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Mahout基于内存的DataMode 推荐引擎Demo2

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//注释的部分是基于文件也可以理解为基于日志文件的,

//DataModel 可以有很多种,实现abstractDataMode的子类,原则上都可以作为数据源,个人觉得,不管是那种DataMode各自有优缺点

//应该视情况而定,

package com.test.mahout;

import java.util.List;

import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.model.GenericDataModel;
import org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray;
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.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
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;

class RecommenderIntro {
    public static void main(String[] args) throws Exception {
      
      
    /*    FastByIDMap<PreferenceArray> preferences =new FastByIDMap<PreferenceArray>();
     *
            PreferenceArray prefsForUser1 = new GenericUserPreferenceArray(10); A
          
            prefsForUser1.setUserID(0, 1L);
            prefsForUser1.setItemID(0, 101L); B
            prefsForUser1.setValue(0, 3.0f); B
          
            prefsForUser1.setItemID(1, 102L);
            prefsForUser1.setValue(1, 4.5f);
          
            … (8 more)
            preferences.put(1L, prefsForUser1); C
            DataModel model = new GenericDataModel(preferences); D
            //A Set up PreferenceArray for user 1
            //B Add the first of 10 preferences
            //C Attach user 1’s preference*/
      
        // laod Data File
      
//        DataModel model = new FileDataModel(new File("C:\\mahout_data.txt"));
      
      FastByIDMap<PreferenceArray> preferences =new FastByIDMap<PreferenceArray>();

/*FastByIDMap是经过优化了的key-value数据结够,在这里用来存储基本的元数据,《mahout in action》书中对这个数据结构做了详细的解释*/
      
        PreferenceArray prefsForUser1=new GenericUserPreferenceArray(3);//注意这里的数字
      // 这里是用来存储一个用户的元数据,这些元数据通常来自日志文件,比如浏览历史,等等,不同的业务场合,它的业务语义应该是不一样
        prefsForUser1.setUserID(0, 1);/*个人对这里也感觉到非常模糊,其实这里保存用户ID完全用不着key-value结构,也用不着用一个long型的ID,这里个人觉得是为了以后扩展或者,保持统一的格式,注意用就是了*/


      
        prefsForUser1.setItemID(0, 101);
        prefsForUser1.setValue(0, 5.0f);
      
        prefsForUser1.setItemID(1, 102);
        prefsForUser1.setValue(1, 3.0f);
      
      
        prefsForUser1.setItemID(2, 103);
        prefsForUser1.setValue(2, 2.5f);
      
      
         preferences.put(1l, prefsForUser1);//在这里添加数据
      
      
         PreferenceArray prefsForUser2=new GenericUserPreferenceArray(4);
        
         prefsForUser2.setUserID(0, 2);
        
         prefsForUser2.setItemID(0, 101);
         prefsForUser2.setValue(0, 2.0f);
        
         prefsForUser2.setItemID(1, 102);
         prefsForUser2.setValue(1,2.5f);
        
         prefsForUser2.setItemID(2, 103);
         prefsForUser2.setValue(2,5.0f);
        

         prefsForUser2.setItemID(3,104);
         prefsForUser2.setValue(3,2.0f);
        
        
         preferences.put(2l, prefsForUser2);
        
        
         PreferenceArray prefsForUser3=new GenericUserPreferenceArray(4);
        
        
         prefsForUser3.setUserID(0, 3);
        
         prefsForUser3.setItemID(0, 101);
         prefsForUser3.setValue(0, 2.5f);
        
         prefsForUser3.setItemID(1, 104);
         prefsForUser3.setValue(1, 4.0f);

       
         prefsForUser3.setItemID(2, 105);
         prefsForUser3.setValue(2, 4.5f);
        
         prefsForUser3.setItemID(3, 107);
         prefsForUser3.setValue(3, 5.0f);
        
         preferences.put(3l, prefsForUser3);
        
        
         PreferenceArray prefsForUser4=new GenericUserPreferenceArray(4);
        
         prefsForUser4.setUserID(0, 4);
        
         prefsForUser4.setItemID(0,101);
         prefsForUser4.setValue(0, 5.0f);
        
         prefsForUser4.setItemID(1,103);
         prefsForUser4.setValue(1, 3.0f);
        
         prefsForUser4.setItemID(2,104);
         prefsForUser4.setValue(2,4.5f);
        
        
         prefsForUser4.setItemID(3, 106);
         prefsForUser4.setValue(3, 4.0f);
        
         preferences.put(4l, prefsForUser4);
        
        
        
         PreferenceArray prefsForUser5=new GenericUserPreferenceArray(6);
        
         prefsForUser5.setUserID(0, 5);
        
         prefsForUser5.setItemID(0, 101);
         prefsForUser5.setValue(0, 4.0f);
        
         prefsForUser5.setItemID(1, 102);
         prefsForUser5.setValue(1, 3.0f);
        
         prefsForUser5.setItemID(2, 103);
         prefsForUser5.setValue(2, 2.0f);
        
        
         prefsForUser5.setItemID(3, 104);
         prefsForUser5.setValue(3, 4.0f);
        
         prefsForUser5.setItemID(4, 105);
         prefsForUser5.setValue(4, 3.5f);
        
         prefsForUser5.setItemID(5, 106);
         prefsForUser5.setValue(5, 4.0f);
        
         preferences.put(5l, prefsForUser5);
        
        DataModel model=new  GenericDataModel(preferences) ;//DataModel的建立
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
      
        UserNeighborhood neighborhood = new NearestNUserNeighborhood(2,similarity, model);

        // Create the recommender engine
        Recommender recommender = new GenericUserBasedRecommender(model,neighborhood, similarity);

        // C For user 1, recommend 1 item
        List<RecommendedItem> recommendations = recommender.recommend(1, 1);
        for (RecommendedItem recommendation : recommendations) {
            System.out.println(recommendation);
        }
      
      
      
    }
}
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