Java 人工智能 - Apache Mahout - 推薦算法
數(shù)據(jù)集
BX-Users 包含用戶信息
BX-Books 包含圖書信息
BX-Book-Ratings 包含圖書評分信息
加載數(shù)據(jù)
從文件加載數(shù)據(jù)
使用 FileDatamodel類從文件加載數(shù)據(jù)
文件中的數(shù)據(jù)是以逗號進行分隔
每一行的順序包含
userId,itemId[,preference[,timestamp]]
[]是可選的
preference 為二元偏好值 對于某本書 要么是喜歡 要么是不喜歡
#號 以及空行都會被忽略
數(shù)據(jù)行業(yè)可以包含其他字段 但這些字段會誒忽略
Datamodel 類 可以接受如下類型
userID、itemID 是 long 類型
preference 是 double 類型
timestrap 是 long 類型
如果能提供這些格式的數(shù)據(jù)集 就可以簡單使用如下代碼加載數(shù)據(jù)
Datamodel model = new FileDatamodel(new File(path));
不適合加載大數(shù)據(jù)量 比如幾千萬行數(shù)據(jù)
需要加載大量數(shù)據(jù)時 使用帶有 JDBC 支持 Datamode 與數(shù)據(jù)庫更加合適
實際的項目中 我們無法保證提供給我們的輸入數(shù)據(jù)中 userID 與 itemID 中為整形值
將字符串轉(zhuǎn)換為整形
對 FileDatamodel 做擴展
package com.osrcd.mahout;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDatamodel;
import java.io.File;
import java.io.IOException;
public class StringItemIdFileDatamodel extends FileDatamodel {
? ?// 初始化將String轉(zhuǎn)換為long的轉(zhuǎn)換器
? ?public ItemMemIDMigrator memIdMigtr;
? ?public StringItemIdFileDatamodel(File dataFile,String regex) throws IOException {
? ? ? ?super(dataFile,regex);
? ?}
? ?@Override
? ?protected long readItemIDFromString(String value) {
? ? ? ?if (memIdMigtr == null) {
? ? ? ? ? ?memIdMigtr = new ItemMemIDMigrator();
? ? ? ?}
? ? ? ?// 轉(zhuǎn)換為 long
? ? ? ?long retValue = memIdMigtr.toLongID(value);
? ? ? ?// 存儲到緩存
? ? ? ?try {
? ? ? ? ? ?if (null == memIdMigtr.toStringID(retValue)) {
? ? ? ? ? ? ? ?memIdMigtr.singleInit(value);
? ? ? ? ? ?}
? ? ? ?} catch (TasteException e) {
? ? ? ? ? ?e.printStackTrace();
? ? ? ?}
? ? ? ?return retValue;
? ?}
? ?// 將long轉(zhuǎn)換為String
? ?String getItemIDAsString(long itemId) throws TasteException {
? ? ? ?return memIdMigtr.toStringID(itemId);
? ?}
}
對AbstractIDMIgrator類做擴展
package com.osrcd.mahout;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.model.AbstractIDMigrator;
public class ItemMemIDMigrator extends AbstractIDMigrator {
? ?private FastByIDMap<String> longToString;
? ?public ItemMemIDMigrator() {
? ? ? ?this.longToString = new FastByIDMap<String>(10000);
? ?}
? ?public void storeMApping(long longID,String stringID) {
? ? ? ?longToString.put(longID, stringID);
? ?}
? ?public void singleInit(String stringID) {
? ? ? ?storeMApping(toLongID(stringID), stringID);
? ?}
? ?@Override
? ?public String toStringID(long longID) throws TasteException {
? ? ? ?return longToString.get(longID);
? ?}
}
對其他有用的方法進行重載
如果用戶ID不是數(shù)字
readUserIDFromString(String value)
更改解析時間戳的方式
readTimestampFromString(String value)
開始加載數(shù)據(jù)集
package com.osrcd.mahout;
import org.apache.mahout.cf.taste.common.TasteException;
import java.io.File;
import java.io.IOException;
public class LoadDataFromFile {
? ?public static void main(String[] args) throws IOException, TasteException {
? ? ? ?StringItemIdFileDatamodel model = new StringItemIdFileDatamodel(
? ? ? ? ? ? ? ?new File(LoadDataFromFile.class.getClassLoader().getResource("BX-Book-Ratings.csv").getPath()), ";");
? ? ? ?System.out.println("Total items：" + model.getNumItems() + "\nTotal users：" + model.getNumUsers());
? ?}
}
輸入的結(jié)果
Total items：340556
Total users：105283
從數(shù)據(jù)庫中加載數(shù)據(jù)
內(nèi)存數(shù)據(jù)庫
package com.osrcd.mahout;
import org.apache.mahout.cf.taste.common.TasteException;
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.model.Datamodel;
import org.apache.mahout.cf.taste.model.Preference;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import java.io.File;
import java.io.IOException;
public class LoadDataFromFile {
? ?public static void main(String[] args) throws IOException, TasteException {
? ? ? ?StringItemIdFileDatamodel model = new StringItemIdFileDatamodel(
? ? ? ? ? ? ? ?new File(LoadDataFromFile.class.getClassLoader().getResource("BX-Book-Ratings.csv").getPath()), ";");
? ? ? ?System.out.println("Total items：" + model.getNumItems() + "\nTotal users：" + model.getNumUsers());
? ? ? ?// 內(nèi)存數(shù)據(jù)庫 散列表映射到存儲一組用戶偏好的數(shù)組 PreferenceArray
? ? ? ?FastByIDMap<PreferenceArray> preferences = new FastByIDMap<>();
? ? ? ?// 創(chuàng)建一個用戶偏好的數(shù)組 存儲用評分
? ? ? ?PreferenceArray prefsForUser1 = new GenericUserPreferenceArray(10);
? ? ? ?// 為0號位置設(shè)置用戶ID
? ? ? ?prefsForUser1.setUserID(0, 0L);
? ? ? ?// 為當(dāng)前偏好（0號位置）設(shè)置項目 ID
? ? ? ?prefsForUser1.setItemID(0, 101L);
? ? ? ?// 為0號位置設(shè)置偏好值
? ? ? ?prefsForUser1.setValue(0, 3.0f);
? ? ? ?prefsForUser1.setItemID(1,102L);
? ? ? ?prefsForUser1.setValue(1, 4.5F);
? ? ? ?// 把該對象添加到散列表當(dāng)中
? ? ? ?preferences.put(1L, prefsForUser1); // userID 用作鍵
? ? ? ?// 使用散列表初始化數(shù)據(jù)模型
? ? ? ?Datamodel datamodel = new GenericDatamodel(preferences);
? ?}
}
協(xié)同過濾
通過用戶對項目的偏好 用戶項目的資料進行推薦
基于用戶的過濾
加載數(shù)據(jù)模型
package com.osrcd.mahout;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
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.Preference;
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.recommender.UserBasedRecommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashMap;
import java.util.List;
public class BookRecommender ?{
? ?static HashMap<String, String> books;
? ?public static void main(String[] args) throws Exception {
? ? ? ?books = loadBooks(BookRecommender.class.getClassLoader().getResource("BX-Books.csv").getPath());
? ? ? ?userBased();
? ?}
? ?public static HashMap<String,String> loadBooks(String filename) throws Exception{
? ? ? ?HashMap<String, String> map = new HashMap<>();
? ? ? ?BufferedReader in = new BufferedReader(new FileReader(filename));
? ? ? ?String line = "";
? ? ? ?while ((line = in.readLine()) != null) {
? ? ? ? ? ?String parts[] = line.replace("\"", "").split(";");
? ? ? ? ? ?map.put(parts[0], parts[1]);
? ? ? ?}
? ? ? ?in.close();
? ? ? ?System.out.println("Total items: " + map.size());
? ? ? ?return map;
? ?}
? ?public static void userBased() throws IOException, TasteException {
? ? ? ?// 加載數(shù)據(jù)模型
? ? ? ?StringItemIdFileDatamodel model = new StringItemIdFileDatamodel(
? ? ? ? ? ? ? ?new File(BookRecommender.class.getClassLoader().getResource("BX-Book-Ratings.csv").getPath()), ";");
? ? ? ?// 用戶關(guān)聯(lián)性 使用皮爾遜相關(guān)系數(shù)
? ? ? ?UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
? ? ? ?// 定義如何指出那些用戶是相似的 既評分彼此相近的用戶
? ? ? ?UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
? ? ? ?// 使用數(shù)據(jù)模型 鄰居 相似對象初始化基于通用用戶推薦
? ? ? ?UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
? ? ? ?long userID = 80683;
? ? ? ?int noItems = 10;
? ? ? ?List<RecommendedItem> recommendations = recommender.recommend(userID, noItems);
? ? ? ?System.out.println("Rated items by user:");
? ? ? ?for (Preference preference : model.getPreferencesFromUser(userID)) {
? ? ? ? ? ?// convert long itemID back to ISBN
? ? ? ? ? ?String itemISBN = model.getItemIDAsString(preference.getItemID());
? ? ? ? ? ?System.out.println("Item：" + books.get(itemISBN) + " | Item id：" + itemISBN + " | Value：" + preference.getValue());
? ? ? ?}
? ? ? ?System.out.println("\nRecommended items:");
? ? ? ?for (RecommendedItem item : recommendations) {
? ? ? ? ? ?String itemISBN = model.getItemIDAsString(item.getItemID());
? ? ? ? ? ?System.out.println("Item：" + books.get(itemISBN) + " | Item id：" + itemISBN + " | Value：" + item.getValue());
? ? ? ?}
? ?}
}
基于項目的過濾
GenericItemSimilarity 預(yù)先計算項目的相似度
PearsonCorrelationSimilarity 實時計算相似度 大量數(shù)據(jù)時 計算速度慢得難以忍受
public static void main(String[] args) throws Exception {
? ? ? ?books = loadBooks(BookRecommender.class.getClassLoader().getResource("BX-Books.csv").getPath());
// ? ? ? ?userBased();
? ? ? ?itemBased();
? ?}
public static void itemBased() throws IOException, TasteException {
? ?StringItemIdFileDatamodel model = new StringItemIdFileDatamodel(new File(BookRecommender.class.getClassLoader().getResource("BX-Book-Ratings.csv").getPath()), ";");
? ?ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(model);
? ?ItemBasedRecommender recommender = new GenericItemBasedRecommender(model, itemSimilarity);
? ?String itemISBN = "0395272238";
? ?long itemID = model.readItemIDFromString(itemISBN);
? ?int noItems = 10;
? ?List<RecommendedItem> recommendations = recommender.mostSimilarItems(itemID, noItems);
? ?System.out.println("Recommendations for item:"+books.get(itemISBN));
? ?System.out.println("\nMost similar items:");
? ?for (RecommendedItem item : recommendations) {
? ? ? ?itemISBN = model.getItemIDAsString(item.getItemID());
? ? ? ?System.out.println("Item：" + books.get(itemISBN) + " | Item id：" + itemISBN + " | Value：" + item.getValue());
? ?}
}
"C:\Program Files\Java\jdk1.8.0_121\bin\java.exe" -agentlib:jdwp=transport=dt_socket,address=127.0.0.1:1975,suspend=y,server=n -javaagent:C:\Users\OSrcD\AppData\Local\JetBrains\Toolbox\Apps\IDEA-U\ch-0\212.4746.92\plugins\java\lib\rt\debugger-agent.jar -Dfile.encoding=GBK -classpath "C:\Program Files\Java\jdk1.8.0_121\jre\lib\charsets.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\deploy.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\access-bridge-64.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\cldrdata.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\dnsns.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\jaccess.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\jfxrt.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\localedata.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\nashorn.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\sunec.jar;C:\Program Files\Java\jdk1.8.0_121\jre\lib\ext\sunjce_provider.jar;C:\Program 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com.osrcd.mahout.BookRecommender
Connected to the target VM, address: '127.0.0.1:1975', transport: 'socket'
Total items: 271379
log4j:WARN No Appenders could be found for logger (org.apache.mahout.cf.taste.impl.model.file.FileDatamodel).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Recommendations for item:The Fellowship of the Ring (Lord of the Rings (Paperback))
Most similar items:
Item：The Hundred Secret Senses | Item id：080411109X | Value：1.0
Item：The HOLLAND SUGGESTIONS : A Novel | Item id：0671003534 | Value：1.0
Item：Girl in Hyacinth Blue | Item id：014029628X | Value：1.0
Item：The Education of Little Tree (A Zia Book) | Item id：0826308791 | Value：1.0
Item：A Fever in the Heart : Ann Rule's Crime Files, Volume III | Item id：0671793551 | Value：1.0
Item：One Hundred Years of Solitude | Item id：0060929790 | Value：1.0
Item：Hellsing, Vol. 1 | Item id：159307056X | Value：1.0
Item：The Phantom Tollbooth | Item id：0394820371 | Value：1.0
Item：Snow Crash (Bantam Spectra Book) | Item id：0553380958 | Value：1.0
Item：The Samurai's Wife (A Sano Ichiro Mystery) | Item id：0312974485 | Value：1.0
Disconnected from the target VM, address: '127.0.0.1:1975', transport: 'socket'
Process finished with exit code 0
