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-<!DOCTYPE html><html><head><title>Engine Template Gallery</title><meta 
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 <nav id="nav-main"><ul><li class="level-1"><a class="expandible" 
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Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" href="/templates/recomme
 ndation/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" 
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Serving</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" 
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 tor</span></a></li></ul></li><li class="level-2"><a class="expandible" 
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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
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with Rate Event</span></a></li><li class="level-3"><a class="final" 
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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="/templates/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" 
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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 
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="fina
 l" href="/templates/classification/quickstart/"><span>Quick 
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 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
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href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
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 nity Projects</span></a></li></ul></li><li class="level-1"><a 
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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Engine Template Gallery</a><span 
class="spacer">&gt;</span></li><li><span 
class="last">Browse</span></li></ul></div><div id="page-title"><h1>Engine 
Template Gallery</h1></div></div><div id="table-of-content-wrapper"><a 
id="edit-page-link" hre
 
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Gallery</a><span class="spacer">&gt;</span></li><li><span 
class="last">Browse</span></li></ul></div><div id="page-title"><h1>Engine 
Template Gallery</h1></div></div><div class="content"><p>Pick a tab for the 
type of template you are looking for. Some still need to be ported (a simple 
process) to Apache PIO and these are marked. Also see each Template description 
for special support instructions.</p><div class="tabs"> <ul class="control"> 
<li data-lang=""><a 
href="#tab-02602ae0-08cd-4bf7-89a1-59f61b779e7a">Recommenders</a></li> <li 
data-lang=""><a 
href="#tab-ba8ca647-cbcc-4fb6-9332-44dc36a5f4f0">Classification</a></li> <li 
data-lang=""><a href="#tab-37cb3eaa-af9b-4c1f-9fdc
 -bc0398ba3dbd">Regression</a></li> <li data-lang=""><a 
href="#tab-b3b7766a-9dd2-4c76-9b11-af2624191a70">NLP</a></li> <li 
data-lang=""><a 
href="#tab-755e8c7a-8357-43da-b91d-3caf835d5802">Clustering</a></li> <li 
data-lang=""><a 
href="#tab-1b3da76d-ba97-4e7d-8a3f-d22262804f63">Similarity</a></li> <li 
data-lang=""><a href="#tab-8bd8fff5-dc00-4a46-b874-1e2861ed34ee">Other</a></li> 
</ul> <div data-tab="Recommenders" 
id="tab-02602ae0-08cd-4bf7-89a1-59f61b779e7a"> <h3><a 
href="https://github.com/actionml/universal-recommender";>The Universal 
Recommender</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=actionml&amp;repo=universal-recommender&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Use for: </p> <ul class="tab-list"> <li 
class="tab-list-element">Personalized recommendations—user-based</li> <li 
class="tab-list-element">Similar items—item-based</li> <li 
class="tab-list-element">Viewed this bought thatâ€
 ”item-based cross-action</li> <li class="tab-list-element">Popular Items and 
User-defined ranking</li> <li class="tab-list-element">Item-set recommendations 
for complimentarty purchases or shopping carts—item-set-based</li> <li 
class="tab-list-element">Hybrid collaborative filtering and content based 
recommendations—limited content-based</li> <li 
class-tab-list-element>Business rules</li> </ul> <p>The name "Universal" refers 
to the use of this template in virtually any case that calls for 
recommendations - ecommerce, news, videos, virtually anywhere user behavioral 
data is known. This recommender uses the new <a 
href="http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html";>Cross-Occurrence
 (CCO) algorithm</a> to auto-correlate different user actions (clickstream 
data), profile data, contextual information (location, device), and some 
content types to make better recommendations. It also implements flexible 
filters and boosts for implementing business rules.</p> 
 <p>Support: <a 
href="https://groups.google.com/forum/#!forum/actionml-user";>The Universal 
Recommender user group</a></p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/apache/predictionio-template-recommender";>Recommendation</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=predictionio-template-recommender&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> An engine template is an almost-complete 
implementation of an engine. PredictionIO's Recommendation Engine Template has 
integrated Apache Spark MLlib's Collaborative Filtering algorithm by default. 
You can customize it easily to fit your specific n
 eeds. </p> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.11.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/apache/predictionio-template-ecom-recommender";>E-Commerce
 Recommendation</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=predictionio-template-ecom-recommender&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template provides personalized 
recommendation for e-commerce applications with the following features by 
default: </p> <ul class="tab-list"> <li class="tab-list-element">Exclude 
out-of-stock items</li> <li class="tab-list
 -element">Provide recommendation to new users who sign up after the model is 
trained</li> <li class="tab-list-element">Recommend unseen items only 
(configurable)</li> <li class="tab-list-element">Recommend popular items if no 
information about the user is available (added in template version v0.4.0)</li> 
</ul> <p>Support: <a href="http://predictionio.apache.org/support/";>Apache 
PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> 
<td>0.11.0-incubating</td> <td>already compatible</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/apache/predictionio-template-similar-product";>Similar 
Product</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=predictionio-template-similar-product&amp;type=star&amp;count=true";
 frameborder="0" align="middle" s
 crolling="0" width="170px" height="20px"></iframe> <p> This engine template 
recommends products that are "similar" to the input product(s). Similarity is 
not defined by user or item attributes but by users' previous actions. By 
default, it uses 'view' action such that product A and B are considered similar 
if most users who view A also view B. The template can be customized to support 
other action types such as buy, rate, like..etc </p> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.11.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/apache/predictionio-template-java-ecom-recommender";>E-Commerce
 Recommendation (Java)</a></h3> <iframe src="htt
 
ps://ghbtns.com/github-btn.html?user=apache&amp;repo=predictionio-template-java-ecom-recommender&amp;type=star&amp;count=true"
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template provides personalized 
recommendation for e-commerce applications with the following features by 
default: </p> <ul class="tab-list"> <li class="tab-list-element">Exclude 
out-of-stock items</li> <li class="tab-list-element">Provide recommendation to 
new users who sign up after the model is trained</li> <li 
class="tab-list-element">Recommend unseen items only (configurable)</li> <li 
class="tab-list-element">Recommend popular items if no information about the 
user is available</li> </ul> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Paralle
 l</td> <td>Java</td> <td>Apache Licence 2.0</td> <td>alpha</td> 
<td>0.11.0-incubating</td> <td>requires conversion</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/PredictionIO/template-scala-parallel-productranking";>Product
 Ranking</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=PredictionIO&amp;repo=template-scala-parallel-productranking&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template sorts a list of products for a 
user based on his/her preference. This is ideal for personalizing the display 
order of product page, catalog, or menu items if you have large number of 
options. It creates engagement and early conversion by placing products that a 
user prefers on the top. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala<
 /td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires 
conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/PredictionIO/template-scala-parallel-complementarypurchase";>Complementary
 Purchase</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=PredictionIO&amp;repo=template-scala-parallel-complementarypurchase&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template recommends the complementary 
items which most user frequently buy at the same time with one or more items in 
the query. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> 
<td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/vaibhavist/template-scala-par
 allel-recommendation">Music Recommendations</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=vaibhavist&amp;repo=template-scala-parallel-recommendation&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is very similar to music recommendations 
template. It is integrated with all the events a music application can have 
such as song played, liked, downloaded, purchased, etc. </p> <p>Support: <a 
href="https://github.com/vaibhavist/template-scala-parallel-recommendation/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/vngrs/template-scala-parallel-viewedthenbought";>Viewed 
This Bought That</a></h3> <
 iframe 
src="https://ghbtns.com/github-btn.html?user=vngrs&amp;repo=template-scala-parallel-viewedthenbought&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This Engine uses co-occurrence algorithm to match 
viewed items to bought items. Using this engine you may predict which item the 
user will buy, given the item(s) browsed. </p> <p>Support: <a 
href="https://github.com/vngrs/template-scala-parallel-viewedthenbought/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/goliasz/pio-template-fpm";>Frequent Pattern 
Mining</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-fpm&am
 p;type=star&amp;count=true" frameborder="0" align="middle" scrolling="0" 
width="170px" height="20px"></iframe> <p> Template uses FP Growth algorithm 
allowing to mine for frequent patterns. Template returns subsequent items 
together with confidence score. Sometimes used as a shopping cart recommender 
but has other uses. </p> <p>Support: <a 
href="https://github.com/goliasz/pio-template-fpm/issues";>Github issues</a></p> 
<br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating";>Similar
 Product with Rating</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=ramaboo&amp;repo=template-scala-parallel-similarproduct-with-rating&amp;type=star&amp;count=true
 " frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Similar product template with rating support! Used 
for the MovieLens Demo. </p> <p>Support: <a 
href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>beta</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/goliasz/pio-template-fpm";>Frequent Pattern 
Mining</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-fpm&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Template uses FP Growth algorithm allowing to mine 
for frequent patterns. Template returns subsequent items
  together with confidence score. </p> <p>Support: <a 
href="https://github.com/goliasz/pio-template-fpm/issues";>Github issues</a></p> 
<br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> 
</div> <div data-tab="Classification" 
id="tab-ba8ca647-cbcc-4fb6-9332-44dc36a5f4f0"> <h3><a 
href="https://github.com/apache/predictionio-template-attribute-based-classifier";>Classification</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=predictionio-template-attribute-based-classifier&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> An engine template is an almost-complete 
implementation of an engine. PredictionIO's Classification Engine Template has 
integrated Ap
 ache Spark MLlib's Naive Bayes algorithm by default. </p> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.11.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/haricharan123/PredictionIo-lingpipe-MultiLabelClassification";>Classification</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=haricharan123&amp;repo=PredictionIo-lingpipe-MultiLabelClassification&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template is an almost-complete 
implementation of an engine meant to used with PredictionIO. This Multi-label 
Classification Engine Template has integrated 
 LingPipe (http://alias-i.com/lingpipe/) algorithm by default. </p> <p>Support: 
</p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Java</td> <td>Apache Licence 2.0</td> 
<td>stable</td> <td>0.9.5</td> <td>already compatible</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/PredictionIO/template-scala-parallel-leadscoring";>Lead 
Scoring</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=PredictionIO&amp;repo=template-scala-parallel-leadscoring&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template predicts the probability of an 
user will convert (conversion event by user) in the current session. </p> 
<p>Support: </p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required
 </th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/apache/predictionio-template-text-classifier";>Text 
Classification</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=predictionio-template-text-classifier&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Use this engine for general text classification 
purposes. Uses OpenNLP library for text vectorization, includes 
t.f.-i.d.f.-based feature transformation and reduction, and uses Spark MLLib's 
Multinomial Naive Bayes implementation for classification. </p> <p>Support: <a 
href="https://github.com/apache/predictionio-template-text-classifier/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Requir
 ed</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 
2.0</td> <td>alpha</td> <td>0.11.0-incubating</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/andrewwuan/PredictionIO-Churn-Prediction-H2O-Sparkling-Water";>Churn
 Prediction - H2O Sparkling Water</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=andrewwuan&amp;repo=PredictionIO-Churn-Prediction-H2O-Sparkling-Water&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is an engine template with Sparkling Water 
integration. The goal is to use Deep Learning algorithm to predict the churn 
rate for a phone carrier's customers. </p> <p>Support: <a 
href="https://github.com/andrewwuan/PredictionIO-Churn-Prediction-H2O-Sparkling-Water/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th
 > </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
 > <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> 
 > <br> <h3><a 
 > href="https://github.com/detrevid/predictionio-template-classification-dl4j";>Classification
 >  Deeplearning4j</a></h3> <iframe 
 > src="https://ghbtns.com/github-btn.html?user=detrevid&amp;repo=predictionio-template-classification-dl4j&amp;type=star&amp;count=true";
 >  frameborder="0" align="middle" scrolling="0" width="170px" 
 > height="20px"></iframe> <p> A classification engine template that uses 
 > Deeplearning4j library. </p> <p>Support: <a 
 > href="https://github.com/detrevid/predictionio-template-classification-dl4j/issues";>Github
 >  issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
 > <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
 > Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
 > <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires 
 > conversion</td> </tr> </table> <br> 
 <h3><a 
href="https://github.com/EmergentOrder/template-scala-probabilistic-classifier-batch-lbfgs";>Probabilistic
 Classifier (Logistic Regression w/ LBFGS)</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=EmergentOrder&amp;repo=template-scala-probabilistic-classifier-batch-lbfgs&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> A PredictionIO engine template using logistic 
regression (trained with limited-memory BFGS ) with raw (probabilistic) 
outputs. </p> <p>Support: <a 
href="https://github.com/EmergentOrder/template-scala-probabilistic-classifier-batch-lbfgs/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>MIT 
License</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> 
</table> <br> <h3><a href="https://github.c
 om/chrischris292/template-classification-opennlp">Document Classification with 
OpenNLP</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=chrischris292&amp;repo=template-classification-opennlp&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Document Classification template with OpenNLP 
GISModel. </p> <p>Support: <a 
href="https://github.com/chrischris292/template-classification-opennlp/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/harry5z/template-circuit-classification-sparkling-water";>Circuit
 End Use Classification</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=harry5z&amp;repo=te
 mplate-circuit-classification-sparkling-water&amp;type=star&amp;count=true" 
frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> A classification engine template that uses machine 
learning models trained with sample circuit energy consumption data and end 
usage to predict the end use of a circuit by its energy consumption history. 
</p> <p>Support: <a 
href="https://github.com/harry5z/template-circuit-classification-sparkling-water/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.1</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/ailurus1991/GBRT_Template_PredictionIO";>GBRT_Classification</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=ailurus1991&amp;repo=GBRT_Template_PredictionIO
 &amp;type=star&amp;count=true" frameborder="0" align="middle" scrolling="0" 
width="170px" height="20px"></iframe> <p> The Gradient-Boosted Regression 
Trees(GBRT) for classification. </p> <p>Support: <a 
href="https://github.com/ailurus1991/GBRT_Template_PredictionIO/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/mohanaprasad1994/PredictionIO-MLlib-Decision-Trees-Template";>MLlib-Decision-Trees-Template</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=mohanaprasad1994&amp;repo=PredictionIO-MLlib-Decision-Trees-Template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> An engine template is an almost-
 complete implementation of an engine. This is a classification engine template 
which has integrated Apache Spark MLlib's Decision tree algorithm by default. 
</p> <p>Support: <a 
href="https://github.com/mohanaprasad1994/PredictionIO-MLlib-Decision-Trees-Template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/jimmyywu/predictionio-template-classification-dl4j-multilayer-network";>Classification
 with MultiLayerNetwork</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=jimmyywu&amp;repo=predictionio-template-classification-dl4j-multilayer-network&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This eng
 ine template integrates the MultiLayerNetwork implementation from the 
Deeplearning4j library into PredictionIO. In this template, we use PredictionIO 
to classify the widely-known IRIS flower dataset by constructing a deep-belief 
net. </p> <p>Support: <a 
href="https://github.com/jimmyywu/predictionio-template-classification-dl4j-multilayer-network/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn";>Deeplearning4j
 RNTN</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=thomasste&amp;repo=template-scala-parallel-dl4j-rntn&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" height="20px"></ifr
 ame> <p> Recursive Neural Tensor Network algorithm is supervised learning 
algorithm used to predict sentiment of sentences. This template is based on 
deeplearning4j RNTN example: 
https://github.com/SkymindIO/deeplearning4j-nlp-examples/tree/master/src/main/java/org/deeplearning4j/rottentomatoes/rntn.
 It's goal is to show how to integrate deeplearning4j library with 
PredictionIO. </p> <p>Support: <a 
href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/singsanj/classifier-kafka-streaming-template";>classifier-kafka-streaming-template</a></h3>
 <iframe src="https://ghbtns.com/github-btn.html?user=singsanj&amp;repo=clas
 sifier-kafka-streaming-template&amp;type=star&amp;count=true" frameborder="0" 
align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> The 
template will provide a simple integration of DASE with kafka using spark 
streaming capabilities in order to play around with real time notification, 
messages .. </p> <p>Support: <a 
href="https://github.com/singsanj/classifier-kafka-streaming-template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> 
</table> <br> <h3><a 
href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template";>Sentiment
 Analysis - Bag of Words Model</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=peoplehum&amp;repo=BagOfWords_SentimentAnalysis_Template&amp;type=star&amp;count=true
 " frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This sentiment analysis template uses a bag of 
words model. Given text, the engine will return sentiment as 1.0 (positive) or 
0.0 (negative) along with scores indicating how +ve or -ve it is. </p> 
<p>Support: <a 
href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/jpioug/predictionio-template-iris";>Classification 
template for Iris</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=jpioug&amp;repo=predictionio-template-iris&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" wid
 th="170px" height="20px"></iframe> <p> This is Python(PySpark) based 
classification example for Iris dataset. </p> <p>Support: </p> <br> <table> 
<tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO 
min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> 
<td>Parallel</td> <td>Python</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.12.0-incubating</td> <td></td> </tr> </table> <br> </div> <div 
data-tab="Regression" id="tab-37cb3eaa-af9b-4c1f-9fdc-bc0398ba3dbd"> <h3><a 
href="https://github.com/goliasz/pio-template-sr";>Survival Regression</a></h3> 
<iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-sr&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Survival regression template is based on brand new 
Spark 1.6 AFT (accelerated failure time) survival analysis algorithm. There are 
interesting applications of survival analysis like: </p> <ul class="tab
 -list"> <li class="tab-list-element">Business Planning : Profiling customers 
who has a higher survival rate and make strategy accordingly.</li> <li 
class="tab-list-element">Lifetime Value Prediction : Engage with customers 
according to their lifetime value</li> <li class="tab-list-element">Active 
customers : Predict when the customer will be active for the next time and take 
interventions accordingly. * Campaign evaluation : Monitor effect of campaign 
on the survival rate of customers.</li> </ul> Source: 
http://www.analyticsvidhya.com/blog/2014/04/survival-analysis-model-you/ 
<p>Support: <a 
href="http://www.analyticsvidhya.com/blog/2014/04/survival-analysis-model-you/";>Blog
 post</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>beta</td> <td>0.9.5</td> <td>requires conversion</td> 
</tr> </table> <br> <h3>
 <a 
href="https://github.com/BensonQiu/predictionio-template-recommendation-sparklingwater";>Sparkling
 Water-Deep Learning Energy Forecasting</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=BensonQiu&amp;repo=predictionio-template-recommendation-sparklingwater&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This Engine Template demonstrates an energy 
forecasting engine. It integrates Deep Learning from the Sparkling Water 
library to perform energy analysis. We can query the circuit and time, and 
return predicted energy usage. </p> <p>Support: <a 
href="https://github.com/BensonQiu/predictionio-template-recommendation-sparklingwater/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td
 > <td>requires conversion</td> </tr> </table> <br> <h3><a 
 > href="https://github.com/detrevid/predictionio-load-forecasting";>Electric 
 > Load Forecasting</a></h3> <iframe 
 > src="https://ghbtns.com/github-btn.html?user=detrevid&amp;repo=predictionio-load-forecasting&amp;type=star&amp;count=true";
 >  frameborder="0" align="middle" scrolling="0" width="170px" 
 > height="20px"></iframe> <p> This is a PredictionIO engine for electric load 
 > forecasting. The engine is using linear regression with stochastic gradient 
 > descent from Spark MLlib. </p> <p>Support: <a 
 > href="https://github.com/detrevid/predictionio-load-forecasting/issues";>Github
 >  issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
 > <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
 > Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
 > <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires 
 > conversion</td> </tr> </table> <br> <h3><a 
 > href="https://github.com/RAditi/PredictionIO-MLLib-
 LinReg-Template">MLLib-LinearRegression</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=RAditi&amp;repo=PredictionIO-MLLib-LinReg-Template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template uses the linear regression with 
stochastic gradient descent algorithm from MLLib to make predictions on 
real-valued data based on features (explanatory variables) </p> <p>Support: <a 
href="https://github.com/RAditi/PredictionIO-MLLib-LinReg-Template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.1</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/mgcdanny/pio-linear-regression-bfgs";>Linear Regression 
BFGS</a></h3> <iframe src="https://ghbtns.com/gi
 
thub-btn.html?user=mgcdanny&amp;repo=pio-linear-regression-bfgs&amp;type=star&amp;count=true"
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Modeling the relationship between a dependent 
variable, y, and one or more explanatory variables, denoted X. </p> <p>Support: 
</p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>beta</td> <td>0.10.0</td> <td></td> </tr> </table> <br> <h3><a 
href="https://github.com/jpioug/predictionio-template-boston-house-prices";>Regression
 template for Boston House Prices</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=jpioug&amp;repo=predictionio-template-boston-house-prices&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is Python(PySpark) based regression ex
 ample for Boston House Prices dataset. </p> <p>Support: </p> <br> <table> <tr> 
<th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min 
version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> 
<td>Parallel</td> <td>Python</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.12.0-incubating</td> <td></td> </tr> </table> <br> </div> <div 
data-tab="NLP" id="tab-b3b7766a-9dd2-4c76-9b11-af2624191a70"> <h3><a 
href="https://github.com/goliasz/pio-template-text-similarity";>Cstablo-template-text-similarity-classification</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-text-similarity&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Text similarity engine based on Word2Vec algorithm. 
Builds vectors of full documents in training phase. Finds similar documents in 
query phase. </p> <p>Support: <a 
href="https://github.com/goliasz/pio-template-text-similarity/
 issues">Github issues</a></p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> 
<td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/rajdeepd/template-Labelling-Topics-with-wikipedia";>Topic
 Labelling with Wikipedia</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=rajdeepd&amp;repo=template-Labelling-Topics-with-wikipedia&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template will label topics (e.g. topic 
generated through LDA topic modeling) with relevant category by referring to 
Wikipedia as a knowledge base. </p> <p>Support: <a 
href="https://github.com/peoplehum/template-Labelling-Topics-with-wikipedia/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Lan
 guage</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/apache/predictionio-template-text-classifier";>Text 
Classification</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=predictionio-template-text-classifier&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Use this engine for general text classification 
purposes. Uses OpenNLP library for text vectorization, includes 
t.f.-i.d.f.-based feature transformation and reduction, and uses Spark MLLib's 
Multinomial Naive Bayes implementation for classification. </p> <p>Support: <a 
href="https://github.com/apache/predictionio-template-text-classifier/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Typ
 e</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min 
version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> 
<td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> 
<td>0.11.0-incubating</td> <td>requires conversion</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn";>Deeplearning4j
 RNTN</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=thomasste&amp;repo=template-scala-parallel-dl4j-rntn&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Recursive Neural Tensor Network algorithm is 
supervised learning algorithm used to predict sentiment of sentences. This 
template is based on deeplearning4j RNTN example: 
https://github.com/SkymindIO/deeplearning4j-nlp-examples/tree/master/src/main/java/org/deeplearning4j/rottentomatoes/rntn.
 It's goal is to show how to integrate deeplearning4j library with 
PredictionIO. </p> <p>Su
 pport: <a 
href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template";>Sentiment
 Analysis - Bag of Words Model</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=peoplehum&amp;repo=BagOfWords_SentimentAnalysis_Template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This sentiment analysis template uses a bag of 
words model. Given text, the engine will return sentiment as 1.0 (positive) or 
0.0 (negative) along with scores indicating how +ve or -ve it is. </p> 
<p>Support: <a href="https://github.
 com/peoplehum/BagOfWords_SentimentAnalysis_Template/issues">Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/infoquestsolutions/OpenNLP-SentimentAnalysis-Template";>OpenNLP
 Sentiment Analysis Template</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=infoquestsolutions&amp;repo=OpenNLP-SentimentAnalysis-Template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Given a sentence, this engine will return a score 
between 0 and 4. This is the sentiment of the sentence. The lower the number 
the more negative the sentence is. It uses the OpenNLP library. </p> 
<p>Support: <a href="https://github.com/info
 questsolutions/OpenNLP-SentimentAnalysis-Template/issues">Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>beta</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/pawel-n/template-scala-cml-sentiment";>Sentiment 
analysis</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=pawel-n&amp;repo=template-scala-cml-sentiment&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template implements various algorithms for 
sentiment analysis, most based on recursive neural networks (RNN) and recursive 
neural tensor networks (RNTN)[1]. It uses an experimental library called 
Composable Machine Learning (CML) and the Stanford Parser. The example data set 
is the Stanfor
 d Sentiment Treebank. </p> <p>Support: <a 
href="https://github.com/pawel-n/template-scala-cml-sentiment/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/pawel-n/template-scala-parallel-word2vec";>Word2Vec</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=pawel-n&amp;repo=template-scala-parallel-word2vec&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template integrates the Word2Vec 
implementation from deeplearning4j with PredictionIO. The Word2Vec algorithm 
takes a corpus of text and computes a vector representation for each word. 
These representations can be subsequently used in many natural 
 language processing applications. </p> <p>Support: <a 
href="https://github.com/pawel-n/template-scala-parallel-word2vec/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/thomasste/template-scala-spark-dl4j-word2vec";>Spark 
Deeplearning4j Word2Vec</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=thomasste&amp;repo=template-scala-spark-dl4j-word2vec&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template shows how to integrate Deeplearnign4j 
spark api with PredictionIO on example of app which uses Word2Vec algorithm to 
predict nearest words. </p> <p>Support: <a href="https://github.com/thomasste
 /template-scala-spark-dl4j-word2vec/issues">Github issues</a></p> <br> <table> 
<tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO 
min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> 
<td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/whhone/template-sentiment-analysis";>Sentiment Analysis 
Template</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=whhone&amp;repo=template-sentiment-analysis&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Given a sentence, return a score between 0 and 4, 
indicating the sentence's sentiment. 0 being very negative, 4 being very 
positive, 2 being neutral. The engine uses the stanford CoreNLP library and the 
Scala binding `gangeli/CoreNLP-Scala` for parsing. </p> <p>Support: <a 
href="https://github.com/whhone/templat
 e-sentiment-analysis/issues">Github issues</a></p> <br> <table> <tr> 
<th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min 
version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> 
<td>Parallel</td> <td>Scala</td> <td>None</td> <td>stable</td> <td>0.9.0</td> 
<td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/thomasste/template-scala-rnn";>Recursive Neural 
Networks (Sentiment Analysis)</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=thomasste&amp;repo=template-scala-rnn&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Predicting sentiment of phrases with use of 
Recursive Neural Network algorithm and OpenNLP parser. </p> <p>Support: <a 
href="https://github.com/thomasste/template-scala-rnn/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion
  Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 
2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires conversion</td> </tr> 
</table> <br> <h3><a 
href="https://github.com/Ling-Ling/CoreNLP-Text-Classification";>CoreNLP Text 
Classification</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=Ling-Ling&amp;repo=CoreNLP-Text-Classification&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine uses CoreNLP to do text analysis in 
order to classify the category a strings of text falls under. </p> <p>Support: 
<a 
href="https://github.com/Ling-Ling/CoreNLP-Text-Classification/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> </
 table> <br> </div> <div data-tab="Clustering" 
id="tab-755e8c7a-8357-43da-b91d-3caf835d5802"> <h3><a 
href="https://github.com/sahiliitm/predictionio-MLlibKMeansClusteringTemplate";>MLlibKMeansClustering</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=sahiliitm&amp;repo=predictionio-MLlibKMeansClusteringTemplate&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is a template which demonstrates the use of 
K-Means clustering algorithm which can be deployed on a spark-cluster using 
prediction.io. </p> <p>Support: <a 
href="https://github.com/sahiliitm/predictionio-MLlibKMeansClusteringTemplate/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> 
</table
 > <br> <h3><a 
 > href="https://github.com/EmergentOrder/template-scala-topic-model-LDA";>Topc 
 > Model (LDA)</a></h3> <iframe 
 > src="https://ghbtns.com/github-btn.html?user=EmergentOrder&amp;repo=template-scala-topic-model-LDA&amp;type=star&amp;count=true";
 >  frameborder="0" align="middle" scrolling="0" width="170px" 
 > height="20px"></iframe> <p> A PredictionIO engine template using Latent 
 > Dirichlet Allocation to learn a topic model from raw text </p> <p>Support: 
 > <a 
 > href="https://github.com/EmergentOrder/template-scala-topic-model-LDA/issues";>Github
 >  issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
 > <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
 > Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
 > <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.4</td> <td>requires 
 > conversion</td> </tr> </table> <br> <h3><a 
 > href="https://github.com/singsanj/KMeans-parallel-template";>KMeans-Clustering-Template</a></h3>
 >  <iframe src="https://ghbtns.com/github-
 
btn.html?user=singsanj&amp;repo=KMeans-parallel-template&amp;type=star&amp;count=true"
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> forked from 
PredictionIO/template-scala-parallel-vanilla. It implements the KMeans 
Algorithm. Can be extended to mainstream implementation with minor changes. 
</p> <p>Support: <a 
href="https://github.com/singsanj/KMeans-parallel-template/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/rajdeepd/template-Labelling-Topics-with-wikipedia";>Topic
 Labelling with Wikipedia</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=rajdeepd&amp;repo=template-Labelling-Topics-with-wikipedia&amp;type=star&amp;coun
 t=true" frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template will label topics (e.g. topic 
generated through LDA topic modeling) with relevant category by referring to 
Wikipedia as a knowledge base. </p> <p>Support: <a 
href="https://github.com/peoplehum/template-Labelling-Topics-with-wikipedia/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/jirotubuyaki/predictionio-template-crp-clustering";>Bayesian
 Nonparametric Chinese Restaurant Process Clustering</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=jirotubuyaki&amp;repo=predictionio-template-crp-clustering&amp;type=star&amp;count=true";
 frameborder="0" ali
 gn="middle" scrolling="0" width="170px" height="20px"></iframe> <p> Chinese 
restaurant process is stochastic process for statistical inference. The 
clustering which uses Chinese restaurant process does not need to decide the 
number of clusters in advance. This algorithm automatically adjusts it. </p> 
<p>Support: <a 
href="https://github.com/jirotubuyaki/predictionio-template-crp-clustering/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> </div> <div data-tab="Similarity" 
id="tab-1b3da76d-ba97-4e7d-8a3f-d22262804f63"> <h3><a 
href="https://github.com/alexice/template-scala-parallel-svd-item-similarity";>Content
 Based SVD Item Similarity Engine</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?us
 
er=alexice&amp;repo=template-scala-parallel-svd-item-similarity&amp;type=star&amp;count=true"
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Template to calculate similarity between items 
based on their attributes—sometimes called content-based similarity. 
Attributes can be either numeric or categorical in the last case it will be 
encoded using one-hot encoder. Algorithm uses SVD in order to reduce data 
dimensionality. Cosine similarity is now implemented but can be easily extended 
to other similarity measures. </p> <p>Support: <a 
href="https://groups.google.com/forum/#!forum/actionml-user";>The Universal 
Recommender user group</a></p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> 
<td>requires conversion</td> </tr> </table> <br> <h3><a href="h
 
ttps://github.com/goliasz/pio-template-text-similarity">Cstablo-template-text-similarity-classification</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-text-similarity&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Text similarity engine based on Word2Vec algorithm. 
Builds vectors of full documents in training phase. Finds similar documents in 
query phase. </p> <p>Support: <a 
href="https://github.com/goliasz/pio-template-text-similarity/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating";>Similar
 Product with R
 ating</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=ramaboo&amp;repo=template-scala-parallel-similarproduct-with-rating&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Similar product template with rating support! Used 
for the MovieLens Demo. </p> <p>Support: <a 
href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>beta</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> </div> <div data-tab="Other" 
id="tab-8bd8fff5-dc00-4a46-b874-1e2861ed34ee"> <h3><a 
href="https://github.com/goliasz/pio-template-fpm";>Frequent Pattern 
Mining</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=p
 io-template-fpm&amp;type=star&amp;count=true" frameborder="0" align="middle" 
scrolling="0" width="170px" height="20px"></iframe> <p> Template uses FP Growth 
algorithm allowing to mine for frequent patterns. Template returns subsequent 
items together with confidence score. </p> <p>Support: <a 
href="https://github.com/goliasz/pio-template-fpm/issues";>Github issues</a></p> 
<br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/anthill/template-decision-tree-feature-importance";>template-decision-tree-feature-importance</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=anthill&amp;repo=template-decision-tree-feature-importance&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width
 ="170px" height="20px"></iframe> <p> This template shows how to use spark' 
decision tree. It enables : - both categorical and continuous features - 
feature importance calculation - tree output in json - reading training data 
from a csv file </p> <p>Support: <a 
href="https://github.com/anthill/template-decision-tree-feature-importance/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/apache/predictionio-template-skeleton";>Skeleton</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=predictionio-template-skeleton&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Skeleton template is for d
 eveloping new engine when you find other engine templates do not fit your 
needs. This template provides a skeleton to kick start new engine development. 
</p> <p>Support: <a href="http://predictionio.apache.org/support/";>Apache 
PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.11.0-incubating</td> <td>already compatible</td> </tr> </table> <br> 
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 <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" 
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SDK</span></a></li><li class="level-3"><a class="final" 
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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 Predictions</span></a></li><li 
class="level-2"><a class="final" href="/deploy/monitoring/"><span>Monitorin
 g 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 
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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" 
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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/recommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" href="/templates/recomme
 ndation/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" 
href="/templates/recommendation/batch-evaluator/"><span>Batch Persistable Evalua
 tor</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="/templates/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 
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="fina
 l" href="/templates/classification/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
class="level-2"><a class="final active" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
class="level-2"><a class="final" 
href="/community/submit-template/"><span>Submit your Engine as a 
Template</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Demo Tutorials</span>
 </a><ul><li class="level-2"><a class="final" 
href="/demo/tapster/"><span>Comics Recommenda

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