Author: buildbot
Date: Fri Aug 29 17:30:25 2014
New Revision: 920728
Log:
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+ <h1 id="intro-to-cooccurrence-recommenders-with-spark">Intro to
Cooccurrence Recommenders with Spark</h1>
+<p>Mahout provides several important building blocks for creating
recommendations using Spark. <em>spark-itemsimilarity</em> can be used to
create "other people also liked these things" type recommendations and paired
with a search engine can personalize recommendations for individual users.
<em>spark-rowsimilarity</em> can provide non-personalized content based
recommendations, using textual content for example.</p>
+<p>Below are the command line jobs but the drivers and associated code can
also be customized and accessed from the Scala APIs.</p>
+<h2 id="1-spark-itemsimilarity">1. spark-itemsimilarity</h2>
+<p><em>spark-itemsimilarity</em> is the Spark counterpart of the of the Mahout
mapreduce job called <em>itemsimilarity</em>. It takes in elements of
interactions, which have userID, itemID, and optionally a value. It will
produce one of more indicator matrices created by comparing every user's
interactions with every other user. The indicator matrix is an item x item
matrix where the values are log-likelihood ratio strengths. For the legacy
mapreduce version, there were several possible similarity measures but these
are being deprecated in favor of LLR because in practice it performs the
best.</p>
+<p>Mahout's mapreduce version of itemsimilarity takes a text file that is
expected to have user and item IDs that conform to Mahout's ID
requirements--they are non-negative integer that can be viewed as row and
column numbers in a matrix.</p>
+<p><em>spark-itemsimilarity</em> also extends the notion of cooccurrence to
cross-cooccurrence, in other words the Spark version will account for
multi-modal interactions and create cross-indicator matrices allowing users to
make use of much more data in creating recommendations or similar item
lists.</p>
+<p>```
+spark-itemsimilarity Mahout 1.0-SNAPSHOT
+Usage: spark-itemsimilarity [options]</p>
+<p>Input, output options
+ -i <value> | --input <value>
+ Input path, may be a filename, directory name, or comma delimited list
of
+ HDFS supported URIs (required)
+ -i2 <value> | --input2 <value>
+ Secondary input path for cross-similarity calculation, same
restrictions
+ as "--input" (optional). Default: empty.
+ -o <value> | --output <value>
+ Path for output, any local or HDFS supported URI (required)</p>
+<p>Algorithm control options:
+ -mppu <value> | --maxPrefs <value>
+ Max number of preferences to consider per user (optional). Default: 500
+ -m <value> | --maxSimilaritiesPerItem <value>
+ Limit the number of similarities per item to this number (optional).
+ Default: 100</p>
+<p>Note: Only the Log Likelihood Ratio (LLR) is supported as a similarity
measure.</p>
+<p>Input text file schema options:
+ -id <value> | --inDelim <value>
+ Input delimiter character (optional). Default: "[,\t]"
+ -f1 <value> | --filter1 <value>
+ String (or regex) whose presence indicates a datum for the primary
item
+ set (optional). Default: no filter, all data is used
+ -f2 <value> | --filter2 <value>
+ String (or regex) whose presence indicates a datum for the secondary
item
+ set (optional). If not present no secondary dataset is collected
+ -rc <value> | --rowIDPosition <value>
+ Column number (0 based Int) containing the row ID string (optional).
+ Default: 0
+ -ic <value> | --itemIDPosition <value>
+ Column number (0 based Int) containing the item ID string (optional).
+ Default: 1
+ -fc <value> | --filterPosition <value>
+ Column number (0 based Int) containing the filter string (optional).
+ Default: -1 for no filter</p>
+<p>Using all defaults the input is expected of the form: "userID<tab>itemId"
or "userID<tab>itemID<tab>any-text..." and all rows will be used</p>
+<p>File discovery options:
+ -r | --recursive
+ Searched the -i path recursively for files that match
--filenamePattern
+ (optional), default: false
+ -fp <value> | --filenamePattern <value>
+ Regex to match in determining input files (optional). Default:
filename
+ in the --input option or "^part-.*" if --input is a directory</p>
+<p>Output text file schema options:
+ -rd <value> | --rowKeyDelim <value>
+ Separates the rowID key from the vector values list (optional).
Default:
+\t"
+ -cd <value> | --columnIdStrengthDelim <value>
+ Separates column IDs from their values in the vector values list
(optional).
+ Default: ":"
+ -td <value> | --elementDelim <value>
+ Separates vector element values in the values list (optional).
Default: " "
+ -os | --omitStrength
+ Do not write the strength to the output files (optional), Default:
false.
+ This option is used to output indexable data for creating a search
engine
+ recommender.</p>
+<p>Default delimiters will produce output of the form:
"itemID1<tab>itemID2:value2<space>itemID10:value10..."</p>
+<p>Spark config options:
+ -ma <value> | --master <value>
+ Spark Master URL (optional). Default: "local". Note that you can
specify
+ the number of cores to get a performance improvement, for example
"local[4]"
+ -sem <value> | --sparkExecutorMem <value>
+ Max Java heap available as "executor memory" on each node (optional).
+ Default: 4g</p>
+<p>General config options:
+ -rs <value> | --randomSeed <value></p>
+<p>-h | --help
+ prints this usage text
+```</p>
+<p>This looks daunting but defaults to simple fairly sane values to take
exactly the same input as legacy code and is pretty flexible. It allows the
user to point to a single text file, a directory full of files, or a tree of
directories to be traversed recursively. The files included can be specified
with either a regex-style pattern or filename. The schema for the file is
defined by column numbers, which map to the important bits of data including
IDs and values. The files can even contain filters, which allow unneeded rows
to be discarded or used for cross-cooccurrence calculations.</p>
+<p>See ItemSimilarityDriver.scala in Mahout's spark module if you want to
customize the code. </p>
+<h3 id="defaults-in-the-spark-itemsimilarity-cli">Defaults in the
<em>spark-itemsimilarity</em> CLI</h3>
+<p>If all defaults are used the input can be as simple as:</p>
+<p><code>userID1,itemID1
+userID2,itemID2
+...</code></p>
+<p>With the command line:</p>
+<p><code>bash$ mahout spark-itemsimilarity --input in-file --output
out-dir</code></p>
+<p>This will use the "local" Spark context and will output the standard text
version of a DRM</p>
+<p><code>itemID1<tab>itemID2:value2<space>itemID10:value10...</code></p>
+<h3 id="more-complex-input">More Complex Input</h3>
+<p>For input of the form:</p>
+<p><code>u1,purchase,iphone
+u1,purchase,ipad
+u2,purchase,nexus
+u2,purchase,galaxy
+u3,purchase,surface
+u4,purchase,iphone
+u4,purchase,galaxy
+u1,view,iphone
+u1,view,ipad
+u1,view,nexus
+u1,view,galaxy
+u2,view,iphone
+u2,view,ipad
+u2,view,nexus
+u2,view,galaxy
+u3,view,surface
+u3,view,nexus
+u4,view,iphone
+u4,view,ipad
+u4,view,galaxy</code></p>
+<h3 id="command-line">Command Line</h3>
+<p>Use the following options can be used:</p>
+<p><code>bash$ mahout spark-itemsimilarity \
+ --input in-file \ # where to look for data
+ --output out-path \ # root dir for output
+ --master masterUrl \ # URL of the Spark master server
+ --filter1 purchase \ # word that flags input for the primary action
+ --filter2 view \ # word that flags input for the secondary action
+ --itemIDPosition 2 \ # column that has the item ID
+ --rowIDPosition 0 \ # column that has the user ID
+ --filterPosition 1 # column that has the filter word</code></p>
+<h3 id="output">Output</h3>
+<p>The output of the job will be the standard text version of two Mahout DRMs.
This is a case where we are calculating cross-cooccurrence so a primary
indicator matrix and cross-indicator matrix will be created</p>
+<p>```
+out-path
+ |-- indicator-matrix - TDF part files
+ -- cross-indicator-matrix - TDF part-files</p>
+<p>```
+The indicator matrix will contain the lines:</p>
+<p><code>galaxy\tnexus:1.7260924347106847
+ipad\tiphone:1.7260924347106847
+nexus\tgalaxy:1.7260924347106847
+iphone\tipad:1.7260924347106847
+surface</code></p>
+<p>The cross-indicator matrix will contain:</p>
+<p><code>iphone\tnexus:1.7260924347106847 iphone:1.7260924347106847
ipad:1.7260924347106847 galaxy:1.7260924347106847
+ipad\tnexus:0.6795961471815897 iphone:0.6795961471815897
ipad:0.6795961471815897 galaxy:0.6795961471815897
+nexus\tnexus:0.6795961471815897 iphone:0.6795961471815897
ipad:0.6795961471815897 galaxy:0.6795961471815897
+galaxy\tnexus:1.7260924347106847 iphone:1.7260924347106847
ipad:1.7260924347106847 galaxy:1.7260924347106847
+surface\tsurface:4.498681156950466 nexus:0.6795961471815897</code></p>
+<h3 id="log-file-input">Log File Input</h3>
+<p>A common method of storing data is in log files. If they are written using
some delimiter they can be consumed directly by spark-itemsimilarity. For
instance input of the form:</p>
+<p><code>2014-06-23 14:46:53.115\tu1\tpurchase\trandom text\tiphone
+2014-06-23 14:46:53.115\tu1\tpurchase\trandom text\tipad
+2014-06-23 14:46:53.115\tu2\tpurchase\trandom text\tnexus
+2014-06-23 14:46:53.115\tu2\tpurchase\trandom text\tgalaxy
+2014-06-23 14:46:53.115\tu3\tpurchase\trandom text\tsurface
+2014-06-23 14:46:53.115\tu4\tpurchase\trandom text\tiphone
+2014-06-23 14:46:53.115\tu4\tpurchase\trandom text\tgalaxy
+2014-06-23 14:46:53.115\tu1\tview\trandom text\tiphone
+2014-06-23 14:46:53.115\tu1\tview\trandom text\tipad
+2014-06-23 14:46:53.115\tu1\tview\trandom text\tnexus
+2014-06-23 14:46:53.115\tu1\tview\trandom text\tgalaxy
+2014-06-23 14:46:53.115\tu2\tview\trandom text\tiphone
+2014-06-23 14:46:53.115\tu2\tview\trandom text\tipad
+2014-06-23 14:46:53.115\tu2\tview\trandom text\tnexus
+2014-06-23 14:46:53.115\tu2\tview\trandom text\tgalaxy
+2014-06-23 14:46:53.115\tu3\tview\trandom text\tsurface
+2014-06-23 14:46:53.115\tu3\tview\trandom text\tnexus
+2014-06-23 14:46:53.115\tu4\tview\trandom text\tiphone
+2014-06-23 14:46:53.115\tu4\tview\trandom text\tipad
+2014-06-23 14:46:53.115\tu4\tview\trandom text\tgalaxy</code></p>
+<p>Can be parsed with the following CLI and run on the cluster producing the
same output as the above example.</p>
+<p><code>bash$ mahout spark-itemsimilarity \
+ --input in-file \
+ --output out-path \
+ --master spark://sparkmaster:4044 \
+ --filter1 purchase \
+ --filter2 view \
+ --inDelim "\t" \
+ --itemIDPosition 4 \
+ --rowIDPosition 1 \
+ --filterPosition 2 \</code></p>
+<h2 id="2-spark-rowsimilarity">2. spark-rowsimilarity</h2>
+<p><em>spark-rowsimilarity</em> is the companion to
<em>spark-itemsimilarity</em> the primary difference is that it takes a text
file version of a DRM with optional application specific IDs. The input is in
text-delimited form where there are three delimiters used. By default it reads
(rowID<tab>columnID1:strength1<space>columnID2:strength2...) Since this job
only supports LLR similarity, which does not use the input strengths, they may
be omitted in the input. It writes
(columnID<tab>columnID1:strength1<space>columnID2:strength2...) The output is
sorted by strength descending. The output can be interpreted as a column id
from the primary input followed by a list of the most similar columns. For a
discussion of the output layout and formatting see
<em>spark-itemsimilarity</em>. </p>
+<p>One significant output option is --omitStrength. This allows output of the
form (columnID<tab>columnID2<space>columnID2<space>...) This is a tab-delimited
file containing a columnID token followed by a space delimited string of
tokens. It can be directly indexed by search engines to create an item-based
recommender.</p>
+<p>The command line interface is:</p>
+<p>```
+spark-rowsimilarity Mahout 1.0-SNAPSHOT
+Usage: spark-rowsimilarity [options]</p>
+<p>Input, output options
+ -i <value> | --input <value>
+ Input path, may be a filename, directory name, or comma delimited list
+ of HDFS supported URIs (required)
+ -i2 <value> | --input2 <value>
+ Secondary input path for cross-similarity calculation, same
restrictions
+ as "--input" (optional). Default: empty.
+ -o <value> | --output <value>
+ Path for output, any local or HDFS supported URI (required)</p>
+<p>Algorithm control options:
+ -mo <value> | --maxObservations <value>
+ Max number of observations to consider per row (optional). Default: 500
+ -m <value> | --maxSimilaritiesPerRow <value>
+ Limit the number of similarities per item to this number (optional).
+ Default: 100</p>
+<p>Note: Only the Log Likelihood Ratio (LLR) is supported as a similarity
measure.</p>
+<p>Output text file schema options:
+ -rd <value> | --rowKeyDelim <value>
+ Separates the rowID key from the vector values list (optional).
+ Default: "\t"
+ -cd <value> | --columnIdStrengthDelim <value>
+ Separates column IDs from their values in the vector values list
+ (optional). Default: ":"
+ -td <value> | --elementDelim <value>
+ Separates vector element values in the values list (optional).
+ Default: " "
+ -os | --omitStrength
+ Do not write the strength to the output files (optional), Default:
+ false.
+This option is used to output indexable data for creating a search engine
+recommender.</p>
+<p>Default delimiters will produce output of the form:
"itemID1<tab>itemID2:value2<space>itemID10:value10..."</p>
+<p>File discovery options:
+ -r | --recursive
+ Searched the -i path recursively for files that match
+ --filenamePattern (optional), Default: false
+ -fp <value> | --filenamePattern <value>
+ Regex to match in determining input files (optional). Default:
+ filename in the --input option or "^part-.*" if --input is a
directory</p>
+<p>Spark config options:
+ -ma <value> | --master <value>
+ Spark Master URL (optional). Default: "local". Note that you can
+ specify the number of cores to get a performance improvement, for
+ example "local[4]"
+ -sem <value> | --sparkExecutorMem <value>
+ Max Java heap available as "executor memory" on each node (optional).
+ Default: 4g</p>
+<p>General config options:
+ -rs <value> | --randomSeed <value></p>
+<p>-h | --help
+ prints this usage text
+```
+See RowSimilarityDriver.scala in Mahout's spark module if you want to
customize the code. </p>
+<h1 id="3-creating-a-recommender">3. Creating a Recommender</h1>
+<p>One significant output option for the spark-itemsimilarity job is
--omitStrength. This is a tab-delimited file containing a itemID token followed
by a space delimited string of tokens of the form:</p>
+<p><code>itemID<tab>itemsIDs-from-the-indicator-matrix</code></p>
+<p>To create a cooccurrence type collaborative filtering recommender using a
search engine simply index this output created with --omitStrength. Then at
runtime query the indexed data with the current user's history of the primary
action on the index field that contains the primary indicator tokens. The
result will be an ordered list of itemIDs as recommendations.</p>
+<p>It is possible to include the indicator strengths by attaching them to the
tokens before indexing but that is engine specific and beyond this description.
Using without weights generally provides good results since the indicators have
been downsampled by strength so the indicator matrix has some degree of quality
guarantee. </p>
+<h2 id="multi-action-recommendations">Multi-action Recommendations</h2>
+<p>Optionally the query can contain the user's history of a secondary action
(input with --input2) against the cross-indicator tokens as a second field.</p>
+<p>In this case the indicator-matrix and the cross-indicator-matrix should be
combined and indexed as two fields. The data will be of the form:</p>
+<p><code>itemID, itemIDs-from-indicator-matrix,
itemIDs-from-cross-indicator-matrix</code></p>
+<p>Now the query will have one string of the user's primary action history and
a second of the user's secondary action history against two fields in the
index.</p>
+<p>It is probably better to index the two (or more) fields as multi-valued
fields (arrays) and query them as such but the above works in much the same way
if the indexed tokens are space delimited as is the query string. </p>
+<p><strong>Note:</strong> Using the underlying code it is possible to use as
many actions as you have data for to create a multi-action recommender that
makes the most of available data. The CLI only supports two actions.</p>
+<h1 id="4-using-spark-rowsimilarity-with-text-data">4. Using
<em>spark-rowsimilarity</em> with Text Data</h1>
+<p>Another use case for these jobs is in finding similar textual content. For
instance given the content of a blog post, which other posts are similar. In
this case the columns are tokenized words and the rows are documents. Since LLR
is being used there is no need to attach TF-IDF weights to the
tokens—they will not be used. The Apache <a
href="http://lucene.apache.org";>Lucene</a> project provides several methods of
<a
href="http://lucene.apache.org/core/4_9_0/core/org/apache/lucene/analysis/package-summary.html#package_description";>analyzing
and tokenizing</a> documents.</p>
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