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href="http://github.com/apache/mahout";>Github</a></li>--> + + - </div><!-- /.container-fluid --> + <!--</ul>--> + </div><!-- /.navbar-collapse --> + </div> </nav> -<body> + <div class="container mt-5 pb-4"> -<div id="wrap"> - <body class=""> + <div class="row"> - <div class="container"> - <p><a name="RecommenderDocumentation-Overview"></a></p> + <div class="col-lg-8"> + <p><a name="RecommenderDocumentation-Overview"></a></p> <h2 id="overview">Overview</h2> <p><em>This documentation concerns the non-distributed, non-Hadoop-based @@ -306,34 +266,39 @@ Weâll start with an example of this.</p> on data in a file. The file should be in CSV format, with lines of the form âuserID,itemID,prefValueâ (e.g. â39505,290002,3.5â):</p> -<pre><code>DataModel model = new FileDataModel(new File("data.txt")); +<div class="highlighter-rouge"><pre class="highlight"><code>DataModel model = new FileDataModel(new File("data.txt")); </code></pre> +</div> <p>Weâll use the <strong>PearsonCorrelationSimilarity</strong> implementation of <strong>UserSimilarity</strong> as our user correlation algorithm, and add an optional preference inference algorithm:</p> -<pre><code>UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model); +<div class="highlighter-rouge"><pre class="highlight"><code>UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model); </code></pre> +</div> <p>Now we create a <strong>UserNeighborhood</strong> algorithm. Here we use nearest-3:</p> -<pre><code>UserNeighborhood neighborhood = +<div class="highlighter-rouge"><pre class="highlight"><code>UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, userSimilarity, model);{code} </code></pre> +</div> <p>Now we can create our <strong>Recommender</strong>, and add a caching decorator:</p> -<pre><code>Recommender recommender = +<div class="highlighter-rouge"><pre class="highlight"><code>Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity); Recommender cachingRecommender = new CachingRecommender(recommender); </code></pre> +</div> <p>Now we can get 10 recommendations for user ID â1234â â done!</p> -<pre><code>List<RecommendedItem> recommendations = +<div class="highlighter-rouge"><pre class="highlight"><code>List<RecommendedItem> recommendations = cachingRecommender.recommend(1234, 10); </code></pre> +</div> <h2 id="item-based-recommender">Item-based Recommender</h2> @@ -348,8 +313,9 @@ are more appropriate.</p> <p>Letâs start over, again with a <strong>FileDataModel</strong> to start:</p> -<pre><code>DataModel model = new FileDataModel(new File("data.txt")); +<div class="highlighter-rouge"><pre class="highlight"><code>DataModel model = new FileDataModel(new File("data.txt")); </code></pre> +</div> <p>Weâll also need an <strong>ItemSimilarity</strong>. We could use <strong>PearsonCorrelationSimilarity</strong>, which computes item similarity in realtime, @@ -357,22 +323,24 @@ but, this is generally too slow to be useful. Instead, in a real application, you would feed a list of pre-computed correlations to a <strong>GenericItemSimilarity</strong>:</p> -<pre><code>// Construct the list of pre-computed correlations +<div class="highlighter-rouge"><pre class="highlight"><code>// Construct the list of pre-computed correlations Collection<GenericItemSimilarity.ItemItemSimilarity> correlations = ...; ItemSimilarity itemSimilarity = new GenericItemSimilarity(correlations); </code></pre> +</div> <p>Then we can finish as before to produce recommendations:</p> -<pre><code>Recommender recommender = +<div class="highlighter-rouge"><pre class="highlight"><code>Recommender recommender = new GenericItemBasedRecommender(model, itemSimilarity); Recommender cachingRecommender = new CachingRecommender(recommender); ... List<RecommendedItem> recommendations = cachingRecommender.recommend(1234, 10); </code></pre> +</div> <p><a name="RecommenderDocumentation-Integrationwithyourapplication"></a></p> <h2 id="integration-with-your-application">Integration with your application</h2> @@ -430,7 +398,7 @@ provides a good starting point.</p> <p>Fortunately, Mahout provides a way to evaluate the accuracy of your Recommender on your own data, in org.apache.mahout.cf.taste.eval</p> -<pre><code>DataModel myModel = ...; +<div class="highlighter-rouge"><pre class="highlight"><code>DataModel myModel = ...; RecommenderBuilder builder = new RecommenderBuilder() { public Recommender buildRecommender(DataModel model) { // build and return the Recommender to evaluate here @@ -440,6 +408,7 @@ RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); double evaluation = evaluator.evaluate(builder, myModel, 0.9, 1.0); </code></pre> +</div> <p>For âbooleanâ data model situations, where there are no notions of preference value, the above evaluation based on estimated preference does @@ -470,30 +439,26 @@ Cooperative Work (CSCW 1994), pp. 175-186, 1994.</p> Research and Development in Information Retrieval (SIGIR 99), pp. 230-237, 1999.</p> - </div> - + </div> -</div> -<div id="footer"> - <div class="container"> - <p>© 2017 Apache Mahout - with help from <a href="http://jekyllbootstrap.com"; target="_blank" title="The Definitive Jekyll Blogging Framework">Jekyll Bootstrap</a> - and <a href="http://getbootstrap.com"; target="_blank">Bootstrap</a> - </p> </div> -</div> - - +</div> + <footer class="footer bg-light"> + <div class="container text-center small"> + Copyright © 2014-2017 The Apache Software Foundation, Licensed under the Apache License, Version 2.0. + </div> +</footer> + <script src="/assets/vendor/jquery/jquery-slim.min.js"></script> + <script src="/assets/vendor/popper/popper.min.js"></script> + <script src="/assets/vendor/bootstrap/js/bootstrap.min.js"></script> + <script src="/assets/header.js"></script> + <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; type="text/javascript"></script> -<!-- Latest compiled and minified JavaScript, requires jQuery 1.x (2.x not supported in IE8) --> -<!-- Placed at the end of the document so the pages load faster --> -<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js";></script> -<script src="/assets/themes/mahout3/js/bootstrap.min.js"></script> </body> -</html> +</html>
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Some starting questions have been @@ -251,30 +211,26 @@ top a ReloadFromJDBCDataModel.</p> nature of the system is that new data comes in from the file or database only periodically â perhaps on the order of minutes.</p> - </div> - + </div> -</div> -<div id="footer"> - <div class="container"> - <p>© 2017 Apache Mahout - with help from <a href="http://jekyllbootstrap.com"; target="_blank" title="The Definitive Jekyll Blogging Framework">Jekyll Bootstrap</a> - and <a href="http://getbootstrap.com"; target="_blank">Bootstrap</a> - </p> </div> -</div> - - +</div> + <footer class="footer bg-light"> + <div class="container text-center small"> + Copyright © 2014-2017 The Apache Software Foundation, Licensed under the Apache License, Version 2.0. + </div> +</footer> + <script src="/assets/vendor/jquery/jquery-slim.min.js"></script> + <script src="/assets/vendor/popper/popper.min.js"></script> + <script src="/assets/vendor/bootstrap/js/bootstrap.min.js"></script> + <script 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this test multiple times, you will get different results, becau </RecommendedItem></pre> - </div> - + </div> -</div> -<div id="footer"> - <div class="container"> - <p>© 2017 Apache Mahout - with help from <a href="http://jekyllbootstrap.com"; target="_blank" title="The Definitive Jekyll Blogging Framework">Jekyll Bootstrap</a> - and <a href="http://getbootstrap.com"; target="_blank">Bootstrap</a> - </p> </div> -</div> - - +</div> + <footer class="footer bg-light"> + <div class="container text-center small"> + Copyright © 2014-2017 The Apache Software Foundation, Licensed under the Apache License, Version 2.0. + </div> +</footer> + <script src="/assets/vendor/jquery/jquery-slim.min.js"></script> + <script src="/assets/vendor/popper/popper.min.js"></script> + <script src="/assets/vendor/bootstrap/js/bootstrap.min.js"></script> + <script src="/assets/header.js"></script> + <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; 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