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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="#">Demo Tutorials</a><span class="spacer">></span></li><li><span
class="last">Comics Recommendation Demo</span></li></ul></div><div
id="page-title"><h1>Comics Recommendation Demo</h1></div></div><div
id="table-of-content-wrapper
"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a
href="#introduction">Introduction</a> </li> <li> <a
href="#tapster-demo-application">Tapster Demo Application</a> </li> <li> <a
href="#apache-predictionio-setup">Apache PredictionIO Setup</a> </li> <li> <a
href="#import-data">Import Data</a> </li> <li> <a
href="#connect-demo-app-with-apache-predictionio">Connect Demo app with Apache
PredictionIO</a> </li> <li> <a href="#links">Links</a> </li> <li> <a
href="#conclusion">Conclusion</a> </li> </ul> </aside><hr/><a
id="edit-page-link"
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/demo/tapster.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="#">Demo Tutorials</a><span
class="spacer">></span></li><li><span class="last">Comics Recommendation
Demo</span></li></ul></div><div id="page-title"><h1>C
omics Recommendation Demo</h1></div></div><div class="content"> <h2
id='introduction' class='header-anchors'>Introduction</h2><p>In this demo, we
will show you how to build a Tinder-style web application (named
"Tapster") recommending comics to users based on their likes/dislikes
of episodes interactively.</p><p>The demo will use <a
href="https://predictionio.apache.org/templates/similarproduct/quickstart/";>Similar
Product Template</a>. Similar Product Template is a great choice if you want
to make recommendations based on immediate user activities or for new users
with limited history. It uses MLLib Alternating Least Squares (ALS)
recommendation algorithm, a <a
href="http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering";>Collaborative
filtering</a> (CF) algorithm commonly used for recommender systems. These
techniques aim to fill in the missing entries of a user-item association
matrix. Users and products are described by a small set of latent factors th
at can be used to predict missing entries. A layman's interpretation of
Collaborative Filtering is "People who like this comic, also like these
comics."</p><p>All the code and data is on GitHub at: <a
href="https://github.com/PredictionIO/Demo-Tapster";>github.com/PredictionIO/Demo-Tapster</a>.</p><h3
id='data' class='header-anchors'>Data</h3><p>The source of the data is from <a
href="http://tapastic.com/";>Tapastic</a>. You can find the data files <a
href="https://github.com/PredictionIO/Demo-Tapster/tree/master/data";>here</a>.</p><p>The
data structure looks like this:</p><p><a
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/episode_list.csv";>Episode
List</a> <code>data/episode_list.csv</code></p><p><strong>Fields:</strong>
episodeId | episodeTitle | episodeCategories | episodeUrl |
episodeImageUrls</p><p>1,000 rows. Each row represents one episode.</p><p><a
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/data/user_list.csv";>User
Like
Event List</a> <code>data/user_list.csv</code></p><p><strong>Fields:</strong>
userId | episodeId | likedTimestamp</p><p>192,587 rows. Each row represents one
user like for the given episode.</p><p>The tutorial has four major steps:</p>
<ul> <li>Demo application setup</li> <li>PredictionIO installation and
setup</li> <li>Import data into database and PredictionIO</li> <li>Integrate
demo application with PredictionIO</li> </ul> <h2 id='tapster-demo-application'
class='header-anchors'>Tapster Demo Application</h2><p>The demo application is
built using Rails.</p><p>You can clone the existing application with:</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
-3</pre></td><td class="code"><pre><span class="gp">$ </span>git clone
https://github.com/PredictionIO/Demo-Tapster.git
-<span class="gp">$ </span><span class="nb">cd </span>Demo-Tapster
-<span class="gp">$ </span>bundle install
-</pre></td></tr></tbody></table> </div> <p>You will need to edit
<code>config/database.yml</code> to match your local database settings. We have
provided some sensible defaults for PostgreSQL, MySQL, and SQLite.</p><p>Setup
the database with:</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="gp">$ </span>rake db:create
-<span class="gp">$ </span>rake db:migrate
-</pre></td></tr></tbody></table> </div> <p>At this point, you should have the
demo application ready but with an empty database. Lets import the episodes
data into our database. We will do this with: <code>$ rake
import:episodes</code>. An "Episode" is a single <a
href="http://en.wikipedia.org/wiki/Comic_strip";>comic strip</a>.</p><p><a
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/lib/tasks/import/episodes.rake";>View
on GitHub</a></p><p>This script is pretty simple. It loops through the CSV
file and creates a new episode for each line in the file in our local
database.</p><p>You can start the app and point your browser to <a
href="http://localhost:3000";>http://localhost:3000</a></p><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><td
class="code"><pre><span class="nv">$rails</span> server
-</pre></td></tr></tbody></table> </div> <p><img alt="Rails Server"
src="/images/demo/tapster/rails-server-997d690e.png"/></p><h2
id='apache-predictionio-setup' class='header-anchors'>Apache PredictionIO
Setup</h2><h3 id='install-apache-predictionio' class='header-anchors'>Install
Apache PredictionIO</h3><p>Follow the installation instructions <a
href="http://predictionio.apache.org/install/";>here</a> or simply run:</p><div
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td
class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td
class="code"><pre><span class="gp">$ </span>bash -c <span
class="s2">"</span><span class="k">$(</span>curl -s
https://raw.githubusercontent.com/apache/predictionio/master/bin/install.sh<span
class="k">)</span><span class="s2">"</span>
-</pre></td></tr></tbody></table> </div> <p><img alt="PIO Install"
src="/images/demo/tapster/pio-install-2d870aed.png"/></p><h3
id='create-a-new-app' class='header-anchors'>Create a New App</h3><p>You will
need to create a new app on Apache PredictionIO to house the Tapster demo. You
can do this with:</p><div class="highlight shell"><table style="border-spacing:
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre
class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>pio
app new tapster
-</pre></td></tr></tbody></table> </div> <p>Take note of the App ID and Access
Key.</p><p><img alt="PIO App New"
src="/images/demo/tapster/pio-app-new-5a8ae503.png"/></p><h3 id='setup-engine'
class='header-anchors'>Setup Engine</h3><p>We are going to copy the Similar
Product Template into the PIO directory.</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="gp">$ </span><span
class="nb">cd </span>PredictionIO
-<span class="gp">$ </span>git clone
https://github.com/apache/predictionio-template-similar-product.git
tapster-episode-similar
-</pre></td></tr></tbody></table> </div> <p>Next we are going to update the App
ID in the âengine.jsonâ file to match the App ID we just created.</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
-3</pre></td><td class="code"><pre><span class="gp">$ </span><span
class="nb">cd </span>tapster-episode-similar
-<span class="gp">$ </span>nano engine.json
-<span class="gp">$ </span><span class="nb">cd</span> ..
-</pre></td></tr></tbody></table> </div> <p><img alt="Engine Setup"
src="/images/demo/tapster/pio-engine-setup-88e25cc0.png"/></p><h3
id='modify--engine-template' class='header-anchors'>Modify Engine
Template</h3><p>By the default, the engine template reads the âviewâ
events. We can easily to change it to read âlikeâ events.</p> <p>Modify
<code>readTraining()</code> in DataSource.scala:</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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- <span class="k">override</span>
- <span class="k">def</span> <span class="n">readTraining</span><span
class="o">(</span><span class="n">sc</span><span class="k">:</span> <span
class="kt">SparkContext</span><span class="o">)</span><span class="k">:</span>
<span class="kt">TrainingData</span> <span class="o">=</span> <span
class="o">{</span>
-
- <span class="o">...</span>
-
- <span class="k">val</span> <span class="n">viewEventsRDD</span><span
class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span
class="kt">ViewEvent</span><span class="o">]</span> <span class="k">=</span>
<span class="n">eventsDb</span><span class="o">.</span><span
class="n">find</span><span class="o">(</span>
- <span class="n">appId</span> <span class="k">=</span> <span
class="n">dsp</span><span class="o">.</span><span class="n">appId</span><span
class="o">,</span>
- <span class="n">entityType</span> <span class="k">=</span> <span
class="nc">Some</span><span class="o">(</span><span
class="s">"user"</span><span class="o">),</span>
- <span class="n">eventNames</span> <span class="k">=</span> <span
class="nc">Some</span><span class="o">(</span><span class="nc">List</span><span
class="o">(</span><span class="s">"like"</span><span class="o">)),</span> <span
class="c1">// MODIFIED
-</span> <span class="c1">// targetEntityType is optional field of an
event.
-</span> <span class="n">targetEntityType</span> <span class="k">=</span>
<span class="nc">Some</span><span class="o">(</span><span
class="nc">Some</span><span class="o">(</span><span
class="s">"item"</span><span class="o">)))(</span><span
class="n">sc</span><span class="o">)</span>
- <span class="c1">// eventsDb.find() returns RDD[Event]
-</span> <span class="o">.</span><span class="n">map</span> <span
class="o">{</span> <span class="n">event</span> <span class="k">=></span>
- <span class="k">val</span> <span class="n">viewEvent</span> <span
class="k">=</span> <span class="k">try</span> <span class="o">{</span>
- <span class="n">event</span><span class="o">.</span><span
class="n">event</span> <span class="k">match</span> <span class="o">{</span>
- <span class="k">case</span> <span class="s">"like"</span> <span
class="k">=></span> <span class="nc">ViewEvent</span><span
class="o">(</span> <span class="c1">// MODIFIED
-</span> <span class="n">user</span> <span class="k">=</span>
<span class="n">event</span><span class="o">.</span><span
class="n">entityId</span><span class="o">,</span>
- <span class="n">item</span> <span class="k">=</span> <span
class="n">event</span><span class="o">.</span><span
class="n">targetEntityId</span><span class="o">.</span><span
class="n">get</span><span class="o">,</span>
- <span class="n">t</span> <span class="k">=</span> <span
class="n">event</span><span class="o">.</span><span
class="n">eventTime</span><span class="o">.</span><span
class="n">getMillis</span><span class="o">)</span>
- <span class="k">case</span> <span class="k">_</span> <span
class="k">=></span> <span class="k">throw</span> <span class="k">new</span>
<span class="nc">Exception</span><span class="o">(</span><span
class="n">s</span><span class="s">"Unexpected event ${event} is
read."</span><span class="o">)</span>
- <span class="o">}</span>
- <span class="o">}</span> <span class="k">catch</span> <span
class="o">{</span>
- <span class="k">case</span> <span class="n">e</span><span
class="k">:</span> <span class="kt">Exception</span> <span
class="o">=></span> <span class="o">{</span>
- <span class="n">logger</span><span class="o">.</span><span
class="n">error</span><span class="o">(</span><span class="n">s</span><span
class="s">"Cannot convert ${event} to ViewEvent."</span> <span
class="o">+</span>
- <span class="n">s</span><span class="s">" Exception:
${e}."</span><span class="o">)</span>
- <span class="k">throw</span> <span class="n">e</span>
- <span class="o">}</span>
- <span class="o">}</span>
- <span class="n">viewEvent</span>
- <span class="o">}</span>
-
- <span class="o">...</span>
- <span class="o">}</span>
-<span class="o">}</span>
-
-</pre></td></tr></tbody></table> </div> <p>Finally to build the engine we will
run:</p><div class="highlight shell"><table style="border-spacing:
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre
class="lineno">1
-2
-3</pre></td><td class="code"><pre><span class="gp">$ </span><span
class="nb">cd </span>tapster-episode-similar
-<span class="gp">$ </span>pio build
-<span class="gp">$ </span><span class="nb">cd</span> ..
-</pre></td></tr></tbody></table> </div> <p><img alt="PIO Build"
src="/images/demo/tapster/pio-build-e6eb1d7c.png"/></p><h2 id='import-data'
class='header-anchors'>Import Data</h2><p>Once everything is installed, start
the event server by running: <code>$ pio eventserver</code></p><p><img
alt="Event Server"
src="/images/demo/tapster/pio-eventserver-88889ec0.png"/></p><div
class="alert-message info"><p>You can check the status of Apache PredictionIO
at any time by running: <code>$ pio status</code></p></div><p>ALERT: If your
laptop goes to sleep you might manually need to restart HBase with:</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
-3</pre></td><td class="code"><pre><span class="gp">$ </span><span
class="nb">cd </span>PredictionIO/venders/hbase-0.98.6/bin
-<span class="gp">$ </span>./stop-hbase.sh
-<span class="gp">$ </span>./start-hbase.sh
-</pre></td></tr></tbody></table> </div> <p>The key event we are importing into
Apache PredictionIO event server is the "Like" event (for example,
user X likes episode Y).</p><p>We will send this data to Apache PredictionIO by
executing <code>$ rake import:predictionio</code> command.</p><p><a
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/lib/tasks/import/predictionio.rake";>View
on GitHub</a></p><p>This script is a little more complex. First we need to
connect to the Event Server.</p><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><td class="code"><pre>client <span
class="o">=</span> PredictionIO::EventClient.new<span
class="o">(</span>ENV[<span class="s1">'PIO_ACCESS_KEY'</span><span
class="o">]</span>, ENV[<span class="s1">'PIO_EVENT_SERVER_URL'</span><span
class="o">]</span>, THREADS<span class="o">)</span>
-</pre></td></tr></tbody></table> </div> <p>You will need to create the
environmental variables <code>PIO_ACCESS_KEY</code> and
<code>PIO_EVENT_SERVER_URL</code>. The default Event Server URL is: <a
href="http://localhost:7070";>http://localhost:7070</a>.</p><div
class="alert-message info"><p>If you forget your <strong>Access Key</strong>
you can always run: <code>$ pio app list</code></p></div><p>You can set these
values in the <code>.env</code> file located in the application root directory
and it will be automatically loaded into your environment each time Rails is
run.</p><p>The next part of the script loops through each line of the
<code>data/user_list.csv</code> file and returns an array of unique user and
episode IDs. Once we have those we can send the data to Apache PredictionIO
like this.</p><p>First the users:</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
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-5</pre></td><td class="code"><pre>user_ids.each_with_index <span
class="k">do</span> |id, i|
- <span class="c"># Send unique user IDs to PredictionIO.</span>
- client.aset_user<span class="o">(</span>id<span class="o">)</span>
- puts <span class="s2">"Sent user ID #{id} to PredictionIO. Action #{i + 1}
of #{user_count}"</span>
-end
-</pre></td></tr></tbody></table> </div> <p>And now the episodes:</p><div
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td
class="gutter gl" style="text-align: right"><pre class="lineno">1
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class="k">do</span> |id, i|
- <span class="c"># Load episode from database - we will need this to include
the categories!</span>
- episode <span class="o">=</span> Episode.where<span
class="o">(</span>episode_id: id<span class="o">)</span>.take
-
- <span class="k">if </span>episode
- <span class="c"># Send unique episode IDs to PredictionIO.</span>
- client.acreate_event<span class="o">(</span>
- <span class="s1">'$set'</span>,
- <span class="s1">'item'</span>,
- id,
- properties: <span class="o">{</span> categories: episode.categories
<span class="o">}</span>
- <span class="o">)</span>
- puts <span class="s2">"Sent episode ID #{id} to PredictionIO. Action #{i +
1} of #{episode_count}"</span>
- <span class="k">else
- </span>puts <span class="s2">"Episode ID #{id} not found in database!
Skipping!"</span>.color<span class="o">(</span>:red<span class="o">)</span>
- end
-end
-</pre></td></tr></tbody></table> </div> <p>Finally we loop through the
<code>data/user_list.csv</code> file a final time to send the like
events:</p><div class="highlight shell"><table style="border-spacing:
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre
class="lineno">1
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-14</pre></td><td class="code"><pre>CSV.foreach<span
class="o">(</span>USER_LIST, headers: <span class="nb">true</span><span
class="o">)</span> <span class="k">do</span> |row|
- user_id <span class="o">=</span> row[0] <span class="c"># userId</span>
- episode_id <span class="o">=</span> row[1] <span class="c"># episodeId</span>
-
- <span class="c"># Send like to PredictionIO.</span>
- client.acreate_event<span class="o">(</span>
- <span class="s1">'like'</span>,
- <span class="s1">'user'</span>,
- user_id,
- <span class="o">{</span> <span class="s1">'targetEntityType'</span> <span
class="o">=</span>> <span class="s1">'item'</span>, <span
class="s1">'targetEntityId'</span> <span class="o">=</span>> episode_id
<span class="o">}</span>
- <span class="o">)</span>
-
- puts <span class="s2">"Sent user ID #{user_id} liked episode ID
#{episode_id} to PredictionIO. Action #{</span><span
class="nv">$INPUT_LINE_NUMBER</span><span class="s2">} of #{line_count}."</span>
-end
-</pre></td></tr></tbody></table> </div> <p>In total the script takes about 4
minutes to run on a basic laptop. At this point all the data is now imported to
Apache PredictionIO.</p><p><img alt="Import"
src="/images/demo/tapster/pio-import-predictionio-1ecd11fd.png"/></p><h3
id='engine-training' class='header-anchors'>Engine Training</h3><p>We train the
engine with the following command:</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="gp">$ </span><span
class="nb">cd </span>tapster-episode-similar
-<span class="gp">$ </span>pio train -- --driver-memory 4g
-</pre></td></tr></tbody></table> </div> <p><img alt="PIO Train"
src="/images/demo/tapster/pio-train-7edffad4.png"/></p><p>Using the
--driver-memory option to limit the memory used by Apache PredictionIO. Without
this Apache PredictionIO can consume too much memory leading to a crash. You
can adjust the 4g up or down depending on your system specs.</p><p>You can set
up a job to periodically retrain the engine so the model is updated with the
latest dataset.</p><h3 id='deploy-model' class='header-anchors'>Deploy
Model</h3><p>You can deploy the model with: <code>$ pio deploy</code> from the
<code>tapster-episode-similar</code> directory.</p><p>At this point, you have
an demo app with data and a Apache PredictionIO server with a trained model all
setup. Next, we will connect the two so you can log the live interaction
(likes) events into Apache PredictionIO event server and query the engine
server for recommendation.</p><h2
id='connect-demo-app-with-apache-predictionio' class='header-an
chors'>Connect Demo app with Apache PredictionIO</h2><h3 id='overview'
class='header-anchors'>Overview</h3><p>On a high level the application keeps a
record of each like and dislike. It uses jQuery to send an array of both likes
and dislikes to the server on each click. The server then queries Apache
PredictionIO for a similar episode which is relayed to jQuery and displayed to
the user.</p><p>Data flow:</p> <ul> <li>The user likes an episode.</li>
<li>Tapster sends the "Like" event to Apache PredictionIO event
server.</li> <li>Tapster queries Apache PredictionIO engine with all the
episodes the user has rated (likes and dislikes) in this session.</li>
<li>Apache PredictionIO returns 1 recommended episode.</li> </ul> <h3
id='javascript' class='header-anchors'>JavaScript</h3><p>All the important code
lives in <code>app/assets/javascripts/application.js</code> <a
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/app/assets/javascripts/application.js";>View
on Git
Hub</a></p><p>Most of this file is just handlers for click things, displaying
the loading dialog and other such things.</p><p>The most important function is
to query the Rails server for results from Apache PredictionIO.</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
-3
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-14</pre></td><td class="code"><pre>// Query the server <span class="k">for
</span>a comic based on previous likes. See episodes#query.
-queryPIO: <span class="k">function</span><span class="o">()</span> <span
class="o">{</span>
- var _this <span class="o">=</span> this; // For closure.
- <span class="nv">$.</span>ajax<span class="o">({</span>
- url: <span class="s1">'/episodes/query'</span>,
- <span class="nb">type</span>: <span class="s1">'POST'</span>,
- data: <span class="o">{</span>
- likes: JSON.stringify<span class="o">(</span>_this.likes<span
class="o">)</span>,
- dislikes: JSON.stringify<span class="o">(</span>_this.dislikes<span
class="o">)</span>,
- <span class="o">}</span>
- <span class="o">})</span>.done<span class="o">(</span><span
class="k">function</span><span class="o">(</span>data<span class="o">)</span>
<span class="o">{</span>
- _this.setComic<span class="o">(</span>data<span class="o">)</span>;
- <span class="o">})</span>;
-<span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <h3 id='rails'
class='header-anchors'>Rails</h3><p>On the Rails side all the fun things happen
in the episodes controller located at:
<code>app/controllers/episodes_controller</code> <a
href="https://github.com/PredictionIO/Demo-Tapster/blob/master/app/controllers/episodes_controller.rb";>View
on GitHub</a>.</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
-3
-4
-5
-6
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-9
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- <span class="c"># Create PredictionIO client.</span>
- client <span class="o">=</span> PredictionIO::EngineClient.new<span
class="o">(</span>ENV[<span class="s1">'PIO_ENGINE_URL'</span><span
class="o">])</span>
-
- <span class="c"># Get posted likes and dislikes.</span>
- likes <span class="o">=</span> ActiveSupport::JSON.decode<span
class="o">(</span>params[:likes]<span class="o">)</span>
- dislikes <span class="o">=</span> ActiveSupport::JSON.decode<span
class="o">(</span>params[:dislikes]<span class="o">)</span>
-
- <span class="k">if </span>likes.empty?
- <span class="c"># We can't query PredictionIO with no likes so</span>
- <span class="c"># we will return a random comic instead.</span>
- @episode <span class="o">=</span> random_episode
-
- render json: @episode
- <span class="k">return
- </span>end
-
- <span class="c"># Query PredictionIO.</span>
- <span class="c"># Here we black list the disliked items so they are not
shown again!</span>
- response <span class="o">=</span> client.send_query<span
class="o">(</span>items: likes, blackList: dislikes, num: 1<span
class="o">)</span>
-
- <span class="c"># With a real application you would want to do some</span>
- <span class="c"># better sanity checking of the response here!</span>
-
- <span class="c"># Get ID of response.</span>
- id <span class="o">=</span> response[<span
class="s1">'itemScores'</span><span class="o">][</span>0][<span
class="s1">'item'</span><span class="o">]</span>
-
- <span class="c"># Find episode in database.</span>
- @episode <span class="o">=</span> Episode.where<span
class="o">(</span>episode_id: id<span class="o">)</span>.take
-
- render json: @episode
-end
-</pre></td></tr></tbody></table> </div> <p>On the first line we make a
connection to Apache PredictionIO. You will need to set the
<code>PIO_ENGINE_URL</code>. This can be done in the <code>.env</code> file.
The default URL is: <a
href="http://localhost:8000";>http://localhost:8000</a>.</p><p>Next we decode
the JSON sent from the browser.</p><p>After that we check to see if the user
has liked anything yet. If not we just return a random episode.</p><p>If the
user has likes then we can send that data to Apache PredictionIO event
server.</p><p>We also blacklist the dislikes so that they are not
returned.</p><p>With our response from Apache PredictionIO itâs just a matter
of looking it up in the database and rendering that object as JSON.</p><p>Once
the response is sent to the browser JavaScript is used to replace the existing
comic and hide the loading message.</p><p>Thats it. Youâre done! If Ruby is
not your language of choice check out our other <a
href="http://predictionio.apach
e.org/sdk/">SDKs</a> and remember you can always interact with the Event
Server though itâs native JSON API.</p><h2 id='links'
class='header-anchors'>Links</h2><p>Source code is on GitHub at: <a
href="https://github.com/PredictionIO/Demo-Tapster";>github.com/PredictionIO/Demo-Tapster</a></p><h2
id='conclusion' class='header-anchors'>Conclusion</h2><p>Love this tutorial
and Apache PredictionIO? Both are open source (Apache 2 License). <a
href="https://github.com/PredictionIO/Demo-Tapster";>Fork</a> this demo and
build upon it. If you produce something cool shoot us an email and we will link
to it from here.</p><p>Found a typo? Think something should be explained
better? This tutorial (and all our other documentation) live in the main repo
<a
href="https://github.com/apache/predictionio/blob/livedoc/docs/manual/source/demo/tapster.html.md";>here</a>.
Our documentation is in the <code>livedoc</code> branch. Find out how to
contribute documentation at <a href="http://predictionio.apache.
org/community/contribute-documentation/">http://predictionio.apache.org/community/contribute-documentation/</a>].</p><p>We
♥ pull requests!</p></div></div></div></div><footer><div
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