<!DOCTYPE html><html><head><title>Training with Implicit Preference (Recommendation)</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Training with Implicit Preference (Recommendation)"/><link rel="canonical" href="https://docs.prediction.io/templates/recommendation/training-with-implicit-preference/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet"/><link href="/stylesheets/application-3598c7d7.css" rel="stylesheet" type="text/css"/><script 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hidden-lg"><div id="page-title"><h1>Training with Implicit Preference (Recommendation)</h1></div></div><div id="table-of-content-wrapper"><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/recommendation/training-with-implicit-preference.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="page-title"><h1>Training with Implicit Preference (Recommendation)</h1></div></div><div class="content"><p>There are two types of user preferences:</p> <ul> <li>explicit preference (also referred as &quot;explicit feedback&quot;), such as &quot;rating&quot; given to item by users.</li> <li>implicit preference (also referred as &quot;implicit feedback&quot;), such as &quot;view&quot; and &quot;buy&quot; history.</li> </ul> <p>MLlib ALS provides two functions, <code>ALS.train()</code> and <code>ALS.trainImplicit()</code> to handle these two cases, respectively. The ALS algorithm takes RDD[Rating] as training data input. The Rating class is defined in Spark MLlib library as:</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="k">case</span> class Rating<span class="o">(</span>user: Int, product: Int, rating: Double<span class="p">)</span>
</pre></td></tr></tbody></table> </div> <p>By default, the recommendation template uses <code>ALS.train()</code> which expects explicit rating values which the user has rated the item.</p><p>To handle implicit preference, <code>ALS.trainImplicit()</code> can be used. In this case, the &quot;rating&quot; value input to ALS is used to calculate the confidence level that the user likes the item. Higher &quot;rating&quot; means a stronger indication that the user likes the item.</p><p>The following provides an example of using implicit preference.</p><h3 id='training-with-view-events' class='header-anchors'>Training with view events</h3><p>For example, if the more number of times the user has viewed the item, the higher confidence that the user likes the item. We can aggregate the number of views and use this as the &quot;rating&quot; value.</p><p>First, we can modify <code>DataSource.scala</code> to aggregate the number of views of the user on the same item:</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">class</span> <span class="nc">DataSource</span><span class="o">(</span><span class="k">val</span> <span class="n">dsp</span><span class="k">:</span> <span class="kt">DataSourceParams</span><span class="o">)</span>
  <span class="k">extends</span> <span class="nc">PDataSource</span><span class="o">[</span><span class="kt">TrainingData</span>,
      <span class="kt">EmptyEvaluationInfo</span>, <span class="kt">Query</span>, <span class="kt">EmptyActualResult</span><span class="o">]</span> <span class="o">{</span>

  <span class="nd">@transient</span> <span class="k">lazy</span> <span class="k">val</span> <span class="n">logger</span> <span class="k">=</span> <span class="nc">Logger</span><span class="o">[</span><span class="kt">this.</span><span class="k">type</span><span class="o">]</span>

  <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="k">val</span> <span class="n">eventsDb</span> <span class="k">=</span> <span class="nc">Storage</span><span class="o">.</span><span class="n">getPEvents</span><span class="o">()</span>
    <span class="k">val</span> <span class="n">eventsRDD</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Event</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">"view"</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="k">val</span> <span class="n">ratingsRDD</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Rating</span><span class="o">]</span> <span class="k">=</span> <span class="n">eventsRDD</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">event</span> <span class="k">=&gt;</span>
      <span class="k">try</span> <span class="o">{</span>
        <span class="k">val</span> <span class="n">ratingValue</span><span class="k">:</span> <span class="kt">Double</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">"view"</span> <span class="k">=&gt;</span> <span class="mf">1.0</span> <span class="c1">// MODIFIED
</span>          <span class="k">case</span> <span class="k">_</span> <span class="k">=&gt;</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="c1">// MODIFIED
</span>        <span class="c1">// key is (user id, item id)
</span>        <span class="c1">// value is the rating value, which is 1.
</span>        <span class="o">((</span><span class="n">event</span><span class="o">.</span><span class="n">entityId</span><span class="o">,</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">ratingValue</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">=&gt;</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 Rating. 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="o">}</span>
    <span class="c1">// MODIFIED
</span>    <span class="c1">// sum all values for the same user id and item id key
</span>    <span class="o">.</span><span class="n">reduceByKey</span> <span class="o">{</span> <span class="k">case</span> <span class="o">(</span><span class="n">a</span><span class="o">,</span> <span class="n">b</span><span class="o">)</span> <span class="k">=&gt;</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> <span class="o">}</span>
    <span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="k">case</span> <span class="o">((</span><span class="n">uid</span><span class="o">,</span> <span class="n">iid</span><span class="o">),</span> <span class="n">r</span><span class="o">)</span> <span class="k">=&gt;</span>
      <span class="nc">Rating</span><span class="o">(</span><span class="n">uid</span><span class="o">,</span> <span class="n">iid</span><span class="o">,</span> <span class="n">r</span><span class="o">)</span>
    <span class="o">}.</span><span class="n">cache</span><span class="o">()</span>

    <span class="k">new</span> <span class="nc">TrainingData</span><span class="o">(</span><span class="n">ratingsRDD</span><span class="o">)</span>
  <span class="o">}</span>
<span class="o">}</span>

</pre></td></tr></tbody></table> </div> <div class="alert-message note"><p>You may put the view count aggregation logic in <code>ALSAlgorithm</code>&#39;s <code>train()</code> instead, depending on your needs.</p></div><p>Then, we can modify ALSAlgorithm.scala to call <code>ALS.trainImplicit()</code> instead of <code>ALS.train()</code>:</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">class</span> <span class="nc">ALSAlgorithm</span><span class="o">(</span><span class="k">val</span> <span class="n">ap</span><span class="k">:</span> <span class="kt">ALSAlgorithmParams</span><span class="o">)</span>
  <span class="k">extends</span> <span class="nc">PAlgorithm</span><span class="o">[</span><span class="kt">PreparedData</span>, <span class="kt">ALSModel</span>, <span class="kt">Query</span>, <span class="kt">PredictedResult</span><span class="o">]</span> <span class="o">{</span>

  <span class="o">...</span>

  <span class="k">def</span> <span class="n">train</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="n">data</span><span class="k">:</span> <span class="kt">PreparedData</span><span class="o">)</span><span class="k">:</span> <span class="kt">ALSModel</span> <span class="o">=</span> <span class="o">{</span>

    <span class="o">...</span>

    <span class="c1">// MODIFIED
</span>    <span class="k">val</span> <span class="n">m</span> <span class="k">=</span> <span class="nc">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="o">(</span>
      <span class="n">ratings</span> <span class="k">=</span> <span class="n">mllibRatings</span><span class="o">,</span>
      <span class="n">rank</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">rank</span><span class="o">,</span>
      <span class="n">iterations</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">numIterations</span><span class="o">,</span>
      <span class="n">lambda</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">lambda</span><span class="o">,</span>
      <span class="n">blocks</span> <span class="k">=</span> <span class="o">-</span><span class="mi">1</span><span class="o">,</span>
      <span class="n">alpha</span> <span class="k">=</span> <span class="mf">1.0</span><span class="o">,</span>
      <span class="n">seed</span> <span class="k">=</span> <span class="n">seed</span><span class="o">)</span>

    <span class="k">new</span> <span class="nc">ALSModel</span><span class="o">(</span>
      <span class="n">rank</span> <span class="k">=</span> <span class="n">m</span><span class="o">.</span><span class="n">rank</span><span class="o">,</span>
      <span class="n">userFeatures</span> <span class="k">=</span> <span class="n">m</span><span class="o">.</span><span class="n">userFeatures</span><span class="o">,</span>
      <span class="n">productFeatures</span> <span class="k">=</span> <span class="n">m</span><span class="o">.</span><span class="n">productFeatures</span><span class="o">,</span>
      <span class="n">userStringIntMap</span> <span class="k">=</span> <span class="n">userStringIntMap</span><span class="o">,</span>
      <span class="n">itemStringIntMap</span> <span class="k">=</span> <span class="n">itemStringIntMap</span><span class="o">)</span>
  <span class="o">}</span>

  <span class="o">...</span>

<span class="o">}</span>

</pre></td></tr></tbody></table> </div> <p>Now the recommendation engine can train a model with implicit preference events.</p></div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//docs.prediction.io/install/" target="blank">Download</a></li><li><a href="//docs.prediction.io/" target="blank">Docs</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">GitHub</a></li><li><a href="mailto:user-subscribe@predictionio.incubator.apache.org" target="blank">Subscribe to User Mailing List</a></li><li><a href="//stackoverflow.com/questions/tagged/predictionio" target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.incubator.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">Source Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:dev-subscribe@predictionio.incubator.apache.org" target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div></div><div id="footer-bottom"><div class="container"><div class="row"><div class="col-md-12"><div id="footer-logo-wrapper"><img alt="PredictionIO" src="/images/logos/logo-white-d1e9c6e6.png"/></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/incubator-predictionio" data-style="mega" data-count-href="/apache/incubator-predictionio/stargazers" data-count-api="/repos/apache/incubator-predictionio#stargazers_count" data-count-aria-label="# stargazers on GitHub" aria-label="Star apache/incubator-predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/incubator-predictionio/fork" data-icon="octicon-git-branch" data-style="mega" data-count-href="/apache/incubator-predictionio/network" data-count-api="/repos/apache/incubator-predictionio#forks_count" data-count-aria-label="# forks on GitHub" aria-label="Fork apache/incubator-predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js"></script><a href="//www.facebook.com/predictionio" target="blank"><img alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> <a href="//twitter.com/predictionio" target="blank"><img alt="PredictionIO on Facebook" src="/images/icons/facebook-5c57939c.png"/></a> </div></div></div></div></div></footer></div><script>(function(w,d,t,u,n,s,e){w['SwiftypeObject']=n;w[n]=w[n]||function(){
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