http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/9fe018b6/templates/productranking/quickstart/index.html
----------------------------------------------------------------------
diff --git a/templates/productranking/quickstart/index.html 
b/templates/productranking/quickstart/index.html
index 6d97cfe..c6924fe 100644
--- a/templates/productranking/quickstart/index.html
+++ b/templates/productranking/quickstart/index.html
@@ -32,7 +32,7 @@ Your system is all ready to go.
 <span class="o">[</span>INFO] <span class="o">[</span>App<span 
class="nv">$]</span>               MyApp1 |    1 | 
3mZWDzci2D5YsqAnqNnXH9SB6Rg3dsTBs8iHkK6X2i54IQsIZI1eEeQQyMfs7b3F | <span 
class="o">(</span>all<span class="o">)</span>
 <span class="o">[</span>INFO] <span class="o">[</span>App<span 
class="nv">$]</span>               MyApp2 |    2 | 
io5lz6Eg4m3Xe4JZTBFE13GMAf1dhFl6ZteuJfrO84XpdOz9wRCrDU44EUaYuXq5 | <span 
class="o">(</span>all<span class="o">)</span>
 <span class="o">[</span>INFO] <span class="o">[</span>App<span 
class="nv">$]</span> Finished listing 2 app<span class="o">(</span>s<span 
class="o">)</span>.
-</pre></td></tr></tbody></table> </div> <p><a href="#"></a></p> <h2 
id='4.-collecting-data' class='header-anchors'>4. Collecting Data</h2><p>Next, 
let&#39;s collect some training data for the app of this Engine. By default, 
the Product Ranking Engine Template supports 2 types of entities: 
<strong>user</strong> and <strong>item</strong>, and event 
<strong>view</strong>. A user can view an item. This template requires 
&#39;$set&#39; user event, &#39;$set&#39; item event and user-view-item 
events.</p> <p>You can send these events to PredictionIO Event Server in 
real-time easily by making a HTTP request or through the provided SDK. Please 
see <a href="/appintegration/">App Integration Overview</a> for more details 
how to integrate your app with SDK.</p><p>Let&#39;s try sending events to 
EventServer with the following <code>curl</code> commands (The corresponding 
SDK code is showed in other tabs).</p><p>Replace 
<code>&lt;ACCCESS_KEY&gt;</code> by the Access Key generated in above steps. 
 Note that <code>localhost:7070</code> is the default URL of the Event 
Server.</p><p>For convenience, set your access key to the shell variable, 
run:</p><p><code>$ ACCESS_KEY=&lt;ACCESS_KEY&gt;</code></p> <p><a 
href="#"></a></p> <p>For example, when a new user with id &quot;u0&quot; is 
created in your app on time <code>2014-11-02T09:39:45.618-08:00</code> (current 
time will be used if eventTime is not specified), you can send a 
<code>$set</code> event for this user. To send this event, run the following 
<code>curl</code> command:</p><div class="tabs"> <ul class="control"> <li 
data-lang="json"><a href="#tab-4c1ffc0e-a65a-482e-8434-891f254db660">REST 
API</a></li> <li data-lang="python"><a 
href="#tab-089201ba-5d33-458d-8c82-b5f3eb1e404c">Python SDK</a></li> <li 
data-lang="php"><a href="#tab-e2ef4c0c-95d3-4ed3-89a2-0abae5017a40">PHP 
SDK</a></li> <li data-lang="ruby"><a 
href="#tab-d6b8ed68-cb24-4a55-be69-eaed1deee57f">Ruby SDK</a></li> <li 
data-lang="java"><a href="#tab-64d78c9a-079d-4de9
 -8035-e80ad709dd3e">Java SDK</a></li> </ul> <div data-tab="REST API" 
data-lang="json" id="tab-4c1ffc0e-a65a-482e-8434-891f254db660"> <div 
class="highlight shell"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td></tr></tbody></table> </div> <p><a href="#"></a></p> <h2 
id='4.-collecting-data' class='header-anchors'>4. Collecting Data</h2><p>Next, 
let&#39;s collect some training data for the app of this Engine. By default, 
the Product Ranking Engine Template supports 2 types of entities: 
<strong>user</strong> and <strong>item</strong>, and event 
<strong>view</strong>. A user can view an item. This template requires 
&#39;$set&#39; user event, &#39;$set&#39; item event and user-view-item 
events.</p> <p>You can send these events to PredictionIO Event Server in 
real-time easily by making a HTTP request or through the provided SDK. Please 
see <a href="/appintegration/">App Integration Overview</a> for more details 
how to integrate your app with SDK.</p><p>Let&#39;s try sending events to 
EventServer with the following <code>curl</code> commands (The corresponding 
SDK code is showed in other tabs).</p><p>Replace 
<code>&lt;ACCCESS_KEY&gt;</code> by the Access Key generated in above steps. 
 Note that <code>localhost:7070</code> is the default URL of the Event 
Server.</p><p>For convenience, set your access key to the shell variable, 
run:</p><p><code>$ ACCESS_KEY=&lt;ACCESS_KEY&gt;</code></p> <p><a 
href="#"></a></p> <p>For example, when a new user with id &quot;u0&quot; is 
created in your app on time <code>2014-11-02T09:39:45.618-08:00</code> (current 
time will be used if eventTime is not specified), you can send a 
<code>$set</code> event for this user. To send this event, run the following 
<code>curl</code> command:</p><div class="tabs"> <ul class="control"> <li 
data-lang="json"><a href="#tab-2bb82c5f-5151-465d-9722-5016f073897b">REST 
API</a></li> <li data-lang="python"><a 
href="#tab-003c26cf-5e2d-4559-8d30-2ca40fc007e4">Python SDK</a></li> <li 
data-lang="php"><a href="#tab-e0bb0a6d-de10-45ee-a74c-f51be724982c">PHP 
SDK</a></li> <li data-lang="ruby"><a 
href="#tab-2b43116f-0cbe-4f97-a800-d2917715a817">Ruby SDK</a></li> <li 
data-lang="java"><a href="#tab-b958944b-91ec-45d7
 -b3bd-da02f4f6bec6">Java SDK</a></li> </ul> <div data-tab="REST API" 
data-lang="json" id="tab-2bb82c5f-5151-465d-9722-5016f073897b"> <div 
class="highlight shell"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -47,7 +47,7 @@ Your system is all ready to go.
   "entityId" : "u0",
   "eventTime" : "2014-11-02T09:39:45.618-08:00"
 }'</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" 
data-lang="python" id="tab-089201ba-5d33-458d-8c82-b5f3eb1e404c"> <div 
class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" 
data-lang="python" id="tab-003c26cf-5e2d-4559-8d30-2ca40fc007e4"> <div 
class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -78,7 +78,7 @@ Your system is all ready to go.
   <span class="n">entity_type</span><span class="o">=</span><span 
class="s">"user"</span><span class="p">,</span>
   <span class="n">entity_id</span><span class="o">=&lt;</span><span 
class="n">USER_ID</span><span class="o">&gt;</span>
 <span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" 
data-lang="php" id="tab-e2ef4c0c-95d3-4ed3-89a2-0abae5017a40"> <div 
class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" 
data-lang="php" id="tab-e0bb0a6d-de10-45ee-a74c-f51be724982c"> <div 
class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -103,7 +103,7 @@ Your system is all ready to go.
   <span class="s1">'entityId'</span> <span class="o">=&gt;</span> <span 
class="o">&lt;</span><span class="nx">USER</span> <span 
class="nx">ID</span><span class="o">&gt;</span>
 <span class="p">));</span>
 <span class="cp">?&gt;</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" 
data-lang="ruby" id="tab-d6b8ed68-cb24-4a55-be69-eaed1deee57f"> <div 
class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" 
data-lang="ruby" id="tab-2b43116f-0cbe-4f97-a800-d2917715a817"> <div 
class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -120,7 +120,7 @@ Your system is all ready to go.
   <span class="s1">'user'</span><span class="p">,</span>
   <span class="o">&lt;</span><span class="no">USER</span> <span 
class="no">ID</span><span class="o">&gt;</span>
 <span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" 
data-lang="java" id="tab-64d78c9a-079d-4de9-8035-e80ad709dd3e"> <div 
class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" 
data-lang="java" id="tab-b958944b-91ec-45d7-b3bd-da02f4f6bec6"> <div 
class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -145,7 +145,7 @@ Your system is all ready to go.
   <span class="o">.</span><span class="na">entityType</span><span 
class="o">(</span><span class="s">"user"</span><span class="o">)</span>
   <span class="o">.</span><span class="na">entityId</span><span 
class="o">(&lt;</span><span class="n">USER_ID</span><span 
class="o">&gt;);</span>
 <span class="n">client</span><span class="o">.</span><span 
class="na">createEvent</span><span class="o">(</span><span 
class="n">userEvent</span><span class="o">);</span>
-</pre></td> </tr></tbody></table> </div> </div> </div> <p>When a new item 
&quot;i0&quot; is created in your app on time 
<code>2014-11-02T09:39:45.618-08:00</code> (current time will be used if 
eventTime is not specified), you can send a <code>$set</code> event for the 
item. Run the following <code>curl</code> command:</p><div class="tabs"> <ul 
class="control"> <li data-lang="json"><a 
href="#tab-0b9829e1-cbe8-47e7-abe5-7a199adc658b">REST API</a></li> <li 
data-lang="python"><a href="#tab-cf24fdc0-898a-4b0f-ac6f-e175dd590439">Python 
SDK</a></li> <li data-lang="php"><a 
href="#tab-7110b2a4-2be5-4d35-94fb-eb71278ea0a7">PHP SDK</a></li> <li 
data-lang="ruby"><a href="#tab-3862ede1-a99b-451f-ba55-b51bc29899a0">Ruby 
SDK</a></li> <li data-lang="java"><a 
href="#tab-26c462a0-35d3-409f-bbf1-2f5735650158">Java SDK</a></li> </ul> <div 
data-tab="REST API" data-lang="json" 
id="tab-0b9829e1-cbe8-47e7-abe5-7a199adc658b"> <div class="highlight shell"> 
<table style="border-spacing: 0"><tbody><tr> <td cla
 ss="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> </div> <p>When a new item 
&quot;i0&quot; is created in your app on time 
<code>2014-11-02T09:39:45.618-08:00</code> (current time will be used if 
eventTime is not specified), you can send a <code>$set</code> event for the 
item. Run the following <code>curl</code> command:</p><div class="tabs"> <ul 
class="control"> <li data-lang="json"><a 
href="#tab-ce99a60b-e560-4ff7-b320-8320c68d1ef0">REST API</a></li> <li 
data-lang="python"><a href="#tab-82fba617-7af8-433d-b2e8-39e0896de3fb">Python 
SDK</a></li> <li data-lang="php"><a 
href="#tab-d97b59b0-b770-4f07-84db-89e97c3786c5">PHP SDK</a></li> <li 
data-lang="ruby"><a href="#tab-058ea976-b6df-4fe4-a5a1-6d6a71a4e118">Ruby 
SDK</a></li> <li data-lang="java"><a 
href="#tab-ac205d09-8dcf-4ec3-bd00-8e0ba2e13590">Java SDK</a></li> </ul> <div 
data-tab="REST API" data-lang="json" 
id="tab-ce99a60b-e560-4ff7-b320-8320c68d1ef0"> <div class="highlight shell"> 
<table style="border-spacing: 0"><tbody><tr> <td cla
 ss="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -160,7 +160,7 @@ Your system is all ready to go.
   "entityId" : "i0",
   "eventTime" : "2014-11-02T09:39:45.618-08:00"
 }'</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" 
data-lang="python" id="tab-cf24fdc0-898a-4b0f-ac6f-e175dd590439"> <div 
class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" 
data-lang="python" id="tab-82fba617-7af8-433d-b2e8-39e0896de3fb"> <div 
class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -173,7 +173,7 @@ Your system is all ready to go.
   <span class="n">entity_type</span><span class="o">=</span><span 
class="s">"item"</span><span class="p">,</span>
   <span class="n">entity_id</span><span class="o">=&lt;</span><span 
class="n">ITEM_ID</span><span class="o">&gt;</span>
 <span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" 
data-lang="php" id="tab-7110b2a4-2be5-4d35-94fb-eb71278ea0a7"> <div 
class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" 
data-lang="php" id="tab-d97b59b0-b770-4f07-84db-89e97c3786c5"> <div 
class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -190,7 +190,7 @@ Your system is all ready to go.
 <span class="p">));</span>
 
 <span class="cp">?&gt;</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" 
data-lang="ruby" id="tab-3862ede1-a99b-451f-ba55-b51bc29899a0"> <div 
class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" 
data-lang="ruby" id="tab-058ea976-b6df-4fe4-a5a1-6d6a71a4e118"> <div 
class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -203,7 +203,7 @@ Your system is all ready to go.
   <span class="o">&lt;</span><span class="no">ITEM</span> <span 
class="no">ID</span><span class="o">&gt;</span>
 <span class="p">)</span>
 
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" 
data-lang="java" id="tab-26c462a0-35d3-409f-bbf1-2f5735650158"> <div 
class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" 
data-lang="java" id="tab-ac205d09-8dcf-4ec3-bd00-8e0ba2e13590"> <div 
class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -214,7 +214,7 @@ Your system is all ready to go.
   <span class="o">.</span><span class="na">entityType</span><span 
class="o">(</span><span class="s">"item"</span><span class="o">)</span>
   <span class="o">.</span><span class="na">entityId</span><span 
class="o">(&lt;</span><span class="n">ITEM_ID</span><span 
class="o">&gt;);</span>
 <span class="n">client</span><span class="o">.</span><span 
class="na">createEvent</span><span class="o">(</span><span 
class="n">itemEvent</span><span class="o">)</span>
-</pre></td> </tr></tbody></table> </div> </div> </div> <p>When the user 
&quot;u0&quot; view item &quot;i0&quot; on time 
<code>2014-11-10T12:34:56.123-08:00</code> (current time will be used if 
eventTime is not specified), you can send a view event. Run the following 
<code>curl</code> command:</p><div class="tabs"> <ul class="control"> <li 
data-lang="json"><a href="#tab-b3b22b1f-d83b-4584-8303-a404d92f91fd">REST 
API</a></li> <li data-lang="python"><a 
href="#tab-f0c87f47-f3c4-49e8-8910-a010a992dbdf">Python SDK</a></li> <li 
data-lang="php"><a href="#tab-93b7e5ed-ea83-4686-9032-d3c68eff6f7e">PHP 
SDK</a></li> <li data-lang="ruby"><a 
href="#tab-4d29ccc1-c9ed-4425-a807-2756a0b25cb4">Ruby SDK</a></li> <li 
data-lang="java"><a href="#tab-141a88f8-2428-4a50-bc48-33463e493774">Java 
SDK</a></li> </ul> <div data-tab="REST API" data-lang="json" 
id="tab-b3b22b1f-d83b-4584-8303-a404d92f91fd"> <div class="highlight shell"> 
<table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text
 -align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> </div> <p>When the user 
&quot;u0&quot; view item &quot;i0&quot; on time 
<code>2014-11-10T12:34:56.123-08:00</code> (current time will be used if 
eventTime is not specified), you can send a view event. Run the following 
<code>curl</code> command:</p><div class="tabs"> <ul class="control"> <li 
data-lang="json"><a href="#tab-78a2d46c-907a-4d38-b093-c105949d9cc5">REST 
API</a></li> <li data-lang="python"><a 
href="#tab-2c3ec4d5-78c5-47a1-a7b6-9aaa058522a5">Python SDK</a></li> <li 
data-lang="php"><a href="#tab-700f4c35-1170-40d9-9baf-434f62a5e04d">PHP 
SDK</a></li> <li data-lang="ruby"><a 
href="#tab-1aebdfa8-cfc5-4627-ae12-5719ef0c8cc6">Ruby SDK</a></li> <li 
data-lang="java"><a href="#tab-8e940c62-6b7e-45bd-b12c-0171bdbf41e4">Java 
SDK</a></li> </ul> <div data-tab="REST API" data-lang="json" 
id="tab-78a2d46c-907a-4d38-b093-c105949d9cc5"> <div class="highlight shell"> 
<table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text
 -align: right"><pre class="lineno">1
 2
 3
 4
@@ -233,7 +233,7 @@ Your system is all ready to go.
   "targetEntityId" : "i0",
   "eventTime" : "2014-11-10T12:34:56.123-08:00"
 }'</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" 
data-lang="python" id="tab-f0c87f47-f3c4-49e8-8910-a010a992dbdf"> <div 
class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" 
data-lang="python" id="tab-2c3ec4d5-78c5-47a1-a7b6-9aaa058522a5"> <div 
class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -250,7 +250,7 @@ Your system is all ready to go.
   <span class="n">target_entity_type</span><span class="o">=</span><span 
class="s">"item"</span><span class="p">,</span>
   <span class="n">target_entity_id</span><span class="o">=&lt;</span><span 
class="n">ITEM</span> <span class="n">ID</span><span class="o">&gt;</span>
 <span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" 
data-lang="php" id="tab-93b7e5ed-ea83-4686-9032-d3c68eff6f7e"> <div 
class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" 
data-lang="php" id="tab-700f4c35-1170-40d9-9baf-434f62a5e04d"> <div 
class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -269,7 +269,7 @@ Your system is all ready to go.
    <span class="s1">'targetEntityId'</span> <span class="o">=&gt;</span> <span 
class="o">&lt;</span><span class="nx">ITEM</span> <span 
class="nx">ID</span><span class="o">&gt;</span>
 <span class="p">));</span>
 <span class="cp">?&gt;</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" 
data-lang="ruby" id="tab-4d29ccc1-c9ed-4425-a807-2756a0b25cb4"> <div 
class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" 
data-lang="ruby" id="tab-1aebdfa8-cfc5-4627-ae12-5719ef0c8cc6"> <div 
class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -286,7 +286,7 @@ Your system is all ready to go.
     <span class="s1">'targetEntityId'</span> <span class="o">=&gt;</span> 
<span class="o">&lt;</span><span class="no">ITEM</span> <span 
class="no">ID</span><span class="o">&gt;</span>
   <span class="p">}</span>
 <span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" 
data-lang="java" id="tab-141a88f8-2428-4a50-bc48-33463e493774"> <div 
class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" 
data-lang="java" id="tab-8e940c62-6b7e-45bd-b12c-0171bdbf41e4"> <div 
class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -344,14 +344,14 @@ User u10 views item i7
 </pre></td></tr></tbody></table> </div> <p>When the engine is deployed 
successfully and running, you should see a console message similar to the 
following:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1
 2</pre></td><td class="code"><pre><span class="o">[</span>INFO] <span 
class="o">[</span>HttpListener] Bound to /0.0.0.0:8000
 <span class="o">[</span>INFO] <span class="o">[</span>MasterActor] Bind 
successful. Ready to serve.
-</pre></td></tr></tbody></table> </div> <p>Do not kill the deployed engine 
process.</p><p>By default, the deployed engine binds to <a 
href="http://localhost:8000";><a 
href="http://localhost:8000";>http://localhost:8000</a></a>. You can visit that 
page in your web browser to check its status.</p><p><img alt="Engine Status" 
src="/images/engine-server-3246414b.png"/></p></p><h2 id='6.-use-the-engine' 
class='header-anchors'>6. Use the Engine</h2><p>Now, You can retrieve predicted 
results. To rank items with IDs &quot;i1&quot;, &quot;i3&quot;, 
&quot;i10&quot;, &quot;i2&quot;, &quot;i5&quot;, &quot;i31&quot;, 
&quot;i9&quot; for user &quot;u2&quot;. You send this JSON <code>{ 
&quot;user&quot;: &quot;u2&quot;, &quot;items&quot;: [&quot;i1&quot;, 
&quot;i3&quot;, &quot;i10&quot;, &quot;i2&quot;, &quot;i5&quot;, 
&quot;i31&quot;, &quot;i9&quot;] }</code> to the deployed engine and it will 
return a JSON of the ranked items. Simply send a query by making a HTTP request 
or through the <code>EngineCl
 ient</code> of an SDK.</p><p>With the deployed engine running, open another 
terminal and run the following <code>curl</code> command or use SDK to send the 
query:</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a 
href="#tab-47d490aa-146a-4663-a5ce-656b36ebca04">REST API</a></li> <li 
data-lang="python"><a href="#tab-9dd2f54e-8f7d-4168-971d-62270b6e2ad0">Python 
SDK</a></li> <li data-lang="php"><a 
href="#tab-3cc211d0-1fdc-4a9f-ad73-e853dc77b67e">PHP SDK</a></li> <li 
data-lang="ruby"><a href="#tab-999bf93c-90de-43c4-bb22-a6e2408e081e">Ruby 
SDK</a></li> <li data-lang="java"><a 
href="#tab-faa847f6-6d49-4d16-abe1-d3d377ea61e9">Java SDK</a></li> </ul> <div 
data-tab="REST API" data-lang="json" 
id="tab-47d490aa-146a-4663-a5ce-656b36ebca04"> <div class="highlight shell"> 
<table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
+</pre></td></tr></tbody></table> </div> <p>Do not kill the deployed engine 
process.</p><p>By default, the deployed engine binds to <a 
href="http://localhost:8000";><a 
href="http://localhost:8000";>http://localhost:8000</a></a>. You can visit that 
page in your web browser to check its status.</p><p><img alt="Engine Status" 
src="/images/engine-server-3246414b.png"/></p></p><h2 id='6.-use-the-engine' 
class='header-anchors'>6. Use the Engine</h2><p>Now, You can retrieve predicted 
results. To rank items with IDs &quot;i1&quot;, &quot;i3&quot;, 
&quot;i10&quot;, &quot;i2&quot;, &quot;i5&quot;, &quot;i31&quot;, 
&quot;i9&quot; for user &quot;u2&quot;. You send this JSON <code>{ 
&quot;user&quot;: &quot;u2&quot;, &quot;items&quot;: [&quot;i1&quot;, 
&quot;i3&quot;, &quot;i10&quot;, &quot;i2&quot;, &quot;i5&quot;, 
&quot;i31&quot;, &quot;i9&quot;] }</code> to the deployed engine and it will 
return a JSON of the ranked items. Simply send a query by making a HTTP request 
or through the <code>EngineCl
 ient</code> of an SDK.</p><p>With the deployed engine running, open another 
terminal and run the following <code>curl</code> command or use SDK to send the 
query:</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a 
href="#tab-967ff989-f440-4108-94b1-5eb7f8c1af9a">REST API</a></li> <li 
data-lang="python"><a href="#tab-3fdceca5-3645-46f2-9bb3-28c11fe8dbe1">Python 
SDK</a></li> <li data-lang="php"><a 
href="#tab-b74462d2-81de-4a87-9da3-d9e6ff704357">PHP SDK</a></li> <li 
data-lang="ruby"><a href="#tab-93504f25-577b-4887-8e0a-319f692260b2">Ruby 
SDK</a></li> <li data-lang="java"><a 
href="#tab-59a355fe-d73f-4fa0-885c-29d7b6d66f24">Java SDK</a></li> </ul> <div 
data-tab="REST API" data-lang="json" 
id="tab-967ff989-f440-4108-94b1-5eb7f8c1af9a"> <div class="highlight shell"> 
<table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
 2
 3
 4</pre></td> <td class="code"><pre><span class="gp">$ </span>curl -H <span 
class="s2">"Content-Type: application/json"</span> <span class="se">\</span>
 -d <span class="s1">'{ "user": "u2", "items": ["i1", "i3", "i10", "i2", "i5", 
"i31", "i9"]}'</span> <span class="se">\</span>
 http://localhost:8000/queries.json
 
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" 
data-lang="python" id="tab-9dd2f54e-8f7d-4168-971d-62270b6e2ad0"> <div 
class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" 
data-lang="python" id="tab-3fdceca5-3645-46f2-9bb3-28c11fe8dbe1"> <div 
class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -362,7 +362,7 @@ http://localhost:8000/queries.json
   <span class="s">"user"</span><span class="p">:</span> <span 
class="s">"u2"</span><span class="p">,</span>
   <span class="s">"items"</span><span class="p">:</span> <span 
class="p">[</span><span class="s">"i1"</span><span class="p">,</span> <span 
class="s">"i3"</span><span class="p">,</span> <span class="s">"i10"</span><span 
class="p">,</span> <span class="s">"i2"</span><span class="p">,</span> <span 
class="s">"i5"</span><span class="p">,</span> <span class="s">"i31"</span><span 
class="p">,</span> <span class="s">"i9"</span><span class="p">]</span>
 <span class="p">})</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" 
data-lang="php" id="tab-3cc211d0-1fdc-4a9f-ad73-e853dc77b67e"> <div 
class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" 
data-lang="php" id="tab-b74462d2-81de-4a87-9da3-d9e6ff704357"> <div 
class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -385,7 +385,7 @@ http://localhost:8000/queries.json
 <span class="nb">print_r</span><span class="p">(</span><span 
class="nv">$response</span><span class="p">);</span>
 
 <span class="cp">?&gt;</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" 
data-lang="ruby" id="tab-999bf93c-90de-43c4-bb22-a6e2408e081e"> <div 
class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" 
data-lang="ruby" id="tab-93504f25-577b-4887-8e0a-319f692260b2"> <div 
class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4
@@ -402,7 +402,7 @@ http://localhost:8000/queries.json
   <span class="s1">'items'</span> <span class="o">=&gt;</span> <span 
class="p">[</span><span class="s1">'i1'</span><span class="p">,</span> <span 
class="s1">'i3'</span><span class="p">,</span> <span 
class="s1">'i10'</span><span class="p">,</span> <span 
class="s1">'i2'</span><span class="p">,</span> <span 
class="s1">'i5'</span><span class="p">,</span> <span 
class="s1">'i31'</span><span class="p">,</span> <span 
class="s1">'i9'</span><span class="p">])</span>
 
 <span class="nb">puts</span> <span class="n">response</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" 
data-lang="java" id="tab-faa847f6-6d49-4d16-abe1-d3d377ea61e9"> <div 
class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" 
data-lang="java" id="tab-59a355fe-d73f-4fa0-885c-29d7b6d66f24"> <div 
class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td 
class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
 4

http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/9fe018b6/templates/recommendation/batch-evaluator/index.html
----------------------------------------------------------------------
diff --git a/templates/recommendation/batch-evaluator/index.html 
b/templates/recommendation/batch-evaluator/index.html
index 566ae62..6f905ba 100644
--- a/templates/recommendation/batch-evaluator/index.html
+++ b/templates/recommendation/batch-evaluator/index.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html><head><title>Batch Persistable Evaluator 
(Recommendation)</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" 
http-equiv="X-UA-Compatible"/><meta name="viewport" 
content="width=device-width, initial-scale=1.0"/><meta class="swiftype" 
name="title" data-type="string" content="Batch Persistable Evaluator 
(Recommendation)"/><link rel="canonical" 
href="https://predictionio.apache.org/templates/recommendation/batch-evaluator/"/><link
 href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link 
href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link 
href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800"
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href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" 
rel="stylesheet"/><link href="/stylesheets/application-eccfc6cb.css" 
rel="stylesheet" type="text/css"/><script 
src="//cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.2/html
 5shiv.min.js"></script><script 
src="//cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><script
 src="//use.typekit.net/pqo0itb.js"></script><script>try{Typekit.load({ async: 
true });}catch(e){}</script></head><body><div id="global"><header><div 
class="container" id="header-wrapper"><div class="row"><div 
class="col-sm-12"><div id="logo-wrapper"><span id="drawer-toggle"></span><a 
href="#"></a><a href="http://predictionio.apache.org/";><img alt="Apache 
PredictionIO" id="logo" 
src="/images/logos/logo-ee2b9bb3.png"/></a><span>®</span></div><div 
id="menu-wrapper"><div id="pill-wrapper"><a class="pill left" 
href="/gallery/template-gallery">TEMPLATES</a> <a class="pill right" 
href="//github.com/apache/predictionio/">OPEN SOURCE</a></div></div><img 
class="mobile-search-bar-toggler hidden-md hidden-lg" 
src="/images/icons/search-glass-704bd4ff.png"/></div></div></div></header><div 
id="search-bar-row-wrapper"><div class="container-fluid" 
id="search-bar-row"><div c
 lass="row"><div class="col-md-9 col-sm-11 col-xs-11"><div class="hidden-md 
hidden-lg" id="mobile-page-heading-wrapper"><p>PredictionIO Docs</p><h4>Batch 
Persistable Evaluator (Recommendation)</h4></div><h4 class="hidden-sm 
hidden-xs">PredictionIO Docs</h4></div><div class="col-md-3 col-sm-1 col-xs-1 
hidden-md hidden-lg"><img id="left-menu-indicator" 
src="/images/icons/down-arrow-dfe9f7fe.png"/></div><div class="col-md-3 
col-sm-12 col-xs-12 swiftype-wrapper"><div class="swiftype"><form 
class="search-form"><img class="search-box-toggler hidden-xs hidden-sm" 
src="/images/icons/search-glass-704bd4ff.png"/><div class="search-box"><img 
src="/images/icons/search-glass-704bd4ff.png"/><input type="text" 
id="st-search-input" class="st-search-input" placeholder="Search 
Doc..."/></div><img class="swiftype-row-hider hidden-md hidden-lg" 
src="/images/icons/drawer-toggle-active-fcbef12a.png"/></form></div></div><div 
class="mobile-left-menu-toggler hidden-md 
hidden-lg"></div></div></div></div><div 
 id="page" class="container-fluid"><div class="row"><div id="left-menu-wrapper" 
class="col-md-3"><nav id="nav-main"><ul><li class="level-1"><a 
class="expandible" href="/"><span>Apache PredictionIO® 
Documentation</span></a><ul><li class="level-2"><a class="final" 
href="/"><span>Welcome to Apache PredictionIO®</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Getting 
Started</span></a><ul><li class="level-2"><a class="final" 
href="/start/"><span>A Quick Intro</span></a></li><li class="level-2"><a 
class="final" href="/install/"><span>Installing Apache 
PredictionIO</span></a></li><li class="level-2"><a class="final" 
href="/start/download/"><span>Downloading an Engine Template</span></a></li><li 
class="level-2"><a class="final" href="/start/deploy/"><span>Deploying Your 
First Engine</span></a></li><li class="level-2"><a class="final" 
href="/start/customize/"><span>Customizing the 
Engine</span></a></li></ul></li><li class="level-1"><a class="expandible" hr
 ef="#"><span>Integrating with Your App</span></a><ul><li class="level-2"><a 
class="final" href="/appintegration/"><span>App Integration 
Overview</span></a></li><li class="level-2"><a class="expandible" 
href="/sdk/"><span>List of SDKs</span></a><ul><li class="level-3"><a 
class="final" href="/sdk/java/"><span>Java & Android SDK</span></a></li><li 
class="level-3"><a class="final" href="/sdk/php/"><span>PHP 
SDK</span></a></li><li class="level-3"><a class="final" 
href="/sdk/python/"><span>Python SDK</span></a></li><li class="level-3"><a 
class="final" href="/sdk/ruby/"><span>Ruby SDK</span></a></li><li 
class="level-3"><a class="final" href="/sdk/community/"><span>Community Powered 
SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li 
class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web 
Service</span></a></li><li class="level-2"><a class="final" 
href="/batchpredict/"><span>Batch Prediction
 s</span></a></li><li class="level-2"><a class="final" 
href="/deploy/monitoring/"><span>Monitoring Engine</span></a></li><li 
class="level-2"><a class="final" href="/deploy/engineparams/"><span>Setting 
Engine Parameters</span></a></li><li class="level-2"><a class="final" 
href="/deploy/enginevariants/"><span>Deploying Multiple Engine 
Variants</span></a></li><li class="level-2"><a class="final" 
href="/deploy/plugin/"><span>Engine Server Plugin</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Customizing an 
Engine</span></a><ul><li class="level-2"><a class="final" 
href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a 
class="final" href="/customize/dase/"><span>Implement DASE</span></a></li><li 
class="level-2"><a class="final" 
href="/customize/troubleshooting/"><span>Troubleshooting Engine 
Development</span></a></li><li class="level-2"><a class="final" 
href="/api/current/#package"><span>Engine Scala 
APIs</span></a></li></ul></li><li 
 class="level-1"><a class="expandible" href="#"><span>Collecting and Analyzing 
Data</span></a><ul><li class="level-2"><a class="final" 
href="/datacollection/"><span>Event Server Overview</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/eventapi/"><span>Collecting Data with 
REST/SDKs</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/webhooks/"><span>Unifying Multichannel Data with 
Webhooks</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/channel/"><span>Channel</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/batchimport/"><span>Importing Data in 
Batch</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/analytics/"><span>Using Analytics 
Tools</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/plugin/"><span>Event Server Plugin</span></a
 ></li></ul></li><li class="level-1"><a class="expandible" 
 >href="#"><span>Choosing an Algorithm</span></a><ul><li class="level-2"><a 
 >class="final" href="/algorithm/"><span>Built-in Algorithm 
 >Libraries</span></a></li><li class="level-2"><a class="final" 
 >href="/algorithm/switch/"><span>Switching to Another 
 >Algorithm</span></a></li><li class="level-2"><a class="final" 
 >href="/algorithm/multiple/"><span>Combining Multiple 
 >Algorithms</span></a></li><li class="level-2"><a class="final" 
 >href="/algorithm/custom/"><span>Adding Your Own 
 >Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" 
 >href="#"><span>Tuning and Evaluation</span></a><ul><li class="level-2"><a 
 >class="final" href="/evaluation/"><span>Overview</span></a></li><li 
 >class="level-2"><a class="final" 
 >href="/evaluation/paramtuning/"><span>Hyperparameter 
 >Tuning</span></a></li><li class="level-2"><a class="final" 
 >href="/evaluation/evaluationdashboard/"><span>Evaluation 
 >Dashboard</span></a></li><li class="level-2"
 ><a class="final" href="/evaluation/metricchoose/"><span>Choosing Evaluation 
 >Metrics</span></a></li><li class="level-2"><a class="final" 
 >href="/evaluation/metricbuild/"><span>Building Evaluation 
 >Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" 
 >href="#"><span>System Architecture</span></a><ul><li class="level-2"><a 
 >class="final" href="/system/"><span>Architecture Overview</span></a></li><li 
 >class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using 
 >Another Data Store</span></a></li></ul></li><li class="level-1"><a 
 >class="expandible" href="#"><span>PredictionIO® Official 
 >Templates</span></a><ul><li class="level-2"><a class="final" 
 >href="/templates/"><span>Intro</span></a></li><li class="level-2"><a 
 >class="expandible" href="#"><span>Recommendation</span></a><ul><li 
 >class="level-3"><a class="final" 
 >href="/templates/recommendation/quickstart/"><span>Quick 
 >Start</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/recommendatio
 n/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/evaluation/"><span>Evaluation 
Explained</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
Events</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-data-prep/"><span>Customize Data 
Preparator</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-serving/"><span>Customize 
Serving</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/training-with-implicit-preference/"><span>Train 
with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/blacklist-items/"><span>Filter Recommended 
Items by Blacklist in Query</span></a></li><li class="level-3"><
 a class="final active" 
href="/templates/recommendation/batch-evaluator/"><span>Batch Persistable 
Evaluator</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>E-Commerce Recommendation</span></a><ul><li class="level-3"><a 
class="final" href="/templates/ecommercerecommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li
 class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train 
with Rate Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust 
Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Similar Product</span></a><ul><li class="level-3"><a 
class="final" href="/templ
 ates/similarproduct/quickstart/"><span>Quick Start</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
class="final" 
href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
Properties</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate 
Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events 
for Users</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/recommended-user/"><span>Recommend 
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id="page-title"><h1>Batch Persistable Evaluator 
(Recommendation)</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#1-modify-datasource">1. Modify DataSource</a> </li> <li> <a 
href="#2-add-a-new-evaluator">2. Add a new Evaluator</a> </li> <li> <a 
href="#3-define-evaluation-and-engineparamsgenerator-object">3. Define 
Evaluation and EngineParamsGenerator object</a> </li> <li> <a 
href="#4-build-and-run">4. build and run</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/templates/recommendation/batch-evaluator.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">PredictionIO® Official 
Templates</a><span class="spacer
 ">&gt;</span></li><li><a href="#">Recommendation</a><span 
class="spacer">&gt;</span></li><li><span class="last">Batch Persistable 
Evaluator</span></li></ul></div><div id="page-title"><h1>Batch Persistable 
Evaluator (Recommendation)</h1></div></div><div class="content"> <p>This how-to 
tutorial would explain how you can also use <code>$pio eval</code> to persist 
predicted result for a batch of queries. Please read the <a 
href="/templates/recommendation/evaluation/">Evaluation</a> to understand the 
usage of DataSoure&#39;s <code>readEval()</code> and the Evaluation component 
first.</p><div class="alert-message warning"><p>This tutorial is based on some 
experimental and developer features, which may be changed in future 
release.</p></div><div class="alert-message note"><p>This tutorial is based on 
Recommendation template version v0.3.2</p></div><h2 id='1.-modify-datasource' 
class='header-anchors'>1. Modify DataSource</h2><p>Modify DataSource&#39;s 
<code>readEval()</code> to generate the
  batch Queries which you want to run batch predict.</p><div class="highlight 
scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
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 >href="/templates/recommendation/quickstart/"><span>Quick 
 >Start</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/recommendatio
 n/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" 
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Explained</span></a></li><li class="level-3"><a class="final" 
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class="level-3"><a class="final" 
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with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
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Items by Blacklist in Query</span></a></li><li class="level-3"><
 a class="final active" 
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 class="level-3"><a class="final" 
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with Rate Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust 
Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Similar Product</span></a><ul><li class="level-3"><a 
class="final" href="/templ
 ates/similarproduct/quickstart/"><span>Quick Start</span></a></li><li 
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href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
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class="level-3"><a class="final" 
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Properties</span></a></li><li class="level-3"><a class="final" 
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Event</span></a></li><li class="level-3"><a class="final" 
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href="/templates/similarproduct/recommended-user/"><span>Recommend 
Users</span></a></li></ul></li><li class="level-
 2"><a class="expandible" href="#"><span>Classification</span></a><ul><li 
class="level-3"><a class="final" 
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Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/dase/"><span>DASE</span></a></li><li 
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href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">PredictionIO® Official Templates</a><span 
class="spacer">&gt;</span></li><li><a href="#">Recommendation</a><span 
class="spacer">&gt;</span></li><
 li><span class="last">Batch Persistable Evaluator</span></li></ul></div><div 
id="page-title"><h1>Batch Persistable Evaluator 
(Recommendation)</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#1-modify-datasource">1. Modify DataSource</a> </li> <li> <a 
href="#2-add-a-new-evaluator">2. Add a new Evaluator</a> </li> <li> <a 
href="#3-define-evaluation-and-engineparamsgenerator-object">3. Define 
Evaluation and EngineParamsGenerator object</a> </li> <li> <a 
href="#4-build-and-run">4. build and run</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/templates/recommendation/batch-evaluator.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">PredictionIO® Official 
Templates</a><span class="spacer
 ">&gt;</span></li><li><a href="#">Recommendation</a><span 
class="spacer">&gt;</span></li><li><span class="last">Batch Persistable 
Evaluator</span></li></ul></div><div id="page-title"><h1>Batch Persistable 
Evaluator (Recommendation)</h1></div></div><div class="content"> <p>This how-to 
tutorial would explain how you can also use <code>$pio eval</code> to persist 
predicted result for a batch of queries. Please read the <a 
href="/templates/recommendation/evaluation/">Evaluation</a> to understand the 
usage of DataSource&#39;s <code>readEval()</code> and the Evaluation component 
first.</p><div class="alert-message warning"><p>This tutorial is based on some 
experimental and developer features, which may be changed in future 
release.</p></div><div class="alert-message note"><p>This tutorial is based on 
Recommendation template version v0.3.2</p></div><h2 id='1.-modify-datasource' 
class='header-anchors'>1. Modify DataSource</h2><p>Modify DataSource&#39;s 
<code>readEval()</code> to generate th
 e batch Queries which you want to run batch predict.</p><div class="highlight 
scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
 2
 3
 4

http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/9fe018b6/templates/recommendation/dase/index.html
----------------------------------------------------------------------
diff --git a/templates/recommendation/dase/index.html 
b/templates/recommendation/dase/index.html
index eb6cddf..e802c20 100644
--- a/templates/recommendation/dase/index.html
+++ b/templates/recommendation/dase/index.html
@@ -313,7 +313,7 @@ val itemStringIntMap <span class="o">=</span> 
BiMap.stringInt<span class="o">(</
   <span class="n">numIterations</span><span class="k">:</span> <span 
class="kt">Int</span><span class="o">,</span>
   <span class="n">lambda</span><span class="k">:</span> <span 
class="kt">Double</span><span class="o">,</span>
   <span class="n">seed</span><span class="k">:</span> <span 
class="kt">Option</span><span class="o">[</span><span 
class="kt">Long</span><span class="o">])</span> <span class="k">extends</span> 
<span class="nc">Params</span>
-</pre></td></tr></tbody></table> </div> <p>The <code>seed</code> parameter is 
an optional parameter, which is used by MLlib ALS algorithm internally to 
generate random values. If the <code>seed</code> is not specified, current 
system time would be used and hence each train may produce different reuslts. 
Specify a fixed value for the <code>seed</code> if you want to have 
deterministic result (For example, when you are 
testing).</p><p><code>ALS.train</code> then returns a 
<code>MatrixFactorizationModel</code> model which contains RDD data. RDD is a 
distributed collection of items which <em>does not</em> persist. To store the 
model, you convert the model to <code>ALSModel</code> class at the end. 
<code>ALSModel</code> is a persistable class that extends 
<code>MatrixFactorizationModel</code>.</p> <blockquote> <p>The detailed 
implementation can be found at 
MyRecommendation/src/main/scala/<strong><em>ALSModel.scala</em></strong></p></blockquote>
 <p>PredictionIO will automatically store th
 e returned model, i.e. <code>ALSModel</code> in this case.</p><h3 
id='predict(...)' 
class='header-anchors'>predict(...)</h3><p><code>predict</code> is called when 
you send a JSON query to <a 
href="http://localhost:8000/queries.json";>http://localhost:8000/queries.json</a>.
 PredictionIO converts the query, such as <code>{ &quot;user&quot;: 
&quot;1&quot;, &quot;num&quot;: 4 }</code> to the <code>Query</code> class you 
defined previously.</p><p>The predictive model 
<code>MatrixFactorizationModel</code> of MLlib ALS, which is now extended as 
<code>ALSModel</code>, offers a method called <code>recommendProducts</code>. 
<code>recommendProducts</code> takes two parameters: user id (i.e. the 
<code>Int</code> index of <code>query.user</code>) and the number of items to 
be returned (i.e. <code>query.num</code>). It predicts the top <em>num</em> of 
items a user will like.</p><div class="highlight scala"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right">
 <pre class="lineno">1
+</pre></td></tr></tbody></table> </div> <p>The <code>seed</code> parameter is 
an optional parameter, which is used by MLlib ALS algorithm internally to 
generate random values. If the <code>seed</code> is not specified, current 
system time would be used and hence each train may produce different results. 
Specify a fixed value for the <code>seed</code> if you want to have 
deterministic result (For example, when you are 
testing).</p><p><code>ALS.train</code> then returns a 
<code>MatrixFactorizationModel</code> model which contains RDD data. RDD is a 
distributed collection of items which <em>does not</em> persist. To store the 
model, you convert the model to <code>ALSModel</code> class at the end. 
<code>ALSModel</code> is a persistable class that extends 
<code>MatrixFactorizationModel</code>.</p> <blockquote> <p>The detailed 
implementation can be found at 
MyRecommendation/src/main/scala/<strong><em>ALSModel.scala</em></strong></p></blockquote>
 <p>PredictionIO will automatically store th
 e returned model, i.e. <code>ALSModel</code> in this case.</p><h3 
id='predict(...)' 
class='header-anchors'>predict(...)</h3><p><code>predict</code> is called when 
you send a JSON query to <a 
href="http://localhost:8000/queries.json";>http://localhost:8000/queries.json</a>.
 PredictionIO converts the query, such as <code>{ &quot;user&quot;: 
&quot;1&quot;, &quot;num&quot;: 4 }</code> to the <code>Query</code> class you 
defined previously.</p><p>The predictive model 
<code>MatrixFactorizationModel</code> of MLlib ALS, which is now extended as 
<code>ALSModel</code>, offers a method called <code>recommendProducts</code>. 
<code>recommendProducts</code> takes two parameters: user id (i.e. the 
<code>Int</code> index of <code>query.user</code>) and the number of items to 
be returned (i.e. <code>query.num</code>). It predicts the top <em>num</em> of 
items a user will like.</p><div class="highlight scala"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right">
 <pre class="lineno">1
 2
 3
 4

http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/9fe018b6/templates/recommendation/evaluation/index.html
----------------------------------------------------------------------
diff --git a/templates/recommendation/evaluation/index.html 
b/templates/recommendation/evaluation/index.html
index 336ef91..7298749 100644
--- a/templates/recommendation/evaluation/index.html
+++ b/templates/recommendation/evaluation/index.html
@@ -111,7 +111,7 @@ Metrics:
   PositiveCount <span class="o">(</span><span class="nv">threshold</span><span 
class="o">=</span>1.0<span class="o">)</span>: 10.006666666666666
 <span class="o">[</span>INFO 2015-03-31 00:36:01,516] <span 
class="o">[</span>CoreWorkflow<span class="nv">$]</span> runEvaluation completed
 
-</pre></td></tr></tbody></table> </div> <p>The console prints out the 
evaluation meric score of each engine params, and finally pretty print the 
optimal engine params. Amongs the 3 engine params we evaluate, the best 
Prediction@k has a score of ~0.1521.</p><h2 id='the-evaluation-design' 
class='header-anchors'>The Evaluation Design</h2><p>We assume you have read the 
<a href="/evaluation">Tuning and Evaluation</a> section. We will cover the 
evaluation aspects which are specific to the recommendation engine.</p><p>In 
recommendation evaluation, the raw data is a sequence of known ratings. A 
rating has 3 components: user, item, and a score. We use the $k-fold$ method 
for evaluation, the raw data is sliced into a sequence of (training, 
validation) data tuple.</p><p>In the validation data, we construct a query for 
<em>each user</em>, and get a list of recommended items from the engine. It is 
vastly different from the classification tutorial, where there is a one-to-one 
corresponding betwee
 n the training data point and the validation data point. In this evaluation, 
our unit of evaluation is <em>user</em>, we evaluate the quality of an engine 
using the known rating of a user.</p><h3 id='key-assumptions' 
class='header-anchors'>Key assumptions</h3><p>There are multiple assumptions we 
have to make when we evaluate a recommendation engine:</p> <ul> 
<li><p>Definition of &#39;good&#39;. We want to quantify if the engine is able 
to recommend items which the user likes, we need to define what is meant by 
&#39;good&#39;. In this examle, we have two kinds of events: &#39;rate&#39; and 
&#39;buy&#39;. The &#39;rate&#39; event is associated with a rating value which 
ranges between 1 to 4, and the &#39;buy&#39; event is mapped to a rating of 4. 
When we implement the metric, we have to specify a rating threshold, only the 
rating above the threshold is considered &#39;good&#39;.</p></li> <li><p>The 
absence of complete rating. It is extremely unlikely that the training data 
contains ra
 ting for all user-item tuples. In contrast, of a system containing 1000 items, 
a user may only have rated 20 of them, leaving 980 items unrated. There is no 
way for us to certainly tell if the user likes an unrated product. When we 
examine the evaluation result, it is important for us to keep in mind that the 
final metric is only an approximation of the actual result.</p></li> 
<li><p>Recommendation affects user behavior. Suppose you are a e-commerce 
company and would like to use the recommendation engine to personalize the 
landing page, the item you show in the langing page directly impacts what the 
user is going to purchase. This is different from weather prediction, whatever 
the weather forecast engine predicts, tomorrow&#39;s weather won&#39;t be 
affected. Therefore, when we conduct offline evaluation for recommendation 
engines, it is possible that the final user behavior is dramatically different 
from the evaluation result. However, in the evaluation, for simplicity, we have 
to 
 assume that user behavior is homogenous.</p></li> </ul> <h2 
id='evaluation-data-generation' class='header-anchors'>Evaluation Data 
Generation</h2><h3 id='actual-result' class='header-anchors'>Actual 
Result</h3><p>In 
MyRecommendation/src/main/scala/<strong><em>Engine.scala</em></strong>, we 
define the <code>ActualResult</code> which represents the user rating for 
validation. It stores the list of ratings in the validation set for a 
user.</p><div class="highlight scala"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1
+</pre></td></tr></tbody></table> </div> <p>The console prints out the 
evaluation metric score of each engine params, and finally pretty print the 
optimal engine params. Amongst the 3 engine params we evaluate, the best 
Prediction@k has a score of ~0.1521.</p><h2 id='the-evaluation-design' 
class='header-anchors'>The Evaluation Design</h2><p>We assume you have read the 
<a href="/evaluation">Tuning and Evaluation</a> section. We will cover the 
evaluation aspects which are specific to the recommendation engine.</p><p>In 
recommendation evaluation, the raw data is a sequence of known ratings. A 
rating has 3 components: user, item, and a score. We use the $k-fold$ method 
for evaluation, the raw data is sliced into a sequence of (training, 
validation) data tuple.</p><p>In the validation data, we construct a query for 
<em>each user</em>, and get a list of recommended items from the engine. It is 
vastly different from the classification tutorial, where there is a one-to-one 
corresponding betw
 een the training data point and the validation data point. In this evaluation, 
our unit of evaluation is <em>user</em>, we evaluate the quality of an engine 
using the known rating of a user.</p><h3 id='key-assumptions' 
class='header-anchors'>Key assumptions</h3><p>There are multiple assumptions we 
have to make when we evaluate a recommendation engine:</p> <ul> 
<li><p>Definition of &#39;good&#39;. We want to quantify if the engine is able 
to recommend items which the user likes, we need to define what is meant by 
&#39;good&#39;. In this example, we have two kinds of events: &#39;rate&#39; 
and &#39;buy&#39;. The &#39;rate&#39; event is associated with a rating value 
which ranges between 1 to 4, and the &#39;buy&#39; event is mapped to a rating 
of 4. When we implement the metric, we have to specify a rating threshold, only 
the rating above the threshold is considered &#39;good&#39;.</p></li> 
<li><p>The absence of complete rating. It is extremely unlikely that the 
training data contains
  rating for all user-item tuples. In contrast, of a system containing 1000 
items, a user may only have rated 20 of them, leaving 980 items unrated. There 
is no way for us to certainly tell if the user likes an unrated product. When 
we examine the evaluation result, it is important for us to keep in mind that 
the final metric is only an approximation of the actual result.</p></li> 
<li><p>Recommendation affects user behavior. Suppose you are a e-commerce 
company and would like to use the recommendation engine to personalize the 
landing page, the item you show in the landing page directly impacts what the 
user is going to purchase. This is different from weather prediction, whatever 
the weather forecast engine predicts, tomorrow&#39;s weather won&#39;t be 
affected. Therefore, when we conduct offline evaluation for recommendation 
engines, it is possible that the final user behavior is dramatically different 
from the evaluation result. However, in the evaluation, for simplicity, we have 
 to assume that user behavior is homogenous.</p></li> </ul> <h2 
id='evaluation-data-generation' class='header-anchors'>Evaluation Data 
Generation</h2><h3 id='actual-result' class='header-anchors'>Actual 
Result</h3><p>In 
MyRecommendation/src/main/scala/<strong><em>Engine.scala</em></strong>, we 
define the <code>ActualResult</code> which represents the user rating for 
validation. It stores the list of ratings in the validation set for a 
user.</p><div class="highlight scala"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1
 2
 3</pre></td><td class="code"><pre><span class="k">case</span> <span 
class="k">class</span> <span class="nc">ActualResult</span><span 
class="o">(</span>
   <span class="n">ratings</span><span class="k">:</span> <span 
class="kt">Array</span><span class="o">[</span><span 
class="kt">Rating</span><span class="o">]</span>

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