/// <summary>Recommend top N items, based on a user description by ratings</summary>
/// <returns>a list of item IDs with scores</returns>
/// <param name='recommender'>the IFoldInRatingPredictor recommender</param>
/// <param name='rated_items'>a list of item IDs and ratings describing the user</param>
/// <param name='n'>the number of items to recommend</param>
public static IList <Tuple <int, float> > RecommendItems(this IFoldInRatingPredictor recommender, IList <Tuple <int, float> > rated_items, int n)
{
var candidate_items = Enumerable.Range(0, ((RatingPredictor)recommender).Ratings.MaxItemID - 1).ToArray();
var scored_items = recommender.ScoreItems(rated_items, candidate_items);
return(scored_items.OrderByDescending(x => x.Item2).Take(n).ToArray());
}
/// <summary>Performs user-wise fold-in evaluation</summary>
/// <returns>the evaluation results</returns>
/// <param name='recommender'>a rating predictor capable of performing a user fold-in</param>
/// <param name='update_data'>the rating data used to represent the users</param>
/// <param name='eval_data'>the evaluation data</param>
static public RatingPredictionEvaluationResults EvaluateFoldIn(this IFoldInRatingPredictor recommender, IRatings update_data, IRatings eval_data)
{
double rmse = 0;
double mae = 0;
double cbd = 0;
int rating_count = 0;
foreach (int user_id in update_data.AllUsers)
{
if (eval_data.AllUsers.Contains(user_id))
{
var known_ratings = (
from index in update_data.ByUser[user_id]
select Tuple.Create(update_data.Items[index], update_data[index])
).ToArray();
var items_to_rate = (from index in eval_data.ByUser[user_id] select eval_data.Items[index]).ToArray();
var predicted_ratings = recommender.ScoreItems(known_ratings, items_to_rate);
foreach (var pred in predicted_ratings)
{
float prediction = pred.Item2;
float actual_rating = eval_data.Get(user_id, pred.Item1, eval_data.ByUser[user_id]);
float error = prediction - actual_rating;
rmse += error * error;
mae += Math.Abs(error);
cbd += Eval.Ratings.ComputeCBD(actual_rating, prediction, recommender.MinRating, recommender.MaxRating);
rating_count++;
}
Console.Error.Write(".");
}
}
mae = mae / rating_count;
rmse = Math.Sqrt(rmse / rating_count);
cbd = cbd / rating_count;
var result = new RatingPredictionEvaluationResults();
result["RMSE"] = (float)rmse;
result["MAE"] = (float)mae;
result["NMAE"] = (float)mae / (recommender.MaxRating - recommender.MinRating);
result["CBD"] = (float)cbd;
return(result);
}
/// <summary>Recommend top N items, based on a user description by ratings</summary>
/// <returns>a list of item IDs with scores</returns>
/// <param name='recommender'>the IFoldInRatingPredictor recommender</param>
/// <param name='rated_items'>a list of item IDs and ratings describing the user</param>
public static IList <Tuple <int, float> > ScoreItems(this IFoldInRatingPredictor recommender, IList <Tuple <int, float> > rated_items)
{
var candidate_items = Enumerable.Range(0, ((RatingPredictor)recommender).Ratings.MaxItemID - 1).ToArray();
return(recommender.ScoreItems(rated_items, candidate_items));
}
/// <summary>Recommend top N items, based on a user description by ratings</summary>
/// <returns>a list of item IDs with scores</returns>
/// <param name='recommender'>the IFoldInRatingPredictor recommender</param>
/// <param name='rated_items'>a list of item IDs and ratings describing the user</param>
/// <param name='candidate_items'>the recommendation candidates</param>
/// <param name='n'>the number of items to recommend</param>
public static IList <Tuple <int, float> > RecommendItems(this IFoldInRatingPredictor recommender, IList <Tuple <int, float> > rated_items, IList <int> candidate_items, int n)
{
var scored_items = recommender.ScoreItems(rated_items, candidate_items);
return(scored_items.OrderByDescending(x => x.Item2).Take(n).ToArray());
}