Mahout Recommended engine usage

Last Update:2018-07-18 Source: Internet

Author: User

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1 user-based recommendation engine

Datamodel: Provides storage and access to user, item, and preference data for computing

Usersimilarity: Calculating the similarity between users

Userneighborhood: Compute the user's neighbor

Recommender: Organize the above components together to provide the user with item recommendation

package Com.taobao.afan;

import java.io.File;

import java.util.List;

import Org.apache.mahout.cf.taste.impl.model.file.FileDataModel;

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.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 Recommenderintro {

Public Static void Main (string[] args) throws exception{

TODO auto-generated Method stub

Datamodel model = new filedatamodel (new File ("./intro.csv")); Loading data files

Usersimilarity similarity = new pearsoncorrelationsimilarity (model); Constructing the method of similarity calculation

Userneighborhood neighborhood =

New Nearestnuserneighborhood (2, similarity, model);

Recommender recommender = new genericuserbasedrecommender (

Model, neighborhood, similarity); Create a recommendation engine

List<recommendeditem> recommendations =

Recommender.recommend (1, 1); Recommend an item to user 1

for (Recommendeditem recommendation:recommendations) {

SYSTEM.OUT.PRINTLN (recommendation);

}

Output:

2011-2-8 12:42:46 Org.slf4j.impl.JCLLoggerAdapter Info

Info: Creating Filedatamodel for file./intro.csv

2011-2-8 12:42:48 Org.slf4j.impl.JCLLoggerAdapter Info

Info: Reading File info ...

2011-2-8 12:42:48 Org.slf4j.impl.JCLLoggerAdapter Info

Info: Read lines:21

2011-2-8 12:42:48 Org.slf4j.impl.JCLLoggerAdapter Info

Info: Processed 5 users

recommendeditem[item:104, value:4.253491]

Recommendation Engine Evaluation

Package Com.taobao.afan;

Import Java.io.File;

Import org.apache.mahout.cf.taste.common.TasteException;

Import Org.apache.mahout.cf.taste.eval.RecommenderBuilder;

Import Org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;

Import Org.apache.mahout.cf.taste.model.DataModel;

Import Org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;

Import Org.apache.mahout.cf.taste.recommender.Recommender;

Import org.apache.mahout.cf.taste.similarity.UserSimilarity;

Import Org.apache.mahout.common.RandomUtils;

Import Org.apache.mahout.cf.taste.impl.model.file.FileDataModel;

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.eval.RecommenderEvaluator;

public class Evaluatorinfo {

public static void Main (string[] args) throws exception{

TODO auto-generated Method Stub

Randomutils.usetestseed ();

Datamodel model = new Filedatamodel (New File ("./intro.csv"));

Recommenderevaluator evaluator = new Averageabsolutedifferencerecommenderevaluator ();

Recommenderbuilder builder = new Recommenderbuilder () {

@Override

Public Recommender Buildrecommender (Datamodel model)

Throws Tasteexception {

Usersimilarity similarity = new pearsoncorrelationsimilarity (model);

Userneighborhood neighborhood =

New Nearestnuserneighborhood (2, similarity, model);

Return

New Genericuserbasedrecommender (model, neighborhood, similarity);

}

};

Double score = evaluator.evaluate (builder, NULL, model, 0.7, 1.0);

SYSTEM.OUT.PRINTLN (score);

}

Output: