This document provides an overview of Apache Mahout, an open-source library for scalable machine learning and data mining. It describes Mahout's collaborative filtering module and how it can be used to build recommender systems. Key classes and algorithms are explained, including item-based collaborative filtering, latent factor models like SVD, and tools for evaluating recommender quality. Potential student projects are outlined, such as implementing a novel similarity measure or improving Mahout's capabilities for temporal recommendation evaluation.

1. An Introduction to Collaborative Filtering
with Apache Mahout
Sebastian Schelter
Recommender Systems Challenge
at ACM RecSys 2012
Database Systems and Information Management Group (DIMA)
Technische Universität Berlin
13.09.2012
http://www.dima.tu-berlin.de/
DIMA – TU Berlin 1

2. Overview
■ Apache Mahout: apache-licensed library
with the goal to provide highly scalable
data mining and machine learning
■ its collaborative filtering module is based on the Taste
framework of Sean Owen
■ mostly aimed at production scenarios, with a focus on
□ processing efficiency
□ integratibility with different datastores, web applications, Amazon EC2
□ scalability, allows computation of recommendations, items similarities and
matrix decompositions via MapReduce on Apache Hadoop
■ not that much used in recommender challenges
□ not enough different algorithms implemented?
□ not enough tooling for evaluation?
→ it‘s open source, so it‘s up to you to change that!
13.09.2012 DIMA – TU Berlin 2

3. Preference & DataModel
■ Preference encapsulates a user-item-interaction as
(user,item,value) triple
□ only numeric userIDs and itemIDs allowed for memory efficiency
□ PreferenceArray encapsulates a set of preferences
■ DataModel encapsulates a dataset
□ lots of convenient accessor methods like getNumUsers(),
getPreferencesForItem(itemID), ...
□ allows to add temporal information to preferences
□ lots of options to store the data (in-memory, file, database, key-value
store)
□ drawback: for a lot of usecases, all the data has to fit into memory to allow
efficient recommendation
DataModel dataModel = new FileDataModel(new File(„movielens.csv“));
PreferenceArray prefsOfUser1 = dataModel.getPreferencesFromUser(1);
13.09.2012 DIMA – TU Berlin 3

4. Recommender
■ Recommender is the basic interface for all of Mahout‘s
recommenders
□ recommend n items for a particular user
□ estimate the preference of a user towards an item
■ a CandidateItemsStrategy fetches all items that might be
recommended for a particular user
■ a Rescorer allows postprocessing recommendations
List<RecommendedItem> topItems = recommender.recommend(1, 10);
float preference = recommender.estimatePreference(1, 25);
13.09.2012 DIMA – TU Berlin 4

5. Item-Based Collaborative Filtering
■ ItemBasedRecommender
□ can also compute item similarities
□ can provide preferences for items as justification for recommendations
■ lots of similarity measures available (Pearson correlation,
Jaccard coefficient, ...)
■ also allows usage of precomputed item similarities stored in a
file (via FileItemSimilarity)
ItemBasedRecommender recommender =
new GenericItemBasedRecommender(dataModel,
new PearsonCorrelationSimilarity(dataModel));
List<RecommendedItem> similarItems =
recommender.mostSimilarItems(5, 10);
13.09.2012 DIMA – TU Berlin 5

6. Latent factor models
■ SVDRecommender
□ uses a decomposition of the user-item-interaction matrix to compute
recommendations
■ uses a Factorizer to compute a Factorization from a
DataModel, several different implementations available
□ Simon Funk‘s SGD
□ Alternating Least Squares
□ Weighted matrix factorization for implicit feedback data
Factorizer factorizer = new ALSWRFactorizer(dataModel, numFeatures,
lambda, numIterations);
Recommender svdRecommender =
new SVDRecommender(dataModel, factorizer);
List<RecommendedItem> topItems = svdRecommender.recommend(1, 10);
13.09.2012 DIMA – TU Berlin 6

7. Evaluating recommenders
■ RecommenderEvaluator, RecommenderIRStatsEvaluator
□ allow to measure the prediction quality of a recommender by using a
random split of the dataset
□ support for MAE, RMSE, Precision, Recall, ....
□ need a DataModel, a RecommenderBuilder, a DataModelBuilder for the
training data
RecommenderEvaluator maeEvaluator = new
AverageAbsoluteDifferenceRecommenderEvaluator();
maeEvaluator.evaluate(
new BiasedRecommenderBuilder(lambda2, lambda3, numIterations),
new InteractionCutDataModelBuilder(maxPrefsPerUser),
dataModel, trainingPercentage, 1 - trainingPercentage);
13.09.2012 DIMA – TU Berlin 7

8. Evaluating recommenders
■ RecommenderEvaluator, RecommenderIRStatsEvaluator
□ allow to measure the prediction quality of a recommender by using a
random split of the dataset
□ support for MAE, RMSE, Precision, Recall, ....
□ need a DataModel, a RecommenderBuilder, a DataModelBuilder for the
training data
RecommenderEvaluator maeEvaluator = new
AverageAbsoluteDifferenceRecommenderEvaluator();
maeEvaluator.evaluate(
new BiasedRecommenderBuilder(lambda2, lambda3, numIterations),
new InteractionCutDataModelBuilder(maxPrefsPerUser),
dataModel, trainingPercentage, 1 - trainingPercentage);
13.09.2012 DIMA – TU Berlin 8

9. Starting to work on Mahout
■ Prerequisites
□ Java 6
□ Maven
□ svn client
■ checkout the source code from
http://svn.apache.org/repos/asf/mahout/trunk
■ import it as a maven project into your favorite IDE
13.09.2012 DIMA – TU Berlin 9

10. Project: novel item similarity measure
■ in the Million Song DataSet Challenge, a novel item
similarity measure was used in the winning solution
■ would be great to see this one also featured in Mahout
■ Task
□ implement the novel item similarity measure as subclass of Mahout’s
ItemSimilarity
■ Future Work
□ this novel similarity measure is asymmetric, ensure that it is correctly
applied in all scenarios
13.09.2012 DIMA – TU Berlin 10

11. Project: temporal split evaluator
■ currently Mahout‘s standard RecommenderEvaluator
randomly splits the data into training and test set
■ for datasets with timestamps it would be much more
interesting use this temporal information to split the data
into training and test set
■ Task
□ create a TemporalSplitRecommenderEvaluator similar to the existing
AbstractDifferenceRecommenderEvaluator
■ Future Work
□ factor out the logic for splitting datasets into training and test set
13.09.2012 DIMA – TU Berlin 11

12. Project: baseline method for rating prediction
■ port MyMediaLite’s UserItemBaseline to Mahout
(preliminary port already available)
■ user-item-baseline estimation is a simple approach that
estimates the global tendency of a user or an item to
deviate from the average rating
(described in Y. Koren: Factor in the Neighbors: Scalable
and Accurate Collaborative Filtering, TKDD 2009)
■ Task
□ polish the code
□ make it work with Mahout’s DataModel
■ Future Work
□ create an ItemBasedRecommender that makes use of the estimated
biases
13.09.2012 DIMA – TU Berlin 12

13. Thank you.
Questions?
Sebastian Schelter
Database Systems and Information Management Group (DIMA)
Technische Universität Berlin
13.09.2012 DIMA – TU Berlin 13