The following steps depict how to prepare for this recipe.

The following steps describe how to run the content-based recommendation recipe.

You will see that it will print the results as follows. Each line of the result contains the customer ID and list of product recommendations for that customer.

A10003PM9DTGHQ  [0446611867, 0446613436, 0446608955, 0446606812, 0446691798, 0446611867, 0446613436, 0446608955, 0446606812, 0446691798]

The following listing shows an entry for one product from the dataset. Here, each data entry includes an ID, title, categorization, similar items to this item, and information about users who has brought the item.

Id:   13
ASIN: 0313230269
title: Clockwork Worlds : Mechanized Environments in SF (Contributions to the Study of Science Fiction and Fantasy)
group: Book
salesrank: 2895088
similar: 2  1559360968  1559361247
categories: 3
   |Books[283155]|Subjects[1000]|Literature & Fiction[17]|History & Criticism[10204]|Criticism & Theory[10207]|General[10213]
   |Books[283155]|Subjects[1000]|Science Fiction & Fantasy[25]|Fantasy[16190]|History & Criticism[16203]
   |Books[283155]|Subjects[1000]|Science Fiction & Fantasy[25]|Science Fiction[16272]|History & Criticism[16288]
reviews: total: 2  downloaded: 2  avg rating: 5
    2002-8-5  customer: A14OJS0VWMOSWO  rating: 5  votes:   2  helpful:   1
    2003-3-21  customer:  A2C27IQUH9N1Z  rating: 5  votes:   4  helpful:   4

We have written a new Hadoop data format to read and parse the Amazon product data, and the data format works similar to the format we have written in the Simple Analytics using MapReduce recipe in Chapter 6, Analytics. The source files src/chapter8/AmazonDataReader.java and src/chapter8/AmazonDataFormat.java contain the code for the Amazon data formatter.

Amazon data formatter will parse the dataset and emit the data about each Amazon product as key-value pairs to the map function. The data about each Amazon product is represented as string and the class AmazonCustomer.java class includes code to parse and write out the data about the Amazon customer.

This recipe includes two MapReduce tasks. The source for those tasks can be found from src/chapter8/MostFrequentUserFinder.java and src/chapter8/ContentBasedRecommendation.java.

public void map(Object key, Text value, Context context) 
  throws IOException, InterruptedException
{
  List<AmazonCustomer>customerList = 
    AmazonCustomer. parseAItemLine(value.toString());
    for(AmazonCustomer customer:
    customerList){ context.write(new Text(customer.customerID),
      new Text(customer.toString()));
}

The map task of the first MapReduce job receives data about each product in the logfile as a different key-value pair. When the map task receives the product data, it emits the customer ID as the key and the product information as the value for each customer who has bought the product.

Hadoop collects all values for the key, and invokes the reducer once for each key. There will be a reducer task for each customer, and each reducer task will receive all products that have been brought by a customer. The reducer emits the list of items brought by each customer, thus building a customer profile. For limiting the size of the dataset, the reducer will not emit any customer who has brought less than five products.

public void reduce(Text key, Iterable<Text> values, Context context)
  throws IOException, InterruptedException
  {
    AmazonCustomer customer = new AmazonCustomer();
    customer.customerID = key.toString();
    for(Text value: values)
    {
      Set<ItemData>itemsBrought = newAmazonCustomer(
        value.toString()).itemsBrought;
      for(ItemDataitemData: itemsBrought)
      {
        customer.itemsBrought.add(itemData);
      }
    }
    if(customer.itemsBrought.size() > 5)
    {
      context.write( newIntWritable(customer.itemsBrought.size()),
        new Text(customer.toString()));
    }
}

The second MapReduce job uses the data generated from the first MapReduce tasks to make recommendations for each customer. The map task receives data about each customer as the input, and the MapReduce tasks make recommendations using the following three steps:

You can learn more about content-based recommendations in Chapter 9, Recommendation Systems, of Mining of Massive Datasets, Anand Rajaraman and Jeffrey D. Ullman. This book can be found from http://infolab.stanford.edu/~ullman/mmds.html.