使用Mahout为布尔型数据生成推荐内容
Generating Recommendations with mahout for Boolean data sets (data sets with no preference value)
关于在Spark MLlib中基于布尔型数据集推荐可参考：Spark MLlib中的协同过滤
通过指定alpha：是一个针对于隐性反馈 ALS 版本的参数，这个参数决定了偏好行为强度的基准。
val alpha = 0.01
val model = ALS.trainImplicit(ratings, rank, numIterations, 0.01, alpha)
参考：http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html
Boolean data Sets
Input data set that doesn’t have a preference value, ie input data set would be of the format UserId1,ItemId1
UserId2,ItemId2
Here it’d based on some data where an user either likes an item or he doesn’t, there is no preference value associated with this.
When we use Boolean data sets we need to appropriately choose the Similarity algorithms and Recommenders
Similarity Algorithms
For Boolean data sets we can either go in for Tanimoto Coefficient Similarity or Log Likelihood Similarity
Recommender
We need to use Generic Boolean Pref User Based Recommender or Generic Boolean Pref Item Based Recommender
Sample codes for generating User based and Item Based recommendations are given below
Used Based Recommender for Boolean Data Sets
import java.io.File;
import java.io.IOException;
import java.util.List;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericBooleanPrefUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
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;
public class UserRecommender {
public static void main(String args[])
{
// specifying the user id to which the recommendations have to be generated for
int userId=510;
//specifying the number of recommendations to be generated
int noOfRecommendations=5;
//specifying theNeighborhood size
double thresholdValue=0.7;
try
{
// Data model created to accept the input file
FileDataModel dataModel = new FileDataModel(newFile("D://input.txt"));
/*TanimotoCoefficientSimilarity is intended for "binary" data sets
where a user either expresses a generic "yes" preference for an item or has no preference.*/
UserSimilarity userSimilarity = new TanimotoCoefficientSimilarity(dataModel);
/*ThresholdUserNeighborhood is preferred in situations where we go in for a
similarity measure between neighbors and not any number*/
UserNeighborhood neighborhood =new ThresholdUserNeighborhood(thresholdValue, userSimilarity, dataModel);
/*GenericBooleanPrefUserBasedRecommender is appropriate for use when no notion
of preference value exists in the data. */
Recommender recommender =new GenericBooleanPrefUserBasedRecommender(dataModel, neighborhood, userSimilarity);
//calling the recommend method to generate recommendations
List<RecommendedItem> recommendations =recommender.recommend(userId, noOfRecommendations);
//
for (RecommendedItem recommendedItem : recommendations)
System.out.println(recommendedItem.getItemID());
}
catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (TasteException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}
Item Based Recommender for Boolean Data Sets
import java.io.File;
import java.io.IOException;
import java.util.List;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;
import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
public class ItemRecommender {
public static void main(String args[])
{
// specifying the user id to which the recommendations have to be generated for
int userId=510;
//specifying the number of recommendations to be generated
int noOfRecommendations=5;
try
{
// Data model created to accept the input file
FileDataModel dataModel = new FileDataModel(newFile("D://input.txt"));
/*Specifies the Similarity algorithm*/
ItemSimilarity itemSimilarity = new LogLikelihoodSimilarity(dataModel);
/*Initalizing the recommender */
ItemBasedRecommender recommender =new GenericItemBasedRecommender(dataModel, itemSimilarity);
//calling the recommend method to generate recommendations
List<RecommendedItem> recommendations =recommender.recommend(userId, noOfRecommendations);
//
for (RecommendedItem recommendedItem : recommendations)
System.out.println(recommendedItem.getItemID());
}
catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (TasteException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}