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(Recommendation)</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#add-query-parameter">Add Query Parameter</a> </li> <li> <a href="#filter-the-data">Filter the Data</a> </li> <li> <a href="#put-it-all-together">Put It All Together</a> </li> <li> <a href="#test-the-result">Test the Result</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/recommendation/blacklist-items.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>Filter Recommended Items by Blacklist in Query (Recommendation)</h1></div></div><div class="content"><p>Let&#39;s say you want to supply a backList for each query to exclude some items from recommendation (For example, in the browsing session, the user just added some items to shopping cart, or you have a list of items you want to filter out, you may want to supply blackList in Query). This how-to will demonstrate how you can do it.</p><p>Note that you may also use <a href="http://templates.prediction.io/PredictionIO/template-scala-parallel-ecommercerecommendation">E-Commerce Recommendation Template</a> which supports this feature by default.</p><p>If you are looking for filtering out items based on the specific user-to-item events logged by EventServer (eg. filter all items which the user has &quot;buy&quot; events on), you can use the <a href="http://templates.prediction.io/PredictionIO/template-scala-parallel-ecommercerecommendation">E-Commerce Recommendation Template</a>. Please refer to the algorithm parameters &quot;unseenOnly&quot; and &quot;seenEvents&quot; of the E-Commerce Recommenation Template.</p><h2 id='add-query-parameter' class='header-anchors'>Add Query Parameter</h2><p>First of all we need to specify query parameter to send items ids that the user has already seen. Lets modify <code>case class Query</code> in MyRecommendation/src/main/scala/<strong><em>Engine.scala</em></strong>:</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="n">user</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
  <span class="n">num</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
  <span class="n">blackList</span><span class="k">:</span> <span class="kt">Set</span><span class="o">[</span><span class="kt">String</span><span class="o">]</span> <span class="c1">// NOTE: line added
</span><span class="o">)</span> <span class="k">extends</span> <span class="nc">Serializable</span>
</pre></td></tr></tbody></table> </div> <h2 id='filter-the-data' class='header-anchors'>Filter the Data</h2><p>Then we need to change the code that computes recommendation score to filter out the seen items. Lets modify class MyRecommendation/src/main/scala/<strong><em>ALSModel.scala</em></strong>. Just add the following two methods to that class.</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="n">user</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
      <span class="n">num</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
      <span class="n">productIdFilter</span><span class="k">:</span> <span class="kt">Set</span><span class="o">[</span><span class="kt">Int</span><span class="o">])</span> <span class="k">=</span> <span class="o">{</span>
    <span class="k">val</span> <span class="n">filteredProductFeatures</span> <span class="k">=</span> <span class="n">productFeatures</span>
      <span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">features</span> <span class="k">=&gt;</span> <span class="o">!</span><span class="n">productIdFilter</span><span class="o">.</span><span class="n">contains</span><span class="o">(</span><span class="n">features</span><span class="o">.</span><span class="n">_1</span><span class="o">))</span> <span class="c1">// (*)
</span>    <span class="n">recommend</span><span class="o">(</span><span class="n">userFeatures</span><span class="o">.</span><span class="n">lookup</span><span class="o">(</span><span class="n">user</span><span class="o">).</span><span class="n">head</span><span class="o">,</span> <span class="n">filteredProductFeatures</span><span class="o">,</span> <span class="n">num</span><span class="o">)</span>
      <span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">t</span> <span class="k">=&gt;</span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">t</span><span class="o">.</span><span class="n">_1</span><span class="o">,</span> <span class="n">t</span><span class="o">.</span><span class="n">_2</span><span class="o">))</span>
  <span class="o">}</span>

  <span class="k">private</span> <span class="k">def</span> <span class="n">recommend</span><span class="o">(</span>
      <span class="n">recommendToFeatures</span><span class="k">:</span> <span class="kt">Array</span><span class="o">[</span><span class="kt">Double</span><span class="o">],</span>
      <span class="n">recommendableFeatures</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[(</span><span class="kt">Int</span>, <span class="kt">Array</span><span class="o">[</span><span class="kt">Double</span><span class="o">])],</span>
      <span class="n">num</span><span class="k">:</span> <span class="kt">Int</span><span class="o">)</span><span class="k">:</span> <span class="kt">Array</span><span class="o">[(</span><span class="kt">Int</span>, <span class="kt">Double</span><span class="o">)]</span> <span class="k">=</span> <span class="o">{</span>
    <span class="k">val</span> <span class="n">recommendToVector</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">DoubleMatrix</span><span class="o">(</span><span class="n">recommendToFeatures</span><span class="o">)</span>
    <span class="k">val</span> <span class="n">scored</span> <span class="k">=</span> <span class="n">recommendableFeatures</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">id</span><span class="o">,</span><span class="n">features</span><span class="o">)</span> <span class="k">=&gt;</span>
      <span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="n">recommendToVector</span><span class="o">.</span><span class="n">dot</span><span class="o">(</span><span class="k">new</span> <span class="nc">DoubleMatrix</span><span class="o">(</span><span class="n">features</span><span class="o">)))</span>
    <span class="o">}</span>
    <span class="n">scored</span><span class="o">.</span><span class="n">top</span><span class="o">(</span><span class="n">num</span><span class="o">)(</span><span class="nc">Ordering</span><span class="o">.</span><span class="n">by</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">_2</span><span class="o">))</span>
  <span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p>Also it’s required to add import of the <code>org.jblas.DoubleMatrix</code> class. Please make attention that method <code>recommend</code> is the copy of method <code>org.apache.spark.mllib.recommendation.MatrixFactorizationModel#recommend</code>. We can&#39;t reuse this because it’s private. Method <code>recommendProductsWithFilter</code> is the almost full copy of <code>org.apache.spark.mllib.recommendation.MatrixFactorizationModel#recommendProduct</code> method. The difference only is the line with commentary ‘(*)’ where we apply filtering.</p><h2 id='put-it-all-together' class='header-anchors'>Put It All Together</h2><p>Next we need to invoke our new method with filtering when we query recommendations. Lets modify method <code>predict</code> in MyRecommendation/src/main/scala/<strong><em>ALSAlgorithm.scala</em></strong>:</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="c1">// Convert String ID to Int index for Mllib
</span>    <span class="n">model</span><span class="o">.</span><span class="n">userStringIntMap</span><span class="o">.</span><span class="n">get</span><span class="o">(</span><span class="n">query</span><span class="o">.</span><span class="n">user</span><span class="o">).</span><span class="n">map</span> <span class="o">{</span> <span class="n">userInt</span> <span class="k">=&gt;</span>
      <span class="c1">// create inverse view of itemStringIntMap
</span>      <span class="k">val</span> <span class="n">itemIntStringMap</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">itemStringIntMap</span><span class="o">.</span><span class="n">inverse</span>
      <span class="c1">// recommendProducts() returns Array[MLlibRating], which uses item Int
</span>      <span class="c1">// index. Convert it to String ID for returning PredictedResult
</span>      <span class="k">val</span> <span class="n">blackListedIds</span> <span class="k">=</span> <span class="n">query</span><span class="o">.</span><span class="n">blackList</span><span class="o">.</span><span class="n">flatMap</span><span class="o">(</span><span class="n">model</span><span class="o">.</span><span class="n">itemStringIntMap</span><span class="o">.</span><span class="n">get</span><span class="o">)</span> <span class="c1">// NOTE: line added
</span>      <span class="k">val</span> <span class="n">itemScores</span> <span class="k">=</span> <span class="n">model</span>
        <span class="o">.</span><span class="n">recommendProductsWithFilter</span><span class="o">(</span><span class="n">userInt</span><span class="o">,</span> <span class="n">query</span><span class="o">.</span><span class="n">num</span><span class="o">,</span> <span class="n">blackListedIds</span><span class="o">)</span> <span class="c1">// NOTE: line modified
</span>        <span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">r</span> <span class="k">=&gt;</span> <span class="nc">ItemScore</span><span class="o">(</span><span class="n">itemIntStringMap</span><span class="o">(</span><span class="n">r</span><span class="o">.</span><span class="n">product</span><span class="o">),</span> <span class="n">r</span><span class="o">.</span><span class="n">rating</span><span class="o">))</span>
      <span class="k">new</span> <span class="nc">PredictedResult</span><span class="o">(</span><span class="n">itemScores</span><span class="o">)</span>
    <span class="o">}.</span><span class="n">getOrElse</span><span class="o">{</span>
      <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="o">(</span><span class="n">s</span><span class="s">"No prediction for unknown user ${query.user}."</span><span class="o">)</span>
      <span class="k">new</span> <span class="nc">PredictedResult</span><span class="o">(</span><span class="nc">Array</span><span class="o">.</span><span class="n">empty</span><span class="o">)</span>
    <span class="o">}</span>
  <span class="o">}</span>
</pre></td></tr></tbody></table> </div> <h2 id='test-the-result' class='header-anchors'>Test the Result</h2><p>Then we can build/train/deploy the engine and test the result:</p><p>The query</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
3
4</pre></td><td class="code"><pre>curl <span class="se">\</span>
-H <span class="s2">"Content-Type: application/json"</span> <span class="se">\</span>
-d <span class="s1">'{ "user": "1", "num": 4 }'</span> <span class="se">\</span>
http://localhost:8000/queries.json
</pre></td></tr></tbody></table> </div> <p>will return the result</p><div class="highlight json"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
3
4
5
6
7
8
9
10
11
12
13
14
15</pre></td><td class="code"><pre><span class="p">{</span><span class="w">
    </span><span class="s2">"itemScores"</span><span class="p">:</span><span class="w"> </span><span class="p">[{</span><span class="w">
        </span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"32"</span><span class="p">,</span><span class="w">
        </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">13.405593705856901</span><span class="w">
    </span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="w">
        </span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"90"</span><span class="p">,</span><span class="w">
        </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">10.980439687813178</span><span class="w">
    </span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="w">
        </span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"75"</span><span class="p">,</span><span class="w">
        </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">10.748973860065737</span><span class="w">
    </span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="w">
        </span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"1"</span><span class="p">,</span><span class="w">
        </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">9.769636099226231</span><span class="w">
    </span><span class="p">}]</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></pre></td></tr></tbody></table> </div> <p>Lets say that the user has seen the <code>32</code> item.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
3
4</pre></td><td class="code"><pre>curl <span class="se">\</span>
-H <span class="s2">"Content-Type: application/json"</span> <span class="se">\</span>
-d <span class="s1">'{ "user": "1", "num": 4, "blackList": ["32"] }'</span> <span class="se">\</span>
http://localhost:8000/queries.json
</pre></td></tr></tbody></table> </div> <p>will return the result</p><div class="highlight json"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
3
4
5
6
7
8
9
10
11
12
13
14
15</pre></td><td class="code"><pre><span class="p">{</span><span class="w">
    </span><span class="s2">"itemScores"</span><span class="p">:</span><span class="w"> </span><span class="p">[{</span><span class="w">
        </span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"90"</span><span class="p">,</span><span class="w">
        </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">10.980439687813178</span><span class="w">
    </span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="w">
        </span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"75"</span><span class="p">,</span><span class="w">
        </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">10.748973860065737</span><span class="w">
    </span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="w">
        </span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"1"</span><span class="p">,</span><span class="w">
        </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">9.769636099226231</span><span class="w">
    </span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="w">
        </span><span class="s2">"item"</span><span class="p">:</span><span class="w"> </span><span class="s2">"49"</span><span class="p">,</span><span class="w">
        </span><span class="s2">"score"</span><span class="p">:</span><span class="w"> </span><span class="mf">8.653951817512265</span><span class="w">
    </span><span class="p">}]</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></pre></td></tr></tbody></table> </div> <p>without item <code>32</code>.</p></div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-4 col-md-push-8 col-xs-12"><div class="subscription-form-wrapper"><h4>Subscribe to our Newsletter</h4><form class="ajax-form" id="subscribe-form" method="POST" action="https://script.google.com/macros/s/AKfycbwhzeKCQJjQ52eVAqNT_vcklH07OITUO7wzOMDXvK6EGAWgaZgF/exec"><input class="required underlined-input" type="email" placeholder="Your email address" name="subscription_email" id="subscription_email"/><input class="pill-button" value="SUBSCRIBE" type="submit" data-state-normal="SUBSCRIBE" data-state-sucess="SUBSCRIBED!" data-state-loading="SENDING..." onclick="t($('#subscription_email').val());"/><p class="result"></p></form></div></div><div class="col-md-2 col-md-pull-4 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/PredictionIO/PredictionIO" target="blank">GitHub</a></li><li><a href="//groups.google.com/forum/#!forum/predictionio-user" target="blank">Support Forum</a></li><li><a href="//stackoverflow.com/questions/tagged/predictionio" target="blank">Stackoverflow</a></li><li><a href="mailto:&#x73;&#x75;&#x70;&#x70;&#x6F;&#x72;&#x74;&#x40;&#x70;&#x72;&#x65;&#x64;&#x69;&#x63;&#x74;&#x69;&#x6F;&#x6E;&#x2E;&#x69;&#x6F;" target="blank">Contact Us</a></li></ul></div></div><div class="col-md-2 col-md-pull-4 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//docs.prediction.io/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/PredictionIO/PredictionIO" target="blank">Source Code</a></li><li><a href="//predictionio.atlassian.net/secure/Dashboard.jspa" target="blank">Bug Tracker</a></li><li><a href="//groups.google.com/forum/#!forum/predictionio-dev" target="blank">Contributors&#146; Forum</a></li><li><a href="//prediction.io/cla">Contributor Agreement</a></li><li><a href="//predictionio.uservoice.com/forums/219398-general/filters/top">Request Features</a></li></ul></div></div><div class="col-md-2 col-md-pull-4 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Enterprise</h4><ul><li><a href="//docs.prediction.io/support/" target="blank">Support</a></li><li><a href="//prediction.io/enterprise">Enterprise</a></li><li><a href="//prediction.io/products/predictionio-enterprise">Services</a></li></ul></div><div class="footer-link-column-row"><h4>Connect</h4><ul><li><a href="//blog.prediction.io/" target="blank">Blog</a></li><li><a href="//predictionio.theresumator.com/" target="blank">Careers</a></li></ul></div></div><div class="col-md-2 col-md-pull-4 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Partnership</h4><ul><li><a href="//prediction.io/partners/program">Partner Program</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/PredictionIO/PredictionIO" data-style="mega" data-count-href="/PredictionIO/PredictionIO/stargazers" data-count-api="/repos/PredictionIO/PredictionIO#stargazers_count" data-count-aria-label="# stargazers on GitHub" aria-label="Star PredictionIO/PredictionIO on GitHub">Star</a> <a class="github-button" href="https://github.com/PredictionIO/PredictionIO/fork" data-icon="octicon-git-branch" data-style="mega" data-count-href="/PredictionIO/PredictionIO/network" data-count-api="/repos/PredictionIO/PredictionIO#forks_count" data-count-aria-label="# forks on GitHub" aria-label="Fork PredictionIO/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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