code.txt

package com.ibm.recommender.sample;

import java.io.IOException;
import java.io.PrintWriter;
import java.util.Iterator;

import javax.annotation.Resource;
import javax.servlet.ServletException;
import javax.servlet.annotation.WebServlet;
import javax.servlet.http.HttpServlet;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;
import javax.sql.DataSource;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.jdbc.PostgreSQLJDBCDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;

/**
 * Servlet implementation class TestServlet
 */
@WebServlet( name="TestServlet", urlPatterns={"/TestServlet"} )
public class TestServlet extends HttpServlet {
	private static final long serialVersionUID = 1L;
	private @Resource (name="jdbc/taste",
            			lookup="jdbc/taste",
            			authenticationType=Resource.AuthenticationType.APPLICATION,
            			shareable=false) DataSource tasteDS;
       
    /**
     * @see HttpServlet#HttpServlet()
     */
    public TestServlet() {
        super();
    }

	/**
	 * @see HttpServlet#doGet(HttpServletRequest request, HttpServletResponse response)
	 */
	protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
		PrintWriter out = response.getWriter();
		
		DataModel dataModel;
		int USER_ID = 400;
		
		try {
		
			// create the data model object
			dataModel = 
			  new PostgreSQLJDBCDataModel(tasteDS, "PREFERENCES.TASTE_PREFERENCES", "USER_ID", "ITEM_ID", "PREFERENCE", "TIMESTAMP");
			
			out.println("<table>");
			out.println("<tr><th colspan=2>Used based recommendations</th></tr>");
			out.println("<tr><td>User id:</td><td>" + USER_ID + "</td></tr>");
			out.println("<tr><td>This user rated:</td><td>" + dataModel.getItemIDsFromUser(USER_ID).size() + " items</td></tr>");
			
			UserSimilarity similarity = //new PearsonCorrelationSimilarity(dataModel);
				new LogLikelihoodSimilarity(dataModel);
			UserNeighborhood neighborhood = new NearestNUserNeighborhood(5, similarity, dataModel);
			Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);

			out.println("<tr><th colspan=2>Recommendations</th></tr>");
			out.println("<tr><th>item_id</th><th>similarity</th></tr>");

			java.util.List<RecommendedItem> list = recommender.recommend(USER_ID, 10);
			Iterator<RecommendedItem>iter = list.iterator();
			
			while ( iter.hasNext()) {
				RecommendedItem item = iter.next();
				out.println("<tr><td>" + item.getItemID() + "</td><td>" + item.getValue() + "</td></tr>");
			}
			
			out.println("</table>");
			
		} catch (TasteException e) {
			e.printStackTrace();
		}
	}
}