无偏好值的推荐(布尔推荐)

待我称王封你为后i 2022-07-13 11:48 144阅读 0赞

对于没有偏好值的数据，或者偏好值为喜欢、不喜欢的情况下，我们可以使用布尔推荐，数据模型如右图。

![20170108105658384][]

package recommender;
    
    import java.util.List;
    
    import org.apache.mahout.cf.taste.common.TasteException;
    import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
    import org.apache.mahout.cf.taste.impl.common.FastIDSet;
    import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
    import org.apache.mahout.cf.taste.impl.model.GenericBooleanPrefDataModel;
    import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
    import org.apache.mahout.cf.taste.impl.recommender.GenericBooleanPrefUserBasedRecommender;
    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;
    /**
     * 无偏好值推荐（布尔推荐）
     * @progect Mahout_0.12.2
     * @file BoolRemmender.java
     * @author holif
     * @date 2017年1月8日
     */
    public class BoolRemmender {
    	public static void main(String[] args) throws TasteException {
    		//用户与物品关系数组    5个用户与7本书之间的对应关系  用1表示喜欢 0行与0列均不参与计算 
    		long predata[][] = {
       {},{0,1,1,1,0,0,0,0},{0,1,0,1,1,0,0,1},
    				{0,1,1,1,1,1,0,1},{0,0,1,1,0,0,1,1},
    				{0,1,0,1,0,1,1,1}};
    		//创建用于存储用户与物品列表的映射
    		FastByIDMap<FastIDSet> preferences = new FastByIDMap<FastIDSet>();
    		for (int usid =1;usid<=5;usid++) {
    			//存储该用户喜欢的书籍的bookid
    			FastIDSet set = new FastIDSet();
    			for (int bookid =1; bookid<=7;bookid++) {
    				if(predata[usid][bookid]==1){
    					set.add(bookid);
    				}
    			}
    			if (!set.isEmpty()) {
    				preferences.put(usid, set);
    			}
    		}
    		//创建Boolean数据模型
    		DataModel model = new GenericBooleanPrefDataModel(preferences);
    		//由于没有偏好值  所以使用基于对数似然比计算相似度 
    		UserSimilarity similarity = new LogLikelihoodSimilarity(model);
    		UserNeighborhood neighborhood = new NearestNUserNeighborhood(10,similarity, model);
    		
    		Recommender recommender = new GenericBooleanPrefUserBasedRecommender(
    				model, neighborhood, similarity);
    		LongPrimitiveIterator iter = model.getUserIDs();
    		while (iter.hasNext()) {
    			long usid = iter.nextLong();
    			List<RecommendedItem> list = recommender.recommend(usid, 5);
    			//只推荐用户未表达喜欢的书
    			//List<RecommendedItem> list = recommender.recommend(usid, 5, false);
    			System.out.print("usid[" + usid + "]");
    			for (RecommendedItem recommendedItem : list) {
    				System.out.print(recommendedItem);
    			}
    			System.out.println();
    		}
    	}
    }

推荐评估见： [创建一个推荐程序][Link 1]

[20170108105658384]: /images/20220713/a78b10d8813a4e6c960118e4cd4085ee.png
[Link 1]: http://blog.csdn.net/baalhuo/article/details/52848819