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))); }
protected void Page_Load(object sender, EventArgs e) { string sortBy = null; if (Request.QueryString["active"] == null) { _activeLetter = "A"; } else { _activeLetter = Request.QueryString["active"]; } if (Request.QueryString["sortBy"] == null) { sortBy = "a"; } else { sortBy = Request.QueryString["sortBy"]; } _sort = TryParseSortChannelsBy(sortBy); GetSortingLinks(); BLClient client = null; // access the DB for most popular channels and bind "most popular" once if (!Page.IsPostBack) { try { client = new BLClient(); MostPopular.DataSource = client.GetChannelMostPopular(); MostPopular.DataBind(); } finally { client.Dispose(); } GetChannels(); } Panel activeLetterPanel = GetActivePanel(_activeLetter); AddSortingInfoToAlphabet(sortBy); activeLetterPanel.CssClass = "ActiveLetter"; }
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 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)); }
static void Main(string[] args) { var processor = new MostPopular(); processor.Setup(); }