public static void Main(string[] args)
{
// load the data
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
var training_data = ItemRecommendation.Read(args[0], user_mapping, item_mapping);
var relevant_users = training_data.AllUsers; // users that will be taken into account in the evaluation
var relevant_items = training_data.AllItems; // items that will be taken into account in the evaluation
var test_data = ItemRecommendation.Read(args[1], user_mapping, item_mapping);
// set up the recommender
var recommender = new MostPopular();
recommender.Feedback = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = ItemPredictionEval.Evaluate(recommender, test_data, training_data, relevant_users, relevant_items);
foreach (var key in results.Keys)
{
Console.WriteLine("{0}={1}", key, results[key]);
}
// 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 = ItemData.Read(args[0]);
var test_data = ItemData.Read(args[1]);
// set up the recommender
var recommender = new MostPopular();
recommender.Feedback = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = recommender.Evaluate(test_data, training_data);
foreach (var key in results.Keys)
{
Console.WriteLine("{0}={1}", key, results[key]);
}
Console.WriteLine(results);
// make a score prediction for a certain user and item
Console.WriteLine(recommender.Predict(1, 1));
}