Title: Recommender Documentation
<a name="RecommenderDocumentation-Overview"></a>
## Overview

_This documentation concerns the non-distributed, non-Hadoop-based
recommender engine / collaborative filtering code inside Mahout. It was
formerly a separate project called "Taste" and has continued development
inside Mahout alongside other Hadoop-based code. It may be viewed as a
somewhat separate, older, more comprehensive and more mature aspect of this
code, compared to current development efforts focusing on Hadoop-based
distributed recommenders. This remains the best entry point into Mahout
recommender engines of all kinds._

A Mahout-based collaborative filtering engine takes users' preferences for
items ("tastes") and returns estimated preferences for other items. For
example, a site that sells books or CDs could easily use Mahout to figure
out, from past purchase data, which CDs a customer might be interested in
listening to.

Mahout provides a rich set of components from which you can construct a
customized recommender system from a selection of algorithms. Mahout is
designed to be enterprise-ready; it's designed for performance, scalability
and flexibility.

Mahout recommenders are not just for Java; it can be run as an external
server which exposes recommendation logic to your application via web
services and HTTP.

Top-level packages define the Mahout interfaces to these key abstractions:
* DataModel
* UserSimilarity
* ItemSimilarity
* UserNeighborhood
* Recommender

Subpackages of org.apache.mahout.cf.taste.impl hold implementations of
these interfaces. These are the pieces from which you will build your own
recommendation engine. That's it! For the academically inclined, Mahout
supports both *memory-based*, *item-based* recommender systems, *slope one*
recommenders, and a couple other experimental implementations. It does not
currently support *model-based* recommenders.

<a name="RecommenderDocumentation-Architecture"></a>
## Architecture

![taste-architecture](attachments/22872433/23003157.png)

This diagram shows the relationship between various Mahout components in a
user-based recommender. An item-based recommender system is similar except
that there are no PreferenceInferrers or Neighborhood algorithms involved.

<a name="RecommenderDocumentation-Recommender"></a>
### Recommender
A Recommender is the core abstraction in Mahout. Given a DataModel, it can
produce recommendations. Applications will most likely use the
GenericUserBasedRecommender implementation GenericItemBasedRecommender,
possibly decorated by CachingRecommender.

<a name="RecommenderDocumentation-DataModel"></a>
### DataModel
A DataModel is the interface to information about user preferences. An
implementation might draw this data from any source, but a database is the
most likely source. Mahout provides MySQLJDBCDataModel, for example, to
access preference data from a database via JDBC and MySQL. Another exists
for PostgreSQL. Mahout also provides a FileDataModel.

There are no abstractions for a user or item in the object model (not
anymore). Users and items are identified solely by an ID value in the
framework. Further, this ID value must be numeric; it is a Java long type
through the APIs. A Preference object or PreferenceArray object
encapsulates the relation between user and preferred items (or items and
users preferring them).

Finally, Mahout supports, in various ways, a so-called "boolean" data model
in which users do not express preferences of varying strengths for items,
but simply express an association or none at all. For example, while users
might express a preference from 1 to 5 in the context of a movie
recommender site, there may be no notion of a preference value between
users and pages in the context of recommending pages on a web site: there
is only a notion of an association, or none, between a user and pages that
have been visited.

<a name="RecommenderDocumentation-UserSimilarity"></a>
### UserSimilarity
A UserSimilarity defines a notion of similarity between two Users. This is
a crucial part of a recommendation engine. These are attached to a
Neighborhood implementation. ItemSimilarities are analagous, but find
similarity between Items.

<a name="RecommenderDocumentation-UserNeighborhood"></a>
### UserNeighborhood
In a user-based recommender, recommendations are produced by finding a
"neighborhood" of similar users near a given user. A UserNeighborhood
defines a means of determining that neighborhood &mdash; for example,
nearest 10 users. Implementations typically need a UserSimilarity to
operate.

<a name="RecommenderDocumentation-Requirements"></a>
## Requirements
<a name="RecommenderDocumentation-Required"></a>
### Required

* [Java/ J2SE 6.0](http://www.java.com/getjava/index.jsp)

<a name="RecommenderDocumentation-Optional"></a>
### Optional
* [Apache Maven](http://maven.apache.org)
  2.2.1 or later, if you want to build from source or build examples. (Mac
users note that even OS X 10.5 ships with Maven 2.0.6, which will not
work.)
* Mahout web applications require a [Servlet 2.3+](http://java.sun.com/products/servlet/index.jsp)
 container, such as [Apache Tomcat|http://jakarta.apache.org/tomcat/]
. It may in fact work with oldercontainers with slight modification.

<a name="RecommenderDocumentation-Demo"></a>
## Demo

To build and run the demo, follow the instructions below, which are written
for Unix-like operating systems:

* Obtain a copy of the Mahout distribution, either from SVN or as a
downloaded archive.
* Download the "1 Million MovieLens Dataset" from [Grouplens.org](http://www.grouplens.org/)
* Unpack the archive and copy movies.dat and ratings.dat to
trunk/integration/src/main/resources/org/apache/mahout/cf/taste/example/grouplens
under the Mahout distribution directory.
* Navigate to the directory where you unpacked the Mahout distribution, and
navigate to trunk.
* Run mvn -DskipTests install, which builds and installs Mahout core to
your local repository
* cd integration
* You may need to give Maven more memory: in a bash shell, export
MAVEN_OPTS=-Xmx1024M
* mvn jetty:run.
* Get recommendations by accessing the web application in your browser:
http://localhost:8080/mahout-integration/RecommenderServlet?userID=1 This
will produce a simple preference-item ID list which could be consumed by a
client application. Get more useful human-readable output with the debug
parameter:
http://localhost:8080/mahout-integration/RecommenderServlet?userID=1&debug=true


<a name="RecommenderDocumentation-Examples"></a>
## Examples
<a name="RecommenderDocumentation-User-basedRecommender"></a>
### User-based Recommender
User-based recommenders are the "original", conventional style of
recommender system. They can produce good recommendations when tweaked
properly; they are not necessarily the fastest recommender systems and are
thus suitable for small data sets (roughly, less than ten million ratings).
We'll start with an example of this.

First, create a DataModel of some kind. Here, we'll use a simple on based
on data in a file. The file should be in CSV format, with lines of the form
"userID,itemID,prefValue" (e.g. "39505,290002,3.5"):


    DataModel model = new FileDataModel(new File("data.txt"));


We'll use the PearsonCorrelationSimilarity implementation of UserSimilarity
as our user correlation algorithm, and add an optional preference inference
algorithm:


    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    // Optional:
    userSimilarity.setPreferenceInferrer(new AveragingPreferenceInferrer());


Now we create a UserNeighborhood algorithm. Here we use nearest-3:


    UserNeighborhood neighborhood =
    	  new NearestNUserNeighborhood(3, userSimilarity, model);
    
    Now we can create our Recommender, and add a caching decorator:
    

Recommender recommender =
	  new GenericUserBasedRecommender(model, neighborhood,
userSimilarity);
Recommender cachingRecommender = new CachingRecommender(recommender);

    
    Now we can get 10 recommendations for user ID "1234" &mdash; done!

List<RecommendedItem> recommendations =
	  cachingRecommender.recommend(1234, 10);

    
    h3.Item-based Recommender
    
    We could have created an item-based recommender instead. Item-based
recommender base recommendation not on user similarity, but on item
similarity. In theory these are about the same approach to the problem,
just from different angles. However the similarity of two items is
relatively fixed, more so than the similarity of two users. So, item-based
recommenders can use pre-computed similarity values in the computations,
which make them much faster. For large data sets, item-based recommenders
are more appropriate.
    
    Let's start over, again with a FileDataModel to start:
    

DataModel model = new FileDataModel(new File("data.txt"));

    
    We'll also need an ItemSimilarity. We could use
PearsonCorrelationSimilarity, which computes item similarity in realtime,
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
GenericItemSimilarity: 
    

// Construct the list of pre-computed correlations
Collection<GenericItemSimilarity.ItemItemSimilarity> correlations =
	  ...;
ItemSimilarity itemSimilarity =
	  new GenericItemSimilarity(correlations);


    
    Then we can finish as before to produce recommendations:
    
<pre><code>
Recommender recommender =
	  new GenericItemBasedRecommender(model, itemSimilarity);
Recommender cachingRecommender = new CachingRecommender(recommender);
...
List<RecommendedItem> recommendations =
	  cachingRecommender.recommend(1234, 10);<code></pre>

    
    h3. Slope-One Recommender
    This is a simple yet effective Recommender and we present another example
to round out the list:
    
<pre><code>
DataModel model = new FileDataModel(new File("data.txt"));
	  // Make a weighted slope one recommender
	  Recommender recommender = new SlopeOneRecommender(model);
	  Recommender cachingRecommender = new
CachingRecommender(recommender);</code></pre>
	


    
<a name="RecommenderDocumentation-Integrationwithyourapplication"></a>
## Integration with your application
<a name="RecommenderDocumentation-Direct"></a>
### Direct

You can create a Recommender, as shown above, wherever you like in your
Java application, and use it. This includes simple Java applications or GUI
applications, server applications, and J2EE web applications.

<a name="RecommenderDocumentation-Standaloneserver"></a>
### Standalone server
A Mahout recommender can also be run as an external server, which may be
the only option for non-Java applications. It can be exposed as a web
application via org.apach.mahout.cf.taste.web.RecommenderServlet, and your
application can then access recommendations via simple HTTP requests and
response. See above, and see the javadoc for details.

<a name="RecommenderDocumentation-Performance"></a>
## Performance
<a name="RecommenderDocumentation-RuntimePerformance"></a>
### Runtime Performance
The more data you give, the better. Though Mahout is designed for
performance, you will undoubtedly run into performance issues at some
point. For best results, consider using the following command-line flags to
your JVM:

* -server: Enables the server VM, which is generally appropriate for
long-running, computation-intensive applications.
* -Xms1024m -Xmx1024m: Make the heap as big as possible -- a gigabyte
doesn't hurt when dealing with tens millions of preferences. Mahout
recommenders will generally use as much memory as you give it for caching,
which helps performance. Set the initial and max size to the same value to
avoid wasting time growing the heap, and to avoid having the JVM run minor
collections to avoid growing the heap, which will clear cached values.
* -da -dsa: Disable all assertions.
* -XX:NewRatio=9: Increase heap allocated to 'old' objects, which is most
of them in this framework
* -XX:+UseParallelGC -XX:+UseParallelOldGC (multi-processor machines only):
Use a GC algorithm designed to take advantage of multiple processors, and
designed for throughput. This is a default in J2SE 5.0.
* -XX:-DisableExplicitGC: Disable calls to System.gc(). These calls can
only hurt in the presence of modern GC algorithms; they may force Mahout to
remove cached data needlessly. This flag isn't needed if you're sure your
code and third-party code you use doesn't call this method.

Also consider the following tips:

* Use CachingRecommender on top of your custom Recommender implementation.
* When using JDBCDataModel, make sure you've taken basic steps to optimize
the table storing preference data. Create a primary key on the user ID and
item ID columns, and an index on them. Set them to be non-null. And so on.
Tune your database for lots of concurrent reads! When using JDBC, the
database is almost always the bottleneck. Plenty of memory and caching are
even more important.
* Also, pooling database connections is essential to performance. If using
a J2EE container, it probably provides a way to configure connection pools.
If you are creating your own DataSource directly, try wrapping it in
org.apache.mahout.cf.taste.impl.model.jdbc.ConnectionPoolDataSource
* See MySQL-specific notes on performance in the javadoc for
MySQLJDBCDataModel.

<a name="RecommenderDocumentation-AlgorithmPerformance:WhichOneIsBest?"></a>
### Algorithm Performance: Which One Is Best?
There is no right answer; it depends on your data, your application,
environment, and performance needs. Mahout provides the building blocks
from which you can construct the best Recommender for your application. The
links below provide research on this topic. You will probably need a bit of
trial-and-error to find a setup that works best. The code sample above
provides a good starting point.

Fortunately, Mahout provides a way to evaluate the accuracy of your
Recommender on your own data, in org.apache.mahout.cf.taste.eval"


    DataModel myModel = ...;
    RecommenderBuilder builder = new RecommenderBuilder() {
      public Recommender buildRecommender(DataModel model) {
        // build and return the Recommender to evaluate here
      }
    };
    RecommenderEvaluator evaluator =
    	  new AverageAbsoluteDifferenceRecommenderEvaluator();
    double evaluation = evaluator.evaluate(builder, myModel, 0.9, 1.0);


For "boolean" data model situations, where there are no notions of
preference value, the above evaluation based on estimated preference does
not make sense. In this case, try this kind of evaluation, which presents
traditional information retrieval figures like precision and recall, which
are more meaningful:


    ...
    RecommenderIRStatsEvaluator evaluator =
    	new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats =
    	evaluator.evaluate(builder, null, myModel, null, 3,
    RecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
    	&sect;1.0);



<a name="RecommenderDocumentation-UsefulLinks"></a>
## Useful Links
You'll want to look at these packages too, which offer more algorithms and
approaches that you may find useful:

* [Cofi](http://www.nongnu.org/cofi/)
: A Java-Based Collaborative Filtering Library
* [CoFE](http://eecs.oregonstate.edu/iis/CoFE/)

Here's a handful of research papers that I've read and found particularly
useful:

J.S. Breese, D. Heckerman and C. Kadie, "[Empirical Analysis of Predictive Algorithms for Collaborative Filtering](http://research.microsoft.com/research/pubs/view.aspx?tr_id=166)
," in Proceedings of the Fourteenth Conference on Uncertainity in
Artificial Intelligence (UAI 1998), 1998.

B. Sarwar, G. Karypis, J. Konstan and J. Riedl, "[Item-based collaborative filtering recommendation algorithms](http://www10.org/cdrom/papers/519/)
" in Proceedings of the Tenth International Conference on the World Wide
Web (WWW 10), pp. 285-295, 2001.

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, "[GroupLens: an open architecture for collaborative filtering of netnews](http://doi.acm.org/10.1145/192844.192905)
" in Proceedings of the 1994 ACM conference on Computer Supported
Cooperative Work (CSCW 1994), pp. 175-186, 1994.

J.L. Herlocker, J.A. Konstan, A. Borchers and J. Riedl, "[An algorithmic framework for performing collaborative filtering](http://www.grouplens.org/papers/pdf/algs.pdf)
" in Proceedings of the 22nd annual international ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR 99), pp. 230-237,
1999.

Clifford Lyon, "[Movie Recommender](http://materialobjects.com/cf/MovieRecommender.pdf)
" CSCI E-280 final project, Harvard University, 2004.

Daniel Lemire, Anna Maclachlan, "[Slope One Predictors for Online Rating-Based Collaborative Filtering](http://www.daniel-lemire.com/fr/abstracts/SDM2005.html)
," Proceedings of SIAM Data Mining (SDM '05), 2005.

Michelle Anderson, Marcel Ball, Harold Boley, Stephen Greene, Nancy Howse, Daniel Lemire and Sean McGrath, "[RACOFI: A Rule-Applying Collaborative Filtering System](http://www.daniel-lemire.com/fr/documents/publications/racofi_nrc.pdf)
"," Proceedings of COLA '03, 2003.

These links will take you to all the collaborative filtering reading you
could ever want!
* [Paul Perry's notes](http://www.paulperry.net/notes/cf.asp)
* [James Thornton's collaborative filtering resources](http://jamesthornton.com/cf/)
* [Daniel Lemire's blog](http://www.daniel-lemire.com/blog/)
 which frequently covers collaborative filtering topics