public List <Suggestion> GetSuggest(UserBehavior db, long userId) { IRater rater = new SimpleRater(); IComparer comparer = new CorrelationUserComparer(); recommender = new ItemCollaborativeFilterRecommender(comparer, rater, 50); recommender.Train(db); var suggestion = recommender.GetSuggestions(userId, 500); return(suggestion); }
public static TestResults Test(this IRecommender classifier, UserBehaviorDatabase db, int numSuggestions) { // We're only using the ratings to check for existence of a rating, so we can use a simple rater for everything SimpleRater rater = new SimpleRater(); UserBehaviorTransformer ubt = new UserBehaviorTransformer(db); UserArticleRatingsTable ratings = ubt.GetUserArticleRatingsTable(rater); int correctUsers = 0; double averagePrecision = 0.0; double averageRecall = 0.0; // Get a list of users in this database who interacted with an article for the first time List <int> distinctUsers = db.UserActions.Select(x => x.UserID).Distinct().ToList(); var distinctUserArticles = db.UserActions.GroupBy(x => new { x.UserID, x.ArticleID }); // Now get suggestions for each of these users foreach (int user in distinctUsers) { List <Suggestion> suggestions = classifier.GetSuggestions(user, numSuggestions); bool foundOne = false; int userIndex = ratings.UserIndexToID.IndexOf(user); int userCorrectArticles = 0; int userTotalArticles = distinctUserArticles.Count(x => x.Key.UserID == user); foreach (Suggestion s in suggestions) { int articleIndex = ratings.ArticleIndexToID.IndexOf(s.ArticleID); // If one of the top N suggestions is what the user ended up reading, then we're golden if (ratings.Users[userIndex].ArticleRatings[articleIndex] != 0) { userCorrectArticles++; if (!foundOne) { correctUsers++; foundOne = true; } } } averagePrecision += (double)userCorrectArticles / numSuggestions; averageRecall += (double)userCorrectArticles / userTotalArticles; } averagePrecision /= distinctUsers.Count; averageRecall /= distinctUsers.Count; return(new TestResults(distinctUsers.Count, correctUsers, averageRecall, averagePrecision)); }
private void bgRecommend_DoWork(object sender, DoWorkEventArgs e) { GetRecommendation args = e.Argument as GetRecommendation; e.Result = recommender.GetSuggestions(args.UserID, args.Ratings); }
public ActionResult Index(string search = "") { if (search == "") { int id = Convert.ToInt32(Session["id"].ToString()); string email = context.login.Where(m => m.Id == id).FirstOrDefault().Email; int realid = context.students.Where(m => m.Email == email).FirstOrDefault().Id; IRater rate = new LinearRater(-4, 2, 0.5, 1); IComparer compare = new CorrelationUserComparer(); recommender = new UserCollaborativeFilterRecommender(compare, rate, 200); UserBehaviorDatabaseParser parser = new UserBehaviorDatabaseParser(); UserBehaviorDatabase db1 = parser.LoadUserBehaviorDatabase("/Data/NewBehavior.txt"); UserBehaviorTransformer ubt = new UserBehaviorTransformer(db1); recommender.Train(db1); int userId; int ratings; userId = realid; ratings = 2; List <Suggestion> result = new List <Suggestion>(); List <RecomendedArticles> rem = new List <RecomendedArticles>(); List <Suggestion> result2 = new List <Suggestion>(); RecomendedArticles recom; if (ratings >= 1 && ratings <= 100) { new GetRecommendation { UserID = userId, Ratings = ratings }; result = recommender.GetSuggestions(userId, ratings); result2 = recommender.GetSuggestions(userId, 6); } foreach (Suggestion suggestion in result) { var ye = context.ufiles.Where(m => m.Id == suggestion.ArticleID).FirstOrDefault(); recom = new RecomendedArticles() { Name = ye.Name, UpdatedFileName = ye.UpdatedFileName, UplodedBy = ye.UplodedBy, Description = ye.Description, Filename = ye.Filename, imagepath = ye.imagepath, UplodedDate = ye.UplodedDate, Rating = suggestion.Rating, Id = ye.Id, }; rem.Add(recom); } NRViewModel recomendedArticles = new NRViewModel(); recomendedArticles.uplodedFiles = context.ufiles.OrderByDescending(m => m.Id).Take(6).ToList(); recomendedArticles.RecomendedArticles = rem; return(View(recomendedArticles)); } else { NRViewModel recomendedArticles = new NRViewModel(); recomendedArticles.uplodedFiles = context.ufiles.OrderByDescending(m => m.Id).Where(m => m.Name.Contains(search)).Take(6).ToList(); if (recomendedArticles.uplodedFiles == null) { ViewBag.messagea = "no item found"; } ViewBag.message = "search"; return(View(recomendedArticles)); } }