Merge pull request #115271 from dagiro/freshness_c71

freshness_c71

Author: Jak-MS

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

@@ -1,31 +1,27 @@
 ---
 title: Generate recommendations using Apache Mahout in Azure HDInsight
-description: Learn how to use the Apache Mahout machine learning library to generate movie recommendations with HDInsight (Hadoop).
+description: Learn how to use the Apache Mahout machine learning library to generate movie recommendations with HDInsight.
 author: hrasheed-msft
 ms.author: hrasheed
 ms.reviewer: jasonh
 ms.service: hdinsight
 ms.topic: conceptual
-ms.custom: hdinsightactive
-ms.date: 01/03/2020
+ms.custom: hdinsightactive,seoapr2020
+ms.date: 05/14/2020
 ---
 
-# Generate movie recommendations using Apache Mahout with Apache Hadoop in HDInsight (SSH)
-
-[!INCLUDE [mahout-selector](../../../includes/hdinsight-selector-mahout.md)]
+# Generate recommendations using Apache Mahout in Azure HDInsight
 
 Learn how to use the [Apache Mahout](https://mahout.apache.org) machine learning library with Azure HDInsight to generate movie recommendations.
 
 Mahout is a [machine learning](https://en.wikipedia.org/wiki/Machine_learning) library for Apache Hadoop. Mahout contains algorithms for processing data, such as filtering, classification, and clustering. In this article, you use a recommendation engine to generate movie recommendations that are based on movies your friends have seen.
 
+For more information about the version of Mahout in HDInsight, see [HDInsight versions and Apache Hadoop components](../hdinsight-component-versioning.md).
+
 ## Prerequisites
 
 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).
-
 ## 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.