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hidden-md hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">PredictionIO® Official Templates</a><span class="spacer">></span></li><li><a href="#">Recommendation</a><span class="spacer">></span></li><li><span class="last">Filter Recommended Items by Blacklist in Query</span></li></ul></div><div id="page-title"><h1>Filter Recommended Items by Blacklist in Query (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/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="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">PredictionIO® Official Templates</a><span class="spacer">></span></li><li><a href="#">Recommendation</a><span class="spacer">></span></li><li><span class="last">Filter Recommended Items by Blacklist in Query</span></li></ul></div><div id="page-title"><h1>Filter Recommended Items by Blacklist in Query (Recommendation)</h1></div></div><div class="content"> <p>Let'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>You can find the complete modified source code <a href="https://github.com/apache/predictionio/tree/develop/examples/scala-parallel-recommendation/blacklist-items">here</a>.</p><p>Note that you may also use <a href="/templates/ecommercerecommendation/quickstart/">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 "buy" events on), you can use the <a href="/templates/ecommercerecommendation/quickstart/">E-Commerce Recommendation Template</a>. Please refer to the algorithm parameters "unseenOnly" and "seenEvents" 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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| 5</pre></td><td class="code"><pre><span class="k">case</span> <span class="k">class</span> <span class="nc">Query</span><span class="o">(</span> |
| <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">// ADDED |
| </span><span class="o">)</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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| 25</pre></td><td class="code"><pre><span class="k">import</span> <span class="nn">com.github.fommil.netlib.BLAS.</span><span class="o">{</span><span class="n">getInstance</span> <span class="k">=></span> <span class="n">blas</span><span class="o">}</span> <span class="c1">// ADDED |
| </span> |
| <span class="o">...</span> |
| |
| <span class="c1">// ADDED |
| </span> <span class="k">def</span> <span class="n">recommendProductsWithFilter</span><span class="o">(</span><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="k">case</span> <span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="k">_</span><span class="o">)</span> <span class="k">=></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">id</span><span class="o">)</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">=></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="c1">// ADDED |
| </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">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">=></span> |
| <span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="n">blas</span><span class="o">.</span><span class="n">ddot</span><span class="o">(</span><span class="n">features</span><span class="o">.</span><span class="n">length</span><span class="o">,</span> <span class="n">recommendToFeatures</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="n">features</span><span class="o">,</span> <span class="mi">1</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> |
| |
| <span class="o">...</span> |
| |
| </pre></td></tr></tbody></table> </div> <p>Please make attention that method <code>recommend</code> is the copy of method <code>org.apache.spark.mllib.recommendation.MatrixFactorizationModel#recommend</code>. We can't reuse this because it’s private. Method <code>recommendProductsWithFilter</code> is the almost full copy of <code>org.apache.spark.mllib.recommendation.MatrixFactorizationModel#recommendProducts</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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| 17</pre></td><td class="code"><pre> <span class="k">def</span> <span class="n">predict</span><span class="o">(</span><span class="n">model</span><span class="k">:</span> <span class="kt">ALSModel</span><span class="o">,</span> <span class="n">query</span><span class="k">:</span> <span class="kt">Query</span><span class="o">)</span><span class="k">:</span> <span class="kt">PredictedResult</span> <span class="o">=</span> <span class="o">{</span> |
| <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">=></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">// recommendProductsWithFilter() 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">blackList</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">// 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">blackList</span><span class="o">)</span> <span class="c1">// MODIFIED |
| </span> <span class="o">.</span><span class="n">map</span> <span class="o">(</span><span class="n">r</span> <span class="k">=></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="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="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 |
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| 4 |
| 5 |
| 6 |
| 7 |
| 8 |
| 9 |
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| 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 |
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| 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-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//predictionio.apache.org/install/" target="blank">Download</a></li><li><a href="//predictionio.apache.org/" target="blank">Docs</a></li><li><a href="//github.com/apache/predictionio" target="blank">GitHub</a></li><li><a href="mailto:user-subscribe@predictionio.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 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/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.apache.org" target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div><div class="row"><div class="col-md-12 footer-link-column"><p>Apache PredictionIO, PredictionIO, Apache, the Apache feather logo, and the Apache PredictionIO project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p><p>All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p></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"/><span>®</span></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/predictionio" data-icon="octicon-star" data-show-count="true" aria-label="Star apache/predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/predictionio/fork" data-icon="octicon-repo-forked" data-show-count="true" aria-label="Fork apache/predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js"></script><a href="https://twitter.com/predictionio" target="blank"><img alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> <a href="https://www.facebook.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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