Merge pull request #100107 from dagiro/freshness163

freshness163

Author: v-albemi

articles/hdinsight/hadoop/apache-hadoop-mahout-linux-mac.md

@@ -5,9 +5,9 @@ author: hrasheed-msft
 ms.author: hrasheed
 ms.reviewer: jasonh
 ms.service: hdinsight
-ms.custom: hdinsightactive
 ms.topic: conceptual
-ms.date: 04/24/2019
+ms.custom: hdinsightactive
+ms.date: 01/03/2020
 ---
 
 # Generate movie recommendations using Apache Mahout with Apache Hadoop in HDInsight (SSH)
@@ -20,15 +20,13 @@ Mahout is a [machine learning](https://en.wikipedia.org/wiki/Machine_learning) l
 
 ## Prerequisites
 
-* An Apache Hadoop cluster on HDInsight. See [Get Started with HDInsight on Linux](./apache-hadoop-linux-tutorial-get-started.md).
-
-* An SSH client. For more information, see [Connect to HDInsight (Apache Hadoop) using SSH](../hdinsight-hadoop-linux-use-ssh-unix.md).
+An Apache Hadoop cluster on HDInsight. See [Get Started with HDInsight on Linux](./apache-hadoop-linux-tutorial-get-started.md).
 
 ## Apache Mahout versioning
 
 For more information about the version of Mahout in HDInsight, see [HDInsight versions and Apache Hadoop components](../hdinsight-component-versioning.md).
 
-## <a name="recommendations"></a>Understanding recommendations
+## Understanding recommendations
 
 One of the functions that is provided by Mahout is a recommendation engine. This engine accepts data in the format of `userID`, `itemId`, and `prefValue` (the preference for the item). Mahout can then perform co-occurrence analysis to determine: *users who have a preference for an item also have a preference for these other items*. Mahout then determines users with like-item preferences, which can be used to make recommendations.
 
@@ -38,15 +36,15 @@ The following workflow is a simplified example that uses movie data:
 
 * **Co-occurrence**: Bob and Alice also liked *The Phantom Menace*, *Attack of the Clones*, and *Revenge of the Sith*. Mahout determines that users who liked the previous three movies also like these three movies.
 
-* **Similarity recommendation**: Because Joe liked the first three movies, Mahout looks at movies that others with similar preferences liked, but Joe has not watched (liked/rated). In this case, Mahout recommends *The Phantom Menace*, *Attack of the Clones*, and *Revenge of the Sith*.
+* **Similarity recommendation**: Because Joe liked the first three movies, Mahout looks at movies that others with similar preferences liked, but Joe hasn't watched (liked/rated). In this case, Mahout recommends *The Phantom Menace*, *Attack of the Clones*, and *Revenge of the Sith*.
 
 ### Understanding the data
 
 Conveniently, [GroupLens Research](https://grouplens.org/datasets/movielens/) provides rating data for movies in a format that is compatible with Mahout. This data is available on your cluster's default storage at `/HdiSamples/HdiSamples/MahoutMovieData`.
 
 There are two files, `moviedb.txt` and `user-ratings.txt`. The `user-ratings.txt` file is used during analysis. The `moviedb.txt` is used to provide user-friendly text information when viewing the results.
 
-The data contained in user-ratings.txt has a structure of `userID`, `movieID`, `userRating`, and `timestamp`, which indicates how highly each user rated a movie. Here is an example of the data:
+The data contained in `user-ratings.txt` has a structure of `userID`, `movieID`, `userRating`, and `timestamp`, which indicates how highly each user rated a movie. Here is an example of the data:
 
     196    242    3    881250949
     186    302    3    891717742
@@ -56,11 +54,17 @@ The data contained in user-ratings.txt has a structure of `userID`, `movieID`, `
 
 ## Run the analysis
 
-From an SSH connection to the cluster, use the following command to run the recommendation job:
+1. Use [ssh command](../hdinsight-hadoop-linux-use-ssh-unix.md) to connect to your cluster. Edit the command below by replacing CLUSTERNAME with the name of your cluster, and then enter the command:
 
-```bash
-mahout recommenditembased -s SIMILARITY_COOCCURRENCE -i /HdiSamples/HdiSamples/MahoutMovieData/user-ratings.txt -o /example/data/mahoutout --tempDir /temp/mahouttemp
-```
+    ```cmd
+    ssh [email protected]
+    ```
+
+1. Use the following command to run the recommendation job:
+
+    ```bash
+    mahout recommenditembased -s SIMILARITY_COOCCURRENCE -i /HdiSamples/HdiSamples/MahoutMovieData/user-ratings.txt -o /example/data/mahoutout --tempDir /temp/mahouttemp
+    ```
 
 > [!NOTE]  
 > The job may take several minutes to complete, and may run multiple MapReduce jobs.
@@ -75,10 +79,12 @@ mahout recommenditembased -s SIMILARITY_COOCCURRENCE -i /HdiSamples/HdiSamples/M
 
     The output appears as follows:
 
-        1    [234:5.0,347:5.0,237:5.0,47:5.0,282:5.0,275:5.0,88:5.0,515:5.0,514:5.0,121:5.0]
-        2    [282:5.0,210:5.0,237:5.0,234:5.0,347:5.0,121:5.0,258:5.0,515:5.0,462:5.0,79:5.0]
-        3    [284:5.0,285:4.828125,508:4.7543354,845:4.75,319:4.705128,124:4.7045455,150:4.6938777,311:4.6769233,248:4.65625,272:4.649266]
-        4    [690:5.0,12:5.0,234:5.0,275:5.0,121:5.0,255:5.0,237:5.0,895:5.0,282:5.0,117:5.0]
+    ```output
+    1    [234:5.0,347:5.0,237:5.0,47:5.0,282:5.0,275:5.0,88:5.0,515:5.0,514:5.0,121:5.0]
+    2    [282:5.0,210:5.0,237:5.0,234:5.0,347:5.0,121:5.0,258:5.0,515:5.0,462:5.0,79:5.0]
+    3    [284:5.0,285:4.828125,508:4.7543354,845:4.75,319:4.705128,124:4.7045455,150:4.6938777,311:4.6769233,248:4.65625,272:4.649266]
+    4    [690:5.0,12:5.0,234:5.0,275:5.0,121:5.0,255:5.0,237:5.0,895:5.0,282:5.0,117:5.0]
+    ```
 
     The first column is the `userID`. The values contained in '[' and ']' are `movieId`:`recommendationScore`.
 
@@ -169,19 +175,21 @@ mahout recommenditembased -s SIMILARITY_COOCCURRENCE -i /HdiSamples/HdiSamples/M
 
      The output from this command is similar to the following text:
 
-       Seven Years in Tibet (1997), score=5.0
-       Indiana Jones and the Last Crusade (1989), score=5.0
-       Jaws (1975), score=5.0
-       Sense and Sensibility (1995), score=5.0
-       Independence Day (ID4) (1996), score=5.0
-       My Best Friend's Wedding (1997), score=5.0
-       Jerry Maguire (1996), score=5.0
-       Scream 2 (1997), score=5.0
-       Time to Kill, A (1996), score=5.0
+        ```output
+        Seven Years in Tibet (1997), score=5.0
+        Indiana Jones and the Last Crusade (1989), score=5.0
+        Jaws (1975), score=5.0
+        Sense and Sensibility (1995), score=5.0
+        Independence Day (ID4) (1996), score=5.0
+        My Best Friend's Wedding (1997), score=5.0
+        Jerry Maguire (1996), score=5.0
+        Scream 2 (1997), score=5.0
+        Time to Kill, A (1996), score=5.0
+        ```
 
 ## Delete temporary data
 
-Mahout jobs do not remove temporary data that is created while processing the job. The `--tempDir` parameter is specified in the example job to isolate the temporary files into a specific path for easy deletion. To remove the temp files, use the following command:
+Mahout jobs don't remove temporary data that is created while processing the job. The `--tempDir` parameter is specified in the example job to isolate the temporary files into a specific path for easy deletion. To remove the temp files, use the following command:
 
 ```bash
 hdfs dfs -rm -f -r /temp/mahouttemp
@@ -192,11 +200,9 @@ hdfs dfs -rm -f -r /temp/mahouttemp
 >
 > `hdfs dfs -rm -f -r /example/data/mahoutout`
 
-
 ## Next steps
 
-Now that you have learned how to use Mahout, discover other ways of working with data on HDInsight:
+Now that you've learned how to use Mahout, discover other ways of working with data on HDInsight:
 
 * [Apache Hive with HDInsight](hdinsight-use-hive.md)
-* [Apache Pig with HDInsight](hdinsight-use-pig.md)
-* [MapReduce with HDInsight](hdinsight-use-mapreduce.md)
+* [MapReduce with HDInsight](hdinsight-use-mapreduce.md)