NO-JIRA fix markdown and update intro to recommenders page
@@ -8,7 +8,15 @@ title: Recommender Quickstart
# Recommender Overview
-Recommenders have changed over the years. Mahout contains a long list of them, which you can still use. But to get the best out of our more modern aproach we'll need to think of the Recommender as a "model creation" component&mdash;supplied by Mahout's new spark-itemsimilarity job, and a "serving" component&mdash;supplied by a modern scalable search engine, like Solr.
+Recommenders have changed over the years. Mahout contains a long list of them, which you can still use. However in about 2013 there was a revolution in recommenders, which favored what we might call "Multimodal", meaning they could take in data of all sorts&mdash;basically anything we might think was an indicator of user taste. The new Samsara algorithm, called Correlated Cross-Occurrence (CCO) is just such a next gen recommender algorithm but Mahout-Samsara only implements the model building part. This can be integrated as the user see fit and the rest of this doc will explain how.
+
+## Turnkey Implementation
+
+If you are looking for an end-to-end OSS recommender based on the Mahout CCO algorithm have a look at [The Universal Recommender](https://github.com/actionml/universal-recommender), which is implemented using [Apache PredictionIO](http://predictionio.apache.org/). See instructions for [installation here](http://actionml.com/docs/pio_by_actionml). There is even an AWS AMI for convenience (this is a for-pay option)
+
+## Build Your Own Integration
+
+To get the most out of our more modern CCO algorithm we'll need to think of the Recommender as a "model creation" component&mdash;supplied by Mahout's new spark-itemsimilarity job, and a "serving" component&mdash;supplied by a modern scalable search engine, like Solr or Elasticsearch. Here we describe a loose integration that does not require using Mahout as a library, it uses Mahout's command line interface. This is clearly not the best but allows one to experiments and get a real recommender running easily.
![image](http://i.imgur.com/fliHMBo.png)
