mahout-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
Subject svn commit: r924457 - in /websites/staging/mahout/trunk/content: ./ users/recommender/intro-cooccurrence-spark.html
Date Thu, 02 Oct 2014 21:27:35 GMT
Author: buildbot
Date: Thu Oct  2 21:27:35 2014
New Revision: 924457

Staging update by buildbot for mahout

    websites/staging/mahout/trunk/content/   (props changed)

Propchange: websites/staging/mahout/trunk/content/
--- cms:source-revision (original)
+++ cms:source-revision Thu Oct  2 21:27:35 2014
@@ -1 +1 @@

Modified: websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
--- websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
+++ websites/staging/mahout/trunk/content/users/recommender/intro-cooccurrence-spark.html
Thu Oct  2 21:27:35 2014
@@ -348,7 +348,7 @@ to recommend.   </p>
-<h3 id="wzxhzdk17how-to-use-multiple-user-actionswzxhzdk18"><a name="multiple-actions">How
To Use Multiple User Actions</a></h3>
+<h3 id="wzxhzdk18how-to-use-multiple-user-actionswzxhzdk19"><a name="multiple-actions">How
To Use Multiple User Actions</a></h3>
 <p>Often we record various actions the user takes for later analytics. These can now
be used to make recommendations. 
 The idea of a recommender is to recommend the action you want the user to make. For an ecom
app this might be 
 a purchase action. It is usually not a good idea to just treat other actions the same as
the action you want to recommend. 
@@ -531,7 +531,7 @@ a blog post,
 the only similarity method supported this is not the optimal way to determine general "bag-of-words"
document similarity. 
 LLR is used more as a quality filter than as a similarity measure. However <em>spark-rowsimilarity</em>
will produce 
 lists of similar docs for every doc if input is docs with lists of terms. The Apache <a
href="">Lucene</a> project provides several methods of <a
and tokenizing</a> documents.</p>
-<h1 id="wzxhzdk234-creating-a-unified-recommenderwzxhzdk24"><a name="unified-recommender">4.
Creating a Unified Recommender</a></h1>
+<h1 id="wzxhzdk244-creating-a-unified-recommenderwzxhzdk25"><a name="unified-recommender">4.
Creating a Unified Recommender</a></h1>
 <p>Using the output of <em>spark-itemsimilarity</em> and <em>spark-rowsimilarity</em>
you can build a unified cooccurrence and content based
  recommender that can be used in both or either mode depending on indicators available and
the history available at 
 runtime for a user.</p>
@@ -631,10 +631,13 @@ For instance you may want to put persona
 by tagging items with some category of popularity (hot, warm, cold for instance) then
 index that as a new indicator field and include the corresponding value in a query 
 on the popularity field. If we use the ecom example but use the query to get "hot" recommendations
it might look like this:</p>
-      field: purchase; q:user's-purchase-history
-      field: view; q:user's view-history
-      field: popularity; q:"hot"</p>
+<div class="codehilite"><pre><span class="n">Query</span><span
+  <span class="n">field</span><span class="o">:</span> <span class="n">purchase</span><span
class="o">;</span> <span class="n">q</span><span class="o">:</span><span
class="n">user</span><span class="s1">&#39;s-purchase-history</span>
+<span class="s1">  field: view; q:user&#39;</span><span class="n">s</span>
<span class="n">view</span><span class="o">-</span><span class="n">history</span>
+  <span class="n">field</span><span class="o">:</span> <span class="n">popularity</span><span
class="o">;</span> <span class="n">q</span><span class="o">:</span><span
 <p>This will return recommendations favoring ones that have the intrinsic indicator
 <h2 id="notes">Notes</h2>

View raw message