package common;
import java.io.*;
import java.util.*;
public class RateMatrix {

	private  char[][] rateMat = null;
	HashMap<Integer,Integer> users = new HashMap<Integer,Integer>();
	HashMap<Integer,Integer> movies = new HashMap<Integer,Integer>();
	
	private File train = null;
	public RateMatrix(String path)
	{
		train=new File(path);
	}
	
	
	public char [][] getRateMat(){
		computeRateMat();
		return this.rateMat;
	}
	public HashMap<Integer,Integer> getUsers(){
		return this.users;
	}
	public HashMap<Integer,Integer> getMovies(){
		return this.movies;
	}

	/**
	 * get the ID set of users and movies,
	 * conpute the num of each for the rateMat
	 * @throws IOException
	 */
	void computeUser_Movies() throws IOException
	{
		BufferedReader in=new BufferedReader(new FileReader(train));
		SortedSet<Integer> userset=new TreeSet<Integer>();
		SortedSet<Integer> movieset=new TreeSet<Integer>();
		String line;
		while((line=in.readLine())!=null){
			String[] uID_mID=line.split("\\s+");
			int userId=Integer.parseInt(uID_mID[0]);
			int movieId=Integer.parseInt(uID_mID[1]);
			userset.add(userId);
			movieset.add(movieId);
		}
		
		Iterator<Integer> userIter=userset.iterator();
		int i=0;
		while(userIter.hasNext()){
			users.put(userIter.next(), i++);
		}
		
		i=0;
		Iterator<Integer> movieIter=movieset.iterator();
		while(movieIter.hasNext()){
			movies.put(movieIter.next(), i++);
		}
	}
	/**
	 *  compute the rateMatrix
	 */
	void computeRateMat()
	{
		try {
			computeUser_Movies();
			this.rateMat=new char[this.users.size()][this.movies.size()];
			for(int i=0;i<rateMat.length;i++)
			Arrays.fill(this.rateMat[i], '0');
			BufferedReader buf=new BufferedReader(new FileReader(train));
			
			String line;
			while((line=buf.readLine())!=null){
				String[] uID_mID_rate=line.split("\\s+");
				int userID=Integer.parseInt(uID_mID_rate[0]);
				int movieID=Integer.parseInt(uID_mID_rate[1]);
				char rate=uID_mID_rate[2].charAt(0);
				
				// get the index of the userID in vector of users
				int i=this.users.get(userID);
				// get the index of the movieID in vector of movies
				int j=this.movies.get(movieID);
				this.rateMat[i][j]=rate;
			}
		} catch (IOException e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		}
	}

	/** for test 
	 *
	 * */
	void ptMatrix()
	{
		int i=this.users.get(187);
		int j=this.movies.get(2373);
		System.out.println(i+" "+j+" "+this.rateMat[i][j]);
	}
	public static void main(String[] args)
	{
		RateMatrix temp=new RateMatrix("train.txt");
		char[][] mat=temp.getRateMat();
		temp.ptMatrix();
	}
}

package SlopeOne;
import java.util.*;

import common.RateMatrix;


class MDiffRate{
	double diff;
	int num;
	public MDiffRate(double diff,int num)
	{
		this.diff = diff;
		this.num = num;
	}
}
public class SlopeOne {
	char[][] rMat = null;
	HashMap<Integer,Integer> userID = null;
	HashMap<Integer,Integer> movieID = null;
	RateMatrix RateMatrix_Fac = null;
	
	MDiffRate[][] mDiffMatrix = null;
	
	public SlopeOne(String path)
	{
		RateMatrix_Fac = new RateMatrix(path);
		this.rMat = RateMatrix_Fac.getRateMat();
		this.userID = RateMatrix_Fac.getUsers();
		this.movieID = RateMatrix_Fac.getMovies();
		this.mDiffMatrix = new MDiffRate[movieID.size()][movieID.size()];
		System.out.println("loading: "+this.userID.size()+" users,"+this.movieID.size()+"movies.");
	}
	
	public char[][] getRMat()
	{
		return this.rMat;
	}
	
	public HashMap<Integer,Integer> getUserID()
	{
		return this.userID;
	}
	
	public HashMap<Integer,Integer> getMovieID()
	{
		return this.movieID;
	}
	
	public MDiffRate[][] getMDiffs()
	{
		return this.mDiffMatrix;
	}
	
	
	
	void buildmDiffs()
	{
		for(int i=0;i<movieID.size();i++)
		{
			for(int j=0;j<i;j++)
			{
				if(j==i) continue;
				int frequency=0;
				double diffs=0;
				for(int z=0;z<userID.size();z++)
				{
					if(rMat[z][i]!='0'&&rMat[z][j]!='0')
					{
						diffs+=(rMat[z][j]-rMat[z][i]);
						frequency++;
					}
				}
				if(frequency>0)   //have common lines
				{
					diffs=diffs/frequency;
					MDiffRate tempdiff1=new MDiffRate(diffs,frequency);
					MDiffRate tempdiff2=new MDiffRate(-diffs,frequency);
					mDiffMatrix[i][j]=tempdiff1;
					mDiffMatrix[j][i]=tempdiff2;
				}
			}
		}
	}
}

package SlopeOne;

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.HashMap;

public class SlopePredict {
	
	File testFile = new File("test.txt");
	File outFile = new File("test.rate");
	
	
	SlopeOne slopeModel = null;
	char[][] rateMat = null;
	HashMap<Integer,Integer> userID = null;
	HashMap<Integer,Integer> movieID = null;
	MDiffRate[][] mDiffMatrix = null;
	
	
    SlopePredict(String trainPath)
	{
		slopeModel = new SlopeOne(trainPath);
		slopeModel.buildmDiffs();
		this.rateMat = slopeModel.getRMat();
		this.userID = slopeModel.getUserID();
		this.movieID = slopeModel.getMovieID();
		this.mDiffMatrix = slopeModel.getMDiffs();
	}
    
    double predict(int userIndex,int movieIndex)
    {
    	double frequencysum=0;
    	double weightsum=0;
    	double ans=0;
    	for(int i=0;i<movieID.size();i++)
    	{
    		if(rateMat[userIndex][i]!='0'){
    			if(mDiffMatrix[movieIndex][i]!=null)
    			{
    			       weightsum += (((double)(rateMat[userIndex][i]-'0'))-mDiffMatrix[movieIndex][i].diff)
    						    *mDiffMatrix[movieIndex][i].num;
    				frequencysum += mDiffMatrix[movieIndex][i].num;
    			}
    		}
    	}
    	ans=weightsum/frequencysum;
    	return ans;
    }
    
    void predict() throws IOException
    {
    	BufferedReader in = new BufferedReader(new FileReader(testFile));
    	BufferedWriter out = new BufferedWriter(new FileWriter(outFile));
    	String line = null;
		int userID_temp,movieID_temp;
		while((line=in.readLine()) != null)
		{
			int userIndex = -1, movieIndex = -1;
			String[] temp = line.split("\\s+");
			userID_temp = Integer.parseInt(temp[0]);
			movieID_temp = Integer.parseInt(temp[1]);
			if(userID.containsKey(userID_temp))
			{
				userIndex = userID.get(userID_temp);
			}
			if(movieID.containsKey(movieID_temp))
			{
			    movieIndex = movieID.get(movieID_temp);
			}
			double ans = -555;
			if(userIndex<0||movieIndex<0)	 		//oops! new user! new movie!
			{
				if(userIndex<0&&movieIndex>=0)
				{
					double sum = 0;
					int num = 0;
					for(int i = 0;i<userID.size();i++)
					{
						if(rateMat[i][movieIndex] != '0')
						{
							sum += rateMat[i][movieIndex]-'0';
							num++;
						}
					}
					ans=sum/num;
				}
				if(userIndex >= 0&&movieIndex < 0)
				{
					double sum = 0;
					int num = 0;
					for(int i=0;i<movieID.size();i++)
					{
						if(rateMat[userIndex][i]!='0')
						{
							sum += rateMat[userIndex][i]-'0';
							num++;
						}
					}
					ans=sum/num;
				}
				if(userIndex<0&&movieIndex<0)
				{
					ans = 3;
				}
			}
			else
			{
				ans = predict(userIndex,movieIndex); //the user and the movie exist in the train                                                                        //set
			}
			long res = Math.round(ans);
			if(res<=0) res = 1;
			if(res>=5) res = 5;
			out.write(res+"\n");
			out.flush();
		}
		out.close();
		in.close();
    }
    
	public static void main(String[] args) {
		// TODO Auto-generated method stub
			SlopePredict sp = new SlopePredict("train.txt");
			try {
				sp.predict();
			} catch (IOException e) {
				// TODO Auto-generated catch block
				e.printStackTrace();
			}
	}

}

References:

1. http://zh.wikipedia.org/wiki/Slope_one

2. SarwarB, Karypis G, Konstan J, et al. Item-based collaborative filteringrecommendation algorithms[C]//Proceedings of the 10th internationalconference on World Wide Web. ACM, 2001: 285-295.

3. LemireD, Maclachlan A. Slope one predictors for online rating-basedcollaborative filtering[J]. Society for Industrial Mathematics,2005, 5: 471-480.

4. http://my.oschina.net/liangtee/blog/124987