我试着做一个电影推荐系统，一直在关注这个网站。链接此处
def count_ratings_users_freq(self, user_id, values):
"""
For each user, emit a row containing their "postings"
(item,rating pairs)
Also emit user rating sum and count for use later steps.
output:
userid, number of movie rated by user, rating number count, (movieid, movie rating)
17 1,3,(70,3)
35 1,1,(21,1)
49 3,7,(19,2 21,1 70,4)
87 2,3,(19,1 21,2)
98 1,2,(19,2)
"""
item_count = 0
item_sum = 0
final = []
for item_id, rating in values:
item_count += 1
item_sum += rating
final.append((item_id, rating))
yield user_id, (item_count, item_sum, final)
是否可以使用HadoopMap和Reduce将上述代码翻译成Java？userid作为密钥
CCD_ 2作为值。非常感谢。
是的，您可以将其转换为map reduce程序。
映射器逻辑：
- 假设输入的格式为（用户ID、电影ID、电影评级）（例如17,70,3），您可以用逗号（，）分隔每一行，并将"用户ID"作为关键字，将（电影ID、影片评级）作为值。例如，对于记录：（17,70,3），您可以发出键：（17）和值：（70,3）
减速器逻辑：
- 您将保留3个变量：movieCount（integer）、movieRatingCount（整数）、moveValue（字符串）
-
对于每个值，您需要解析该值并获得"电影评级"。例如，对于值（70,3），您将解析电影评级=3。
-
对于每个有效记录，您将递增movieCount。您将把解析后的"movierating"添加到"movieRatingCount"中，并将该值附加到"moveValues"字符串中。
您将获得所需的输出。
以下是实现这一点的代码：
package com.myorg.hadooptests;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
public class MovieRatings {
public static class MovieRatingsMapper
extends Mapper<LongWritable, Text , IntWritable, Text>{
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String valueStr = value.toString();
int index = valueStr.indexOf(',');
if(index != -1) {
try
{
IntWritable keyUserID = new IntWritable(Integer.parseInt(valueStr.substring(0, index)));
context.write(keyUserID, new Text(valueStr.substring(index + 1)));
}
catch(Exception e)
{
// You could get a NumberFormatException
}
}
}
}
public static class MovieRatingsReducer
extends Reducer<IntWritable, Text, IntWritable, Text> {
public void reduce(IntWritable key, Iterable<Text> values,
Context context) throws IOException, InterruptedException {
int movieCount = 0;
int movieRatingCount = 0;
String movieValues = "";
for (Text value : values) {
String[] tokens = value.toString().split(",");
if(tokens.length == 2)
{
movieRatingCount += Integer.parseInt(tokens[1].trim()); // You could get a NumberFormatException
movieCount++;
movieValues = movieValues.concat(value.toString() + " ");
}
}
context.write(key, new Text(Integer.toString(movieCount) + "," + Integer.toString(movieRatingCount) + ",(" + movieValues.trim() + ")"));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "CompositeKeyExample");
job.setJarByClass(MovieRatings.class);
job.setMapperClass(MovieRatingsMapper.class);
job.setReducerClass(MovieRatingsReducer.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path("/in/in2.txt"));
FileOutputFormat.setOutputPath(job, new Path("/out/"));
System.exit(job.waitForCompletion(true) ? 0:1);
}
}
对于输入：
17,70,3
35,21,1
49,19,2
49,21,1
49,70,4
87,19,1
87,21,2
98,19,2
我得到了输出：
17 1,3,(70,3)
35 1,1,(21,1)
49 3,7,(70,4 21,1 19,2)
87 2,3,(21,2 19,1)
98 1,2,(19,2)