| public Predict ( int user_id, int item_id ) : double | ||
| user_id | int | |
| item_id | int | |
| return | double | |
/// <summary>Predict the rating of a given user for a given item</summary>
/// <remarks>
/// If the user or the item are not known to the recommender, the global effects prediction is returned.
/// To avoid this behavior for unknown entities, use CanPredict() to check before.
/// </remarks>
/// <param name="user_id">the user ID</param>
/// <param name="item_id">the item ID</param>
/// <returns>the predicted rating</returns>
public override float Predict(int user_id, int item_id)
{
if (user_id >= user_factors.dim1 || item_id >= item_factors.dim1)
{
return(global_effects.Predict(user_id, item_id));
}
double result = global_effects.Predict(user_id, item_id) + DataType.MatrixExtensions.RowScalarProduct(user_factors, user_id, item_factors, item_id);
if (result > MaxRating)
{
return(MaxRating);
}
if (result < MinRating)
{
return(MinRating);
}
return((float)result);
}
/// <summary>Predict the rating of a given user for a given item</summary>
/// <remarks>
/// If the user or the item are not known to the recommender, the global effects prediction is returned.
/// To avoid this behavior for unknown entities, use CanPredict() to check before.
/// </remarks>
/// <param name="user_id">the user ID</param>
/// <param name="item_id">the item ID</param>
/// <returns>the predicted rating</returns>
public override double Predict(int user_id, int item_id)
{
if (user_id >= user_factors.dim1 || item_id >= item_factors.dim1)
{
return(global_effects.Predict(user_id, item_id));
}
double result = global_effects.Predict(user_id, item_id) + MatrixUtils.RowScalarProduct(user_factors, user_id, item_factors, item_id);
if (result > MaxRating)
{
return(MaxRating);
}
if (result < MinRating)
{
return(MinRating);
}
return(result);
}
public static void Main(string[] args)
{
// load the data
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
var training_data = MyMediaLite.IO.RatingPrediction.Read(args[0], user_mapping, item_mapping);
var test_data = MyMediaLite.IO.RatingPrediction.Read(args[1], user_mapping, item_mapping);
// set up the recommender
var recommender = new UserItemBaseline();
recommender.Ratings = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = RatingEval.Evaluate(recommender, test_data);
Console.WriteLine("RMSE={0} MAE={1}", results["RMSE"], results["MAE"]);
// make a prediction for a certain user and item
Console.WriteLine(recommender.Predict(user_mapping.ToInternalID(1), item_mapping.ToInternalID(1)));
}
public static void Main(string[] args)
{
// load the data
var training_data = RatingData.Read(args[0]);
var test_data = RatingData.Read(args[1]);
// set up the recommender
var recommender = new UserItemBaseline();
recommender.Ratings = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = recommender.Evaluate(test_data);
Console.WriteLine("RMSE={0} MAE={1}", results["RMSE"], results["MAE"]);
Console.WriteLine(results);
// make a prediction for a certain user and item
Console.WriteLine(recommender.Predict(1, 1));
var bmf = new BiasedMatrixFactorization {Ratings = training_data};
Console.WriteLine(bmf.DoCrossValidation());
}
