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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-o
f-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/incubator-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>Comics 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 sma
ll set of latent factors that 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/dat
a/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
+<!DOCTYPE html><html><head><title>Comics Recommendation Demo</title><meta
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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/incubator-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>Comics 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 smal
l set of latent factors that 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
@@ -261,7 +261,7 @@ queryPIO: <span class="k">function</span><span
class="o">()</span> <span class="
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 (incubating)
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://predi
ctionio.apache.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 documenation) live in the main repo <a
href="https://github.com/apache/incubator-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
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+</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 (incubating)
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://predi
ctionio.apache.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
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