///
public override void Train()
{
int num_attributes = item_attributes.NumberOfColumns;
x_reg = new float[num_attributes];
for (int attribute_id = 0; attribute_id < num_attributes; attribute_id++)
{
x_reg[attribute_id] = FrequencyRegularization ? (float)(Regularization / item_attributes.NumEntriesByColumn(attribute_id)) : Regularization;
}
base.Train();
}
///
protected override void InitModel()
{
base.InitModel();
user_factors = null;
item_factors = null;
p = new Matrix <float> (MaxUserID + 1, NumFactors);
p.InitNormal(InitMean, InitStdDev);
y = new Matrix <float> (MaxItemID + 1, NumFactors);
y.InitNormal(InitMean, InitStdDev);
x = new Matrix <float> (item_attributes.NumberOfColumns, NumFactors);
x.InitNormal(InitMean, InitStdDev);
q = new Matrix <float> (MaxItemID + 1, NumFactors);
q.InitNormal(InitMean, InitStdDev);
int num_attributes = item_attributes.NumberOfColumns;
x_reg = new float[num_attributes];
for (int attribute_id = 0; attribute_id < num_attributes; attribute_id++)
{
x_reg [attribute_id] = FrequencyRegularization? (RegX / (float)(1 + Math.Exp(-0.005 * item_attributes.NumEntriesByColumn(attribute_id)))) : RegX;
}
y_reg = new float[MaxItemID + 1];
for (int item_id = 0; item_id <= MaxItemID; item_id++)
{
var feedback_count_by_item = Feedback.ItemMatrix [item_id];
if (feedback_count_by_item.Count > 0)
{
y_reg [item_id] = FrequencyRegularization ? (float)(RegY / Math.Sqrt(feedback_count_by_item.Count)) : RegY;
}
else
{
y_reg [item_id] = 0;
}
}
Console.Write("Learning attributes...");
BPRLinear learnAttr = new BPRLinear();
learnAttr.Feedback = Feedback;
learnAttr.ItemAttributes = item_attributes;
learnAttr.NumIter = NumIter; //10;
learnAttr.LearnRate = LearnRate; //0.05f;
learnAttr.Regularization = 0.015f; //0.001f;
learnAttr.Train();
item_attribute_weight_by_user = learnAttr.ItemAttributeWeights;
learnAttr = null;
Console.WriteLine("Done");
}
