Repository: incubator-predictionio-site Updated Branches: refs/heads/asf-site 0f1e2f89e -> 1b72b3036
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href="https://www.apache.org/";><span>Apache Homepage</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/licenses/";><span>License</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/sponsorship.html";><span>Sponsorship</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/thanks.html";><span>Thanks</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/security/";><span>Security</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">PredictionIO Official Templates</a><span class="spacer">></span></li><li><a href="# ">E-Commerce Recommendation</a><span class="spacer">></span></li><li><span class="last">Adjust Score</span></li></ul></div><div id="page-title"><h1>Adjust Score (E-Commerce Recommendation)</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#modification">Modification</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/ecommercerecommendation/adjust-score.html.md.erb";><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="#">E-Commerce Recommendation</a><span class="spacer">></span></li><li><span class="last">Adjust Score</span></li></ul></div><div id="page-title"><h1>Adjust Score (E-Commerce Re commendation)</h1></div></div><div class="content"> <p>This examples demonstrates how to modify E-Commerce Recommendation template to further adjust score.</p><p>By default, items have a weight of 1.0. Giving an item a weight greater than 1.0 will make them appear more often and can be useful for i.e. promoted products. An item can also be given a weight smaller than 1.0 (but bigger than 0), in which case it will be recommended less often than originally. Weight values smaller than 0.0 are invalid.</p><p>You can find the complete modified source code <a href="https://github.com/apache/incubator-predictionio/tree/develop/examples/scala-parallel-ecommercerecommendation/adjust-score";>here</a>.</p><h2 id='modification' class='header-anchors'>Modification</h2><h3 id='ecommalgorithm.scala' class='header-anchors'>ECommAlgorithm.scala</h3><p>Add a case class to represent each group items which are given the same weight.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody ><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3 +4 +5</pre></td><td class="code"><pre><span class="c1">// ADDED +</span><span class="k">case</span> <span class="k">class</span> <span class="nc">WeightGroup</span><span class="o">(</span> + <span class="n">items</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="n">weight</span><span class="k">:</span> <span class="kt">Double</span> +<span class="o">)</span> +</pre></td></tr></tbody></table> </div> <p>In ECommAlgorithm, add <code>weightedItems</code> function to extract the sequence of <code>WeightGroup</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 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +16 +17 +18 +19 +20 +21 +22 +23 +24 +25 +26 +27 +28</pre></td><td class="code"><pre> <span class="c1">// ADDED +</span> <span class="cm">/** Get the latest constraint weightedItems */</span> + <span class="k">def</span> <span class="n">weightedItems</span><span class="k">:</span> <span class="kt">Seq</span><span class="o">[</span><span class="kt">WeightGroup</span><span class="o">]</span> <span class="k">=</span> <span class="o">{</span> + <span class="k">try</span> <span class="o">{</span> + <span class="k">val</span> <span class="n">constr</span> <span class="k">=</span> <span class="nc">LEventStore</span><span class="o">.</span><span class="n">findByEntity</span><span class="o">(</span> + <span class="n">appName</span> <span class="k">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">appName</span><span class="o">,</span> + <span class="n">entityType</span> <span class="k">=</span> <span class="s">"constraint"</span><span class="o">,</span> + <span class="n">entityId</span> <span class="k">=</span> <span class="s">"weightedItems"</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">Seq</span><span class="o">(</span><span class="s">"$set"</span><span class="o">)),</span> + <span class="n">limit</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="mi">1</span><span class="o">),</span> + <span class="n">latest</span> <span class="k">=</span> <span class="kc">true</span><span class="o">,</span> + <span class="n">timeout</span> <span class="k">=</span> <span class="nc">Duration</span><span class="o">(</span><span class="mi">200</span><span class="o">,</span> <span class="s">"millis"</span><span class="o">)</span> + <span class="o">)</span> + <span class="k">if</span> <span class="o">(</span><span class="n">constr</span><span class="o">.</span><span class="n">hasNext</span><span class="o">)</span> <span class="o">{</span> + <span class="n">constr</span><span class="o">.</span><span class="n">next</span><span class="o">.</span><span class="n">properties</span><span class="o">.</span><span class="n">get</span><span class="o">[</span><span class="kt">Seq</span><span class="o">[</span><span class="kt">WeightGroup</span><span class="o">]](</span><span class="s">"weights"</span><span class="o">)</span> + <span class="o">}</span> <span class="k">else</span> <span class="o">{</span> + <span class="nc">Nil</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">scala.concurrent.TimeoutException</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">"Timeout when read set weightedItems event."</span> <span class="o">+</span> + <span class="n">s</span><span class="s">" Empty list is used. ${e}"</span><span class="o">)</span> + <span class="nc">Nil</span> + <span class="k">case</span> <span class="n">e</span><span class="k">:</span> <span class="kt">Exception</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">"Error when read set weightedItems event: ${e}"</span><span class="o">)</span> + <span class="k">throw</span> <span class="n">e</span> + <span class="o">}</span> + <span class="o">}</span> +</pre></td></tr></tbody></table> </div> <p>Modify the <code>predictKnownUser()</code>, <code>predictDefault()</code> and <code>predictSimilar()</code>:</p> <ul> <li>add the <code>weights: Map[Int, Double]</code> parameter</li> <li>adjust score according to item weights</li> </ul> <div class="highlight scala"><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 +16 +17 +18 +19 +20 +21</pre></td><td class="code"><pre> <span class="k">def</span> <span class="n">predictKnownUser</span><span class="o">(</span> + <span class="n">userFeature</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">productModels</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">Int</span>, <span class="kt">ProductModel</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="n">whiteList</span><span class="k">:</span> <span class="kt">Option</span><span class="o">[</span><span class="kt">Set</span><span class="o">[</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">Int</span><span class="o">],</span> + <span class="n">weights</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">Int</span>, <span class="kt">Double</span><span class="o">]</span> <span class="c1">// ADDED +</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="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">i</span><span class="o">,</span> <span class="n">pm</span><span class="o">)</span> <span class="k">=></span> + <span class="c1">// NOTE: features must be defined, so can call .get +</span> <span class="k">val</span> <span class="n">s</span> <span class="k">=</span> <span class="n">dotProduct</span><span class="o">(</span><span class="n">userFeature</span><span class="o">,</span> <span class="n">pm</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="o">)</span> + <span class="c1">// may customize here to further adjust score +</span> <span class="c1">// ADDED +</span> <span class="k">val</span> <span class="n">adjustedScore</span> <span class="k">=</span> <span class="n">s</span> <span class="o">*</span> <span class="n">weights</span><span class="o">(</span><span class="n">i</span><span class="o">)</span> + <span class="o">(</span><span class="n">i</span><span class="o">,</span> <span class="n">adjustedScore</span><span class="o">)</span> + <span class="o">}</span> + <span class="o">...</span> + + <span class="o">}</span> +</pre></td></tr></tbody></table> </div> <p>Lastly, modify the <code>predict()</code> method. The sequence of <code>WeightGroup</code> transforms into a <code>Map[Int, Double]</code> that we can easily query to extract the weight given to an item, using its <code>Int</code> index.</p><div class="highlight scala"><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 +16 +17 +18 +19 +20 +21 +22 +23 +24 +25 +26 +27 +28 +29 +30 +31 +32 +33 +34 +35 +36 +37 +38 +39 +40 +41 +42 +43 +44 +45 +46 +47 +48 +49 +50 +51</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">ECommModel</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="o">...</span> + + <span class="c1">// ADDED +</span> <span class="k">val</span> <span class="n">weights</span><span class="k">:</span> <span class="kt">Map</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">for</span> <span class="o">{</span> + <span class="n">group</span> <span class="k"><-</span> <span class="n">weightedItems</span> + <span class="n">item</span> <span class="k"><-</span> <span class="n">group</span><span class="o">.</span><span class="n">items</span> + <span class="n">index</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">get</span><span class="o">(</span><span class="n">item</span><span class="o">)</span> + <span class="o">}</span> <span class="k">yield</span> <span class="o">(</span><span class="n">index</span><span class="o">,</span> <span class="n">group</span><span class="o">.</span><span class="n">weight</span><span class="o">))</span> + <span class="o">.</span><span class="n">toMap</span> + <span class="o">.</span><span class="n">withDefaultValue</span><span class="o">(</span><span class="mf">1.0</span><span class="o">)</span> + + <span class="o">...</span> + + <span class="k">val</span> <span class="n">topScores</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="k">if</span> <span class="o">(</span><span class="n">userFeature</span><span class="o">.</span><span class="n">isDefined</span><span class="o">)</span> <span class="o">{</span> + <span class="c1">// the user has feature vector +</span> <span class="n">predictKnownUser</span><span class="o">(</span> + <span class="n">userFeature</span> <span class="k">=</span> <span class="n">userFeature</span><span class="o">.</span><span class="n">get</span><span class="o">,</span> + <span class="n">productModels</span> <span class="k">=</span> <span class="n">productModels</span><span class="o">,</span> + <span class="n">query</span> <span class="k">=</span> <span class="n">query</span><span class="o">,</span> + <span class="n">whiteList</span> <span class="k">=</span> <span class="n">whiteList</span><span class="o">,</span> + <span class="n">blackList</span> <span class="k">=</span> <span class="n">finalBlackList</span><span class="o">,</span> + <span class="n">weights</span> <span class="k">=</span> <span class="n">weights</span> <span class="c1">// ADDED +</span> <span class="o">)</span> + <span class="o">}</span> <span class="k">else</span> <span class="o">{</span> + <span class="o">...</span> + + <span class="k">if</span> <span class="o">(</span><span class="n">recentFeatures</span><span class="o">.</span><span class="n">isEmpty</span><span class="o">)</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 features vector for recent items ${recentItems}."</span><span class="o">)</span> + <span class="n">predictDefault</span><span class="o">(</span> + <span class="n">productModels</span> <span class="k">=</span> <span class="n">productModels</span><span class="o">,</span> + <span class="n">query</span> <span class="k">=</span> <span class="n">query</span><span class="o">,</span> + <span class="n">whiteList</span> <span class="k">=</span> <span class="n">whiteList</span><span class="o">,</span> + <span class="n">blackList</span> <span class="k">=</span> <span class="n">finalBlackList</span><span class="o">,</span> + <span class="n">weights</span> <span class="k">=</span> <span class="n">weights</span> <span class="c1">// ADDED +</span> <span class="o">)</span> + <span class="o">}</span> <span class="k">else</span> <span class="o">{</span> + <span class="n">predictSimilar</span><span class="o">(</span> + <span class="n">recentFeatures</span> <span class="k">=</span> <span class="n">recentFeatures</span><span class="o">,</span> + <span class="n">productModels</span> <span class="k">=</span> <span class="n">productModels</span><span class="o">,</span> + <span class="n">query</span> <span class="k">=</span> <span class="n">query</span><span class="o">,</span> + <span class="n">whiteList</span> <span class="k">=</span> <span class="n">whiteList</span><span class="o">,</span> + <span class="n">blackList</span> <span class="k">=</span> <span class="n">finalBlackList</span><span class="o">,</span> + <span class="n">weights</span> <span class="k">=</span> <span class="n">weights</span> <span class="c1">// ADDED +</span> <span class="o">)</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, to send an event to Event Server:</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 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +16 +17 +18 +19 +20</pre></td><td class="code"><pre><span class="gp">$ </span>curl -i -X POST http://localhost:7070/events.json?accessKey<span class="o">=</span><span class="nv">$ACCESS_KEY</span> <span class="se">\</span> +-H <span class="s2">"Content-Type: application/json"</span> <span class="se">\</span> +-d <span class="s1">'{ + "event" : "$set", + "entityType" : "constraint", + "entityId" : "weightedItems", + "properties" : { + "weights": [ + { + "items": ["i4", "i14"], + "weight": 1.2 + }, + { + "items": ["i11"], + "weight": 1.5 + } + ] + }, + "eventTime" : "2014-11-02T09:39:45.618-08:00" +}'</span> +</pre></td></tr></tbody></table> </div> <p>That's it! Now your engine can predict with adjusted scores.</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.incubator.apache.org/install/" target="blank">Download</a></li><li><a href="//predictionio.incubator.apache.org/" target="blank">Docs</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">GitHub</a></li><li><a href="mailto:[email protected]"; 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.incubator.apache.org/community/contribute-code/" target="bl ank">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:[email protected]"; 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 class="row"><div class="col-md-12 footer-link-column"><a class="pull-right" href="http://incubator.apache.org/projects/predictionio.html";><img alt="Apache Incubator" src="/images/logos/apache_incubator-6954bd16.png"/></a><span >Apache PredictionIO is an effort undergoing incubation at The Apache Software >Foundation (ASF), sponsored by the Apache Incubator. 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