帮我写一个mapreduce商品推荐数据及源码

好的，我可以为您提供一个示例的MapReduce程序用于商品推荐。请注意，这只一个示例，您需要根据您的数据自定义。

首先，您需要提供两个输入文件：一个是商品和它们的属性，第二个是用户和他们购买的商品。示例数据结构如下：

商品和属性文件：

Product1, category1
Product2, category2
Product3, category1
Product4, category2
Product5, category3

用户和购买商品文件：

User1, Product1
User1, Product2
User2, Product3
User3, Product4
User4, Product3
User4, Product5

然后，您需要实现两个MapReduce任务。第一个任务用来计算商品之间的相关性，第二个任务用来根据用户购买历史记录推荐商品。

第一个任务：

Mapper：

public class ProductMapper extends Mapper<LongWritable, Text, TextPair, IntWritable> {

    private final TextPair pair = new TextPair();
    private final IntWritable one = new IntWritable(1);

    @Override
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] split = value.toString().split(",");
        pair.setFirst(split[1]);
        pair.setSecond(split[0]);
        context.write(pair, one);
    }
}

Reducer：

public class ProductReducer extends Reducer<TextPair, IntWritable, TextPair, DoubleWritable> {

    private final TextPair pair = new TextPair();
    private final DoubleWritable result = new DoubleWritable();

    @Override
    public void reduce(TextPair key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }
        pair.setFirst(key.getFirst());
        pair.setSecond(key.getSecond());
        double correlation = (double) sum / context.getConfiguration().getInt("total_users", 1);
        result.set(correlation);
        context.write(pair, result);
    }
}

第二个任务：

Mapper：

public class UserMapper extends Mapper<LongWritable, Text, Text, Text> {

    private final Text user = new Text();
    private final Text item = new Text();

    @Override
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] split = value.toString().split(",");
        user.set(split[0]);
        item.set(split[1]);
        context.write(user, item);
    }
}

Reducer：

public class UserReducer extends Reducer<Text, Text, Text, Text> {

    private final Text output = new Text();

    @Override
    public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        Map<Text, Double> recommendations = new HashMap<>();
        for (Text value : values) {
            recommendations.put(value, 0.0);
            for (Map.Entry<TextPair, DoubleWritable> entry : context.getCacheFilesData().entrySet()) {
                TextPair pair = entry.getKey();
                double correlation = entry.getValue().get();
                if (pair.getSecond().equals(value)) {
                    recommendations.compute(pair.getFirst(), (k, v) -> v == null ? correlation : v + correlation);
                }
            }
        }
        List<Map.Entry<Text, Double>> list = new ArrayList<>(recommendations.entrySet());
        list.sort((o1, o2) -> o2.getValue().compareTo(o1.getValue()));
        output.set(list.subList(0, 5).toString());
        context.write(key, output);
    }
}

您需要将这两个任务配置到一个Job中。在Job Configuration中，您需要将所有相关性放到一个分布式缓存中，以便在第二个任务中进行推荐。以下是示例代码：

Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(RecommendationJob.class);
job.setJobName("Recommendation");

job.setMapperClass(ProductMapper.class);
job.setMapOutputKeyClass(TextPair.class);
job.setMapOutputValueClass(IntWritable.class);

job.setReducerClass(ProductReducer.class);
job.setOutputKeyClass(TextPair.class);
job.setOutputValueClass(DoubleWritable.class);

FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));

job.waitForCompletion(true);

Configuration conf2 = new Configuration();
Job job2 = Job.getInstance(conf2);
job2.setJarByClass(RecommendationJob.class);
job2.setJobName("Recommendation2");

job2.setMapperClass(UserMapper.class);
job2.setMapOutputKeyClass(Text.class);
job2.setMapOutputValueClass(Text.class);

job2.setReducerClass(UserReducer.class);
job2.setOutputKeyClass(Text.class);
job2.setOutputValueClass(Text.class);

job2.addCacheFile(new URI(args[1] + "/part-r-00000#product_correlations"));

FileInputFormat.addInputPath(job2, new Path(args[2]));
FileOutputFormat.setOutputPath(job2, new Path(args[3]));

job2.waitForCompletion(true);

希望这个示例对您有所帮助。如果您需要任何其他帮助，请告诉我。