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- <div class="col-xs-8">
- <div class="page-header">
- <h1>CCOs with Last.fm </h1>
- </div>
- <p>Most reccomender examples utilize the MovieLense dataset, but that
relies only on ratings (which makes the recommender being demonstrated look
less trivial). Right next to the MovieLense dataset is the LastFM data set.
The LastFM dataset has ratings by user, friends of the user, bands listened to
by user, and tags by user. This is the kind of exciting data set weâd like
to work with!</p>
+ <div class="container mt-5 pb-4">
+
+ <div class="row">
+
+ <div class="col-lg-8">
+ <p>Most reccomender examples utilize the MovieLense dataset, but that
relies only on ratings (which makes the recommender being demonstrated look
less trivial). Right next to the MovieLense dataset is the LastFM data set.
The LastFM dataset has ratings by user, friends of the user, bands listened to
by user, and tags by user. This is the kind of exciting data set weâd like
to work with!</p>
<p>Start by downloading the LastFM dataset from
http://files.grouplens.org/datasets/hetrec2011/hetrec2011-lastfm-2k.zip</p>
<p>Iâm going to assume youâve unzipped them to /path/to/lastfm/*
-Weâre going to use a new trick for creating our IndexedDataSets, the
<code>apply</code> function. <code>apply</code> takes an <code>RDD[(String,
String)]</code> that is an RDD of tuples where both elements are strings. We
load RDDs, and use Spark to manipulate the RDDs into this form. The files from
LastFM are tab seperated- but it should be noted, that this could easily be
done from log files, but would just take a touch more Spark-Fu.</p>
+Weâre going to use a new trick for creating our IndexedDataSets, the <code
class="highlighter-rouge">apply</code> function. <code
class="highlighter-rouge">apply</code> takes an <code
class="highlighter-rouge">RDD[(String, String)]</code> that is an RDD of tuples
where both elements are strings. We load RDDs, and use Spark to manipulate the
RDDs into this form. The files from LastFM are tab seperated- but it should be
noted, that this could easily be done from log files, but would just take a
touch more Spark-Fu.</p>
<p>The second important thing to note is that the first element in each tuple
is going to be the rows in the resulting matrix, the second element will be the
column, and at that position there will be a one. The BiDictionary will
automatically be created from the strings.
For those following along at home- <a href="cco-lastfm.scala">the full Scala
worksheet</a> might be easier than copying and pasting
from this page.</p>
-<pre><code>import
org.apache.mahout.sparkbindings.indexeddataset.IndexedDatasetSpark
-
-val userTagsRDD = sc.textFile("/path/to/lastfm/user_taggedartists.dat")
-.map(line => line.split("\t"))
-.map(a => (a(0), a(2)))
-.filter(_._1 != "userID")
-val userTagsIDS = IndexedDatasetSpark.apply(userTagsRDD)(sc)
-
-val userArtistsRDD = sc.textFile("/path/to/lastfm/user_artists.dat")
-.map(line => line.split("\t"))
-.map(a => (a(0), a(1)))
-.filter(_._1 != "userID")
-val userArtistsIDS = IndexedDatasetSpark.apply(userArtistsRDD)(sc)
-
-val userFriendsRDD = sc.textFile("/path/to/lastfm/user_friends.dat")
-.map(line => line.split("\t"))
-.map(a => (a(0), a(1)))
-.filter(_._1 != "userID")
-val userFriendsIDS = IndexedDatasetSpark.apply(userFriendsRDD)(sc)
+<div class="language-scala highlighter-rouge"><pre
class="highlight"><code><span class="k">import</span> <span
class="nn">org.apache.mahout.sparkbindings.indexeddataset.IndexedDatasetSpark</span>
+
+<span class="k">val</span> <span class="n">userTagsRDD</span> <span
class="k">=</span> <span class="n">sc</span><span class="o">.</span><span
class="n">textFile</span><span class="o">(</span><span
class="s">"/path/to/lastfm/user_taggedartists.dat"</span><span
class="o">)</span>
+<span class="o">.</span><span class="n">map</span><span
class="o">(</span><span class="n">line</span> <span class="k">=></span>
<span class="n">line</span><span class="o">.</span><span
class="n">split</span><span class="o">(</span><span class="s">"\t"</span><span
class="o">))</span>
+<span class="o">.</span><span class="n">map</span><span
class="o">(</span><span class="n">a</span> <span class="k">=></span> <span
class="o">(</span><span class="n">a</span><span class="o">(</span><span
class="mi">0</span><span class="o">),</span> <span class="n">a</span><span
class="o">(</span><span class="mi">2</span><span class="o">)))</span>
+<span class="o">.</span><span class="n">filter</span><span
class="o">(</span><span class="k">_</span><span class="o">.</span><span
class="n">_1</span> <span class="o">!=</span> <span
class="s">"userID"</span><span class="o">)</span>
+<span class="k">val</span> <span class="n">userTagsIDS</span> <span
class="k">=</span> <span class="nc">IndexedDatasetSpark</span><span
class="o">.</span><span class="n">apply</span><span class="o">(</span><span
class="n">userTagsRDD</span><span class="o">)(</span><span
class="n">sc</span><span class="o">)</span>
+
+<span class="k">val</span> <span class="n">userArtistsRDD</span> <span
class="k">=</span> <span class="n">sc</span><span class="o">.</span><span
class="n">textFile</span><span class="o">(</span><span
class="s">"/path/to/lastfm/user_artists.dat"</span><span class="o">)</span>
+
<span class="o">.</span><span class="n">map</span><span
class="o">(</span><span class="n">line</span> <span class="k">=></span>
<span class="n">line</span><span class="o">.</span><span
class="n">split</span><span class="o">(</span><span class="s">"\t"</span><span
class="o">))</span>
+
<span class="o">.</span><span class="n">map</span><span
class="o">(</span><span class="n">a</span> <span class="k">=></span> <span
class="o">(</span><span class="n">a</span><span class="o">(</span><span
class="mi">0</span><span class="o">),</span> <span class="n">a</span><span
class="o">(</span><span class="mi">1</span><span class="o">)))</span>
+
<span class="o">.</span><span class="n">filter</span><span
class="o">(</span><span class="k">_</span><span class="o">.</span><span
class="n">_1</span> <span class="o">!=</span> <span
class="s">"userID"</span><span class="o">)</span>
+
+<span class="k">val</span> <span class="n">userArtistsIDS</span> <span
class="k">=</span> <span class="nc">IndexedDatasetSpark</span><span
class="o">.</span><span class="n">apply</span><span class="o">(</span><span
class="n">userArtistsRDD</span><span class="o">)(</span><span
class="n">sc</span><span class="o">)</span>
+
+<span class="k">val</span> <span class="n">userFriendsRDD</span> <span
class="k">=</span> <span class="n">sc</span><span class="o">.</span><span
class="n">textFile</span><span class="o">(</span><span
class="s">"/path/to/lastfm/user_friends.dat"</span><span class="o">)</span>
+ <span class="o">.</span><span
class="n">map</span><span class="o">(</span><span class="n">line</span> <span
class="k">=></span> <span class="n">line</span><span class="o">.</span><span
class="n">split</span><span class="o">(</span><span class="s">"\t"</span><span
class="o">))</span>
+ <span class="o">.</span><span
class="n">map</span><span class="o">(</span><span class="n">a</span> <span
class="k">=></span> <span class="o">(</span><span class="n">a</span><span
class="o">(</span><span class="mi">0</span><span class="o">),</span> <span
class="n">a</span><span class="o">(</span><span class="mi">1</span><span
class="o">)))</span>
+ <span class="o">.</span><span
class="n">filter</span><span class="o">(</span><span class="k">_</span><span
class="o">.</span><span class="n">_1</span> <span class="o">!=</span> <span
class="s">"userID"</span><span class="o">)</span>
+
+<span class="k">val</span> <span class="n">userFriendsIDS</span> <span
class="k">=</span> <span class="nc">IndexedDatasetSpark</span><span
class="o">.</span><span class="n">apply</span><span class="o">(</span><span
class="n">userFriendsRDD</span><span class="o">)(</span><span
class="n">sc</span><span class="o">)</span>
</code></pre>
+</div>
<p>How much easier was that?! In each RDD creations we:</p>
<p>Load our data using sc.textFile</p>
-<pre><code>sc.textFile("/path/to/lastfm/user_taggedartists.dat")
+<div class="highlighter-rouge"><pre
class="highlight"><code>sc.textFile("/path/to/lastfm/user_taggedartists.dat")
</code></pre>
+</div>
<p>Split the data into an array based on tabs (\t)</p>
-<pre><code>.map(line => line.split("\t"))
+<div class="highlighter-rouge"><pre class="highlight"><code>.map(line =>
line.split("\t"))
</code></pre>
+</div>
<p>Pull the userID column into the first position of the tuple, and the other
attribute we want into the second position.</p>
-<pre><code>.map(a => (a(0), a(1)))
+<div class="highlighter-rouge"><pre class="highlight"><code>.map(a =>
(a(0), a(1)))
</code></pre>
+</div>
<p>Remove the header (the only line that will have âuserIDâ in that
position)</p>
-<pre><code>.filter(_._1 != "userID")
+<div class="highlighter-rouge"><pre class="highlight"><code>.filter(_._1 !=
"userID")
</code></pre>
+</div>
-<p>Then we easily create an IndexedDataSet using the <code>apply</code>
method.
+<p>Then we easily create an IndexedDataSet using the <code
class="highlighter-rouge">apply</code> method.
val userTagsIDS = IndexedDatasetSpark.apply(userTagsRDD)(sc)
-Note the <code>(sc)</code> at the end. You may or may not need that.
<code>sc</code> is the SparkContext and should be passed as an implicit
parameter, however the REPL environment (e.g. Mahout Shell or notebooks) has a
hard time with the implicits, so I had to pass it explicitly.</p>
+Note the <code class="highlighter-rouge">(sc)</code> at the end. You may or
may not need that. <code class="highlighter-rouge">sc</code> is the
SparkContext and should be passed as an implicit parameter, however the REPL
environment (e.g. Mahout Shell or notebooks) has a hard time with the
implicits, so I had to pass it explicitly.</p>
<p>Now we compute our co-occurrence matrices:</p>
-<pre><code class="language-scala">import
org.apache.mahout.math.cf.SimilarityAnalysis
+<div class="language-scala highlighter-rouge"><pre
class="highlight"><code><span class="k">import</span> <span
class="nn">org.apache.mahout.math.cf.SimilarityAnalysis</span>
-val artistReccosLlrDrmListByArtist = SimilarityAnalysis.cooccurrencesIDSs(
-Array(userArtistsIDS, userTagsIDS, userFriendsIDS),
-maxInterestingItemsPerThing = 20,
-maxNumInteractions = 500,
-randomSeed = 1234)
+<span class="k">val</span> <span
class="n">artistReccosLlrDrmListByArtist</span> <span class="k">=</span> <span
class="nc">SimilarityAnalysis</span><span class="o">.</span><span
class="n">cooccurrencesIDSs</span><span class="o">(</span>
+ <span class="nc">Array</span><span
class="o">(</span><span class="n">userArtistsIDS</span><span class="o">,</span>
<span class="n">userTagsIDS</span><span class="o">,</span> <span
class="n">userFriendsIDS</span><span class="o">),</span>
+ <span
class="n">maxInterestingItemsPerThing</span> <span class="k">=</span> <span
class="mi">20</span><span class="o">,</span>
+ <span
class="n">maxNumInteractions</span> <span class="k">=</span> <span
class="mi">500</span><span class="o">,</span>
+ <span
class="n">randomSeed</span> <span class="k">=</span> <span
class="mi">1234</span><span class="o">)</span>
</code></pre>
+</div>
<p>Letâs see an example of how this would work-</p>
@@ -352,52 +246,54 @@ randomSeed = 1234)
<p>First I load, and create incore maps from the mapping files:</p>
-<pre><code class="language-scala">val artistMap =
sc.textFile("/path/to/lastfm/artists.dat")
- .map(line => line.split("\t"))
- .map(a => (a(1), a(0)))
- .filter(_._1 != "name")
- .collect
- .toMap
+<div class="language-scala highlighter-rouge"><pre
class="highlight"><code><span class="k">val</span> <span
class="n">artistMap</span> <span class="k">=</span> <span
class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span
class="o">(</span><span class="s">"/path/to/lastfm/artists.dat"</span><span
class="o">)</span>
+ <span class="o">.</span><span class="n">map</span><span
class="o">(</span><span class="n">line</span> <span class="k">=></span>
<span class="n">line</span><span class="o">.</span><span
class="n">split</span><span class="o">(</span><span class="s">"\t"</span><span
class="o">))</span>
+ <span class="o">.</span><span class="n">map</span><span
class="o">(</span><span class="n">a</span> <span class="k">=></span> <span
class="o">(</span><span class="n">a</span><span class="o">(</span><span
class="mi">1</span><span class="o">),</span> <span class="n">a</span><span
class="o">(</span><span class="mi">0</span><span class="o">)))</span>
+ <span class="o">.</span><span class="n">filter</span><span
class="o">(</span><span class="k">_</span><span class="o">.</span><span
class="n">_1</span> <span class="o">!=</span> <span
class="s">"name"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">collect</span>
+ <span class="o">.</span><span class="n">toMap</span>
-val tagsMap = sc.textFile("/path/tolastfm/tags.dat")
- .map(line => line.split("\t"))
- .map(a => (a(1), a(0)))
- .filter(_._1 != "tagValue")
- .collect
- .toMap
+<span class="k">val</span> <span class="n">tagsMap</span> <span
class="k">=</span> <span class="n">sc</span><span class="o">.</span><span
class="n">textFile</span><span class="o">(</span><span
class="s">"/path/tolastfm/tags.dat"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">map</span><span
class="o">(</span><span class="n">line</span> <span class="k">=></span>
<span class="n">line</span><span class="o">.</span><span
class="n">split</span><span class="o">(</span><span class="s">"\t"</span><span
class="o">))</span>
+ <span class="o">.</span><span class="n">map</span><span
class="o">(</span><span class="n">a</span> <span class="k">=></span> <span
class="o">(</span><span class="n">a</span><span class="o">(</span><span
class="mi">1</span><span class="o">),</span> <span class="n">a</span><span
class="o">(</span><span class="mi">0</span><span class="o">)))</span>
+ <span class="o">.</span><span class="n">filter</span><span
class="o">(</span><span class="k">_</span><span class="o">.</span><span
class="n">_1</span> <span class="o">!=</span> <span
class="s">"tagValue"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">collect</span>
+ <span class="o">.</span><span class="n">toMap</span>
</code></pre>
+</div>
-<p>This will create some <code>Map</code>s that I can use to type readable
names for the artist and tags to create my âhistoryâ.</p>
-
-<pre><code class="language-scala">val kilroyUserArtists = svec(
(userArtistsIDS.columnIDs.get(artistMap("Beck")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("David Bowie")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Gary Numan")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Less Than Jake")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Lou Reed")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Parliament")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Radiohead")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Seu Jorge")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("The Skatalites")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Reverend Horton Heat")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Talking Heads")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Tom Waits")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Waylon Jennings")).get, 1) ::
- (userArtistsIDS.columnIDs.get(artistMap("Wu-Tang Clan")).get, 1) :: Nil,
- cardinality = userArtistsIDS.columnIDs.size
-)
-
-
-
-val kilroyUserTags = svec(
- (userTagsIDS.columnIDs.get(tagsMap("classical")).get, 1) ::
- (userTagsIDS.columnIDs.get(tagsMap("skacore")).get, 1) ::
- (userTagsIDS.columnIDs.get(tagsMap("why on earth is this just a bonus
track")).get, 1) ::
- (userTagsIDS.columnIDs.get(tagsMap("punk rock")).get, 1) :: Nil,
- cardinality = userTagsIDS.columnIDs.size)
+<p>This will create some <code class="highlighter-rouge">Map</code>s that I
can use to type readable names for the artist and tags to create my
âhistoryâ.</p>
+
+<div class="language-scala highlighter-rouge"><pre
class="highlight"><code><span class="k">val</span> <span
class="n">kilroyUserArtists</span> <span class="k">=</span> <span
class="n">svec</span><span class="o">(</span> <span class="o">(</span><span
class="n">userArtistsIDS</span><span class="o">.</span><span
class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span
class="s">"Beck"</span><span class="o">)).</span><span
class="n">get</span><span class="o">,</span> <span class="mi">1</span><span
class="o">)</span> <span class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"David
Bowie"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Gary
Numan"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Less Than
Jake"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Lou
Reed"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span
class="s">"Parliament"</span><span class="o">)).</span><span
class="n">get</span><span class="o">,</span> <span class="mi">1</span><span
class="o">)</span> <span class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span
class="s">"Radiohead"</span><span class="o">)).</span><span
class="n">get</span><span class="o">,</span> <span class="mi">1</span><span
class="o">)</span> <span class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Seu
Jorge"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"The
Skatalites"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Reverend
Horton Heat"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Talking
Heads"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Tom
Waits"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Waylon
Jennings"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userArtistsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span
class="n">artistMap</span><span class="o">(</span><span class="s">"Wu-Tang
Clan"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span> <span class="nc">Nil</span><span class="o">,</span>
+ <span class="n">cardinality</span> <span class="k">=</span> <span
class="n">userArtistsIDS</span><span class="o">.</span><span
class="n">columnIDs</span><span class="o">.</span><span class="n">size</span>
+<span class="o">)</span>
+
+
+
+<span class="k">val</span> <span class="n">kilroyUserTags</span> <span
class="k">=</span> <span class="n">svec</span><span class="o">(</span>
+ <span class="o">(</span><span class="n">userTagsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span class="n">tagsMap</span><span
class="o">(</span><span class="s">"classical"</span><span
class="o">)).</span><span class="n">get</span><span class="o">,</span> <span
class="mi">1</span><span class="o">)</span> <span class="o">::</span>
+ <span class="o">(</span><span class="n">userTagsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span class="n">tagsMap</span><span
class="o">(</span><span class="s">"skacore"</span><span
class="o">)).</span><span class="n">get</span><span class="o">,</span> <span
class="mi">1</span><span class="o">)</span> <span class="o">::</span>
+ <span class="o">(</span><span class="n">userTagsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span class="n">tagsMap</span><span
class="o">(</span><span class="s">"why on earth is this just a bonus
track"</span><span class="o">)).</span><span class="n">get</span><span
class="o">,</span> <span class="mi">1</span><span class="o">)</span> <span
class="o">::</span>
+ <span class="o">(</span><span class="n">userTagsIDS</span><span
class="o">.</span><span class="n">columnIDs</span><span class="o">.</span><span
class="n">get</span><span class="o">(</span><span class="n">tagsMap</span><span
class="o">(</span><span class="s">"punk rock"</span><span
class="o">)).</span><span class="n">get</span><span class="o">,</span> <span
class="mi">1</span><span class="o">)</span> <span class="o">::</span> <span
class="nc">Nil</span><span class="o">,</span>
+ <span class="n">cardinality</span> <span class="k">=</span> <span
class="n">userTagsIDS</span><span class="o">.</span><span
class="n">columnIDs</span><span class="o">.</span><span
class="n">size</span><span class="o">)</span>
</code></pre>
+</div>
-<p>So what we have then is me typing in a name to <code>artistMap</code> where
the keys are human readable names of my favorite bands, which returns the value
which is the LastFM ID, which in turn is the key in the BiDictionary map, and
returns the matrix position. Iâm making a sparse vector where I want the
index at the value I just fetched (which in an awry way refers to the artist I
specified) to have the value 1.</p>
+<p>So what we have then is me typing in a name to <code
class="highlighter-rouge">artistMap</code> where the keys are human readable
names of my favorite bands, which returns the value which is the LastFM ID,
which in turn is the key in the BiDictionary map, and returns the matrix
position. Iâm making a sparse vector where I want the index at the value I
just fetched (which in an awry way refers to the artist I specified) to have
the value 1.</p>
<p>Same idea for the tags.</p>
@@ -405,48 +301,42 @@ val kilroyUserTags = svec(
<p>val kilroysRecs = (artistReccosLlrDrmListByArtist(0).matrix %<em>%
kilroyUserArtists + artistReccosLlrDrmListByArtist(1).matrix %</em>%
kilroyUserTags).collect
Finally letâs sort that vector out and get some user ids and strengths.</p>
-<pre><code class="language-scala">import
org.apache.mahout.math.scalabindings.MahoutCollections._
-import collection._
-import JavaConversions._
+<div class="language-scala highlighter-rouge"><pre
class="highlight"><code><span class="k">import</span> <span
class="nn">org.apache.mahout.math.scalabindings.MahoutCollections._</span>
+<span class="k">import</span> <span class="nn">collection._</span>
+<span class="k">import</span> <span class="nn">JavaConversions._</span>
-// Which Users I should Be Friends with.
-println(kilroysRecs(::, 0).toMap.toList.sortWith(_._2 > _._2).take(5))
+<span class="c1">// Which Users I should Be Friends with.
+</span><span class="n">println</span><span class="o">(</span><span
class="n">kilroysRecs</span><span class="o">(::,</span> <span
class="mi">0</span><span class="o">).</span><span class="n">toMap</span><span
class="o">.</span><span class="n">toList</span><span class="o">.</span><span
class="n">sortWith</span><span class="o">(</span><span class="k">_</span><span
class="o">.</span><span class="n">_2</span> <span class="o">></span> <span
class="k">_</span><span class="o">.</span><span class="n">_2</span><span
class="o">).</span><span class="n">take</span><span class="o">(</span><span
class="mi">5</span><span class="o">))</span>
</code></pre>
+</div>
-<p><code>kilroysRecs</code> is actually a one column matrix, so we take that,
and the convert it into something we can sort. We then take the top 5
suggestions. Keep in mind, this will return the Mahout user ID, which you
would also have to reverse map back to the lastFM userID. The lastFM userID is
just another Integer, and not particularly exciting so I left that out.</p>
+<p><code class="highlighter-rouge">kilroysRecs</code> is actually a one column
matrix, so we take that, and the convert it into something we can sort. We then
take the top 5 suggestions. Keep in mind, this will return the Mahout user ID,
which you would also have to reverse map back to the lastFM userID. The lastFM
userID is just another Integer, and not particularly exciting so I left that
out.</p>
<p>If you wanted to recommend artists like a normal recommendation engine- you
would change the first position in all of the input matrices to be
âartistIDâ. This is left as an exercise to the user.</p>
<p><a href="cco-lastfm.scala">Full Scala Worksheet</a></p>
</div>
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