private MyRecommender()
{
UserMapping = new EntityMapping();
ItemMapping = new EntityMapping();
// load the data
var data = RatingData.Read("../mymedialite/data/ml-100k/u.data", UserMapping, ItemMapping);
// set up the recommender
recommender = (IncrementalRatingPredictor) Model.Load("bmf.model");
recommender.Ratings = data;
}
private void CreateRecommender()
{
BiasedMatrixFactorization recommender = new BiasedMatrixFactorization();
Console.Error.Write("Reading in ratings ... ");
TimeSpan time = Wrap.MeasureTime(delegate() {
recommender.Ratings = RatingData.Read(ratings_file, user_mapping, item_mapping);
});
Console.Error.WriteLine("done ({0,0:0.##}).", time.TotalSeconds.ToString(CultureInfo.InvariantCulture));
//Console.Error.Write("Reading in additional ratings ... ");
//string[] rating_files = Directory.GetFiles("../../saved_data/", "user-ratings-*");
//Console.Error.WriteLine("done.");
foreach (var indices_for_item in recommender.Ratings.ByItem)
if (indices_for_item.Count > 0)
movies_by_frequency.Add(new Tuple<int, float>(recommender.Ratings.Items[indices_for_item[0]], indices_for_item.Count));
movies_by_frequency = movies_by_frequency.OrderByDescending(x => x.Item2).ToList();
for (int i = 0; i < n_movies; i++)
top_n_movies.Add( movies_by_frequency[i].Item1 );
Console.Error.Write("Loading prediction model ... ");
recommender.UpdateUsers = true;
recommender.UpdateItems = false;
recommender.BiasReg = 0.001f;
recommender.Regularization = 0.045f;
recommender.NumIter = 60;
time = Wrap.MeasureTime(delegate() {
recommender.LoadModel(model_file);
});
Console.Error.WriteLine("done ({0,0:0.##}).", time.TotalSeconds.ToString(CultureInfo.InvariantCulture));
rating_predictor = recommender;
current_user_id = user_mapping.ToInternalID(current_user_external_id.ToString());
//rating_predictor.AddUser(current_user_id);
// add movies that were not in the training set
//rating_predictor.AddItem( item_mapping.InternalIDs.Count - 1 );
PredictAllRatings();
}
