WEB-SEARCH-PROJECT.rar_JAVProject_javⅴyαtbdsm_jαⅴ webjav.net_pro

import java.io.BufferedReader; import java.io.File; import java.io.FileNotFoundException; import java.io.FileReader; import java.io.IOException; import java.io.PrintStream; import java.util.Arrays; import java.util.Hashtable; import java.util.Scanner; public class ItemBasedRecommendationSystem { private User[] movies; private int UserName; public int Users = 200; private int Movies = 1000; private int m = 30; private double[] similarity; private double[][] ratings; private int[] topA; private Hashtable<Integer, Integer> needToBeWritten; private Hashtable<Double,Double> negativeValues; private double[] averageOfUser; private double UserAverage; private double[]User; public ItemBasedRecommendationSystem() throws FileNotFoundException { similarity = new double[Movies]; ratings = new double[Users][Movies]; topA = new int[m]; Scanner s = new Scanner(new BufferedReader(new FileReader(new File("train.txt")))); for (int i = 0; i < Users; i++) { for (int j = 0; j < Movies; j++) { ratings[i][j] = s.nextInt(); } } findUserAverages(); } public int loadNextUser(double[] User, Scanner s) { int name = UserName; while (name == UserName) { try { User[s.nextInt() - 1] = s.nextInt(); if (s.hasNext()) { name = s.nextInt() - 1; } } catch (Exception e) { break; } } return name; } private void findUserAverages() { averageOfUser = new double[Users]; for (int i = 0; i < Users; i++) { double sum = 0, count = 0; for (int j = 0; j < Movies; j++) { if (ratings[i][j] != 0) { sum += ratings[i][j]; count++; } } averageOfUser[i] = sum / count; } } private double findAverage(double[] User) { double sum = 0, count = 0; for (int i = 0; i < User.length; i++) { if (User[i] >= 1) { sum += User[i]; count++; } } return sum / count; } public void predictItemBasedSimilarity(String input, String output) throws FileNotFoundException, IOException { Scanner s = new Scanner(new BufferedReader(new FileReader(new File(input)))); PrintStream fout = new PrintStream(new File(output)); UserName = s.nextInt() - 1; while (s.hasNext()) { needToBeWritten = new Hashtable<Integer, Integer>(); User = new double[Movies]; Arrays.fill(User, -1); int nextUser = loadNextUser(User, s); for (int movie = 0; movie < User.length; movie++) { if (User[movie] == 0) { UserAverage = findAverage(User); findSimilarMoviesTo(movie); findTopANearestFor(); needToBeWritten.put(movie, 1); double prediction=predict(); double rounded=Math.round(prediction); double adjusted=adjust(rounded); System.out.println(UserName+" "+movie+" "+prediction+" "+rounded+" "+adjusted); User[movie] = adjusted; } } write(UserName, User, fout); UserName = nextUser; } fout.flush(); fout.close(); } private int adjust(double d) { if (d > 5) { return 5; } else if (d <= 0) { int temp=(int)(d+5); if(temp>5) return 5; else if (temp==0) return 1; else return temp; } else { return (int)d; } } private void findTopANearestFor() { int j = 0; for (int i = Movies - 1; j < m && i >= 0; i--) { int movie = movies[i].name; if(User[movie]>0){ topA[j] = movies[i].name; j++; } } for (int j=0; j < m; j++) { topA[j] = -1; } } private void sortUsers() { movies = new User[Movies]; for (int i = 0; i < Movies; i++) { movies[i] = new User(Math.abs(similarity[i]), i); } Arrays.sort(movies, User.BySimilarity); } private void findSimilarMoviesTo(int predictForMovie) { for (int movie = 0; movie < Movies; movie++) { if (movie == predictForMovie) { continue; } double movieMean=findMeanFor(movie); double predictForMovieMean=findMeanFor(predictForMovie); double innerProduct = 0; double lengthOfUser = 1; double lengthofUser = 1; for (int user = 0; user < Users; user++) { if (ratings[user][movie] > 0 && ratings[user][predictForMovie] > 0) { innerProduct += (ratings[user][movie] -averageOfUser[user]) * (ratings[user][predictForMovie] - averageOfUser[user]); lengthOfUser += Math.pow((ratings[user][movie] - averageOfUser[user]), 2); lengthofUser += Math.pow((ratings[user][predictForMovie] - averageOfUser[user]), 2); } } similarity[movie] = innerProduct / (Math.sqrt(lengthOfUser) * Math.sqrt(lengthofUser)); } sortUsers(); } private void write(int UserName, double[] User, PrintStream fout) { for (int movie = 0; movie < User.length; movie++) { if (User[movie] != -1 && needToBeWritten.get(movie) != null) { fout.println((UserName + 1) + " " + (movie + 1) + " " + (int)(Math.round(User[movie]))); } } } private int findMeanFor(int movie){ double sum=0,count=0; for(int user=0;user<Users;user++){ if(ratings[user][movie]>0){ sum+=ratings[user][movie]; count++; } } return (int)Math.round(sum/count); } private double predict() { int j = 0; double numerator = 0; double denominator = 0; for (int i = 0; i < m && topA[i] != -1; i++) { int movie = topA[i]; if(User[movie]>0){ numerator += similarity[movie] * (User[movie]); denominator += Math.abs(similarity[movie]); } } return (numerator / denominator); } public static void main(String args[]) throws FileNotFoundException, IOException { ItemBasedRecommendationSystem r = new ItemBasedRecommen