public TSvdBoostedKnnModel TrainKnnModel(TSvdBoostedKnnModel model, List <IUser> trainUsers)
{
foreach (var user in trainUsers)
{
model.Users.Add(SvdBoostedKnnUser.FromIUser(user, NewUserFeatureGenerator.GetNewUserFeatures(model, user)));
}
return(model);
}
public IEnumerable <IRecommendation> GenerateRecommendations(IUser user, TSvdBoostedKnnModel model, List <IArtist> artists, int nearestNeighboursCount)
{
var svdBoostedKnnUser = SvdBoostedKnnUser.FromIUser(user, NewUserFeatureGenerator.GetNewUserFeatures(model, user));
var neighbours = CalculateKNearestNeighbours(svdBoostedKnnUser, model.Users, nearestNeighboursCount);
if (neighbours.Count == 0)
{
return(null);
}
return(RecommendationGenerator.GenerateRecommendations(svdBoostedKnnUser, neighbours, model, artists));
}
public float PredictRatingForArtist(IUser user, TSvdBoostedKnnModel model, List <IArtist> artists, int artistIndex, int nearestNeighboursCount)
{
var svdBoostedKnnUser = SvdBoostedKnnUser.FromIUser(user, NewUserFeatureGenerator.GetNewUserFeatures(model, user));
var neighbours = CalculateKNearestNeighbours(svdBoostedKnnUser, model.Users, nearestNeighboursCount);
if (neighbours.Count == 0)
{
return(1.0f);
}
return(RecommendationGenerator.PredictRatingForArtist(svdBoostedKnnUser, neighbours, model, artists, artistIndex));
}
public override float PredictRatingForArtist(IUser user, ISvdModel model, List <IArtist> artists, int artistIndex)
{
var newUserFeatures = NewUserFeatureGenerator.GetNewUserFeatures(model, user);
var userRating = 0.0f;
for (var f = 0; f < model.FeatureCount; f++)
{
userRating += newUserFeatures[f] * model.ArtistFeatures[f, artistIndex];
}
userRating = CapUserRatings(userRating);
return(userRating);
}
public override float PredictRatingForArtist(IUser user, IBiasSvdModel model, List <IArtist> artists, int artist)
{
var userBias = user.Ratings.Average(rating => rating.Value - model.GlobalAverage);
var newUserFeatures = NewUserFeatureGenerator.GetNewUserFeatures(model, user);
var userRating = 0.0f;
for (var f = 0; f < model.FeatureCount; f++)
{
userRating += newUserFeatures[f] * model.ArtistFeatures[f, artist];
}
userRating = CapUserRatings(model.GlobalAverage + userBias + model.ArtistBias[artist]);
return(userRating);
}
