0 : item_bias[item_id];
float userItemTerm = (newUser || newItem) ? 0 : MatrixExtensions.RowScalarProduct(user_factors, user_id, item_factors, item_id);
return(itemBias + userItemTerm + userAttrsTerm + itemAttrsTerm);
}
public override float Predict(Feedback feedback)
{
int userId = UsersMap.ToInternalID(feedback.User.Id);
int itemId = ItemsMap.ToInternalID(feedback.Item.Id);
var featurs = feedback.GetAllAttributes().Select(a => FeatureBuilder.TranslateAttribute(a));
bool newUser = (userId > MaxUserID);
bool newItem = (itemId > MaxItemID);
float userAttrsTerm = 0, itemAttrsTerm = 0;
foreach (var feat in featurs)
{
// if feat_index is greater than MaxFeatureId it means that the feature is new in test set so its factors has not been learnt
if (feat.Item1 < NumTrainFeaturs)
{
float x_z = feat.Item2;
int g_z = FeatureGroups[feat.Item1];
float alpha_z = weights[g_z];
userAttrsTerm += newUser ? 0 : alpha_z *x_z *MatrixExtensions.RowScalarProduct(feature_factors, feat.Item1, user_factors, userId);
itemAttrsTerm += newItem ? 0 : alpha_z *x_z *MatrixExtensions.RowScalarProduct(feature_factors, feat.Item1, item_factors, itemId);
}
}
int u = 0;
int i = 1;
float alpha_u = weights[u];
float alpha_i = weights[i];
float itemBias = newItem ? 0 : item_bias[itemId];
float userItemTerm = (newUser || newItem) ? 0 : alpha_u *alpha_i *MatrixExtensions.RowScalarProduct(user_factors, userId, item_factors, itemId);
return(itemBias + userItemTerm + alpha_u * userAttrsTerm + alpha_i * itemAttrsTerm);
}