| public Predict ( int user_id, int item_id ) : double | ||
| user_id | int | |
| item_id | int | |
| return | double | |
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));
}
///
public override float Predict(int user_id, int item_id)
{
if (user_id > MaxUserID || item_id > MaxItemID)
{
return(float.MinValue);
}
int result = 1;
int ac;
foreach (int a in item_attributes[item_id])
{
if (attribute_count_by_user[user_id].TryGetValue(a, out ac))
{
result += ac;
}
}
return((float)result * (most_popular.Predict(user_id, item_id) + 1) / (item_attributes[item_id].Count + 1));
// +1 guarantees that songs with a user-accessed attribute are ranked above other songs,
// even if they have a count of zero.
// TODO think about other kinds of normalization
}
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)));
}
