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