Ever given a thought on how machine intelligent the softwares are getting nowadays? Lets imagine a situation when you surf a video on youtube and you are not given any recommendations for similar videos, Or a situation when none of the irritating mails we get automatically go to our spam folder? Life would not be as easy as it currently is. Machine learning is getting in trend and so we thought about Integrating Apache Mahout with AEM to simply make our AEM applications smarter and interesting! Mahout helps us in 3 different ways: 1. Recommendations 2. Classification 3. Clustering We will focus on a typical use case of developing E-commerce applications in AEM and providing product recommendations on the basis of user search history.

1. APACHE SLING&FRIENDSTECHMEETUP
BERLIN, 26-28SEPTEMBER 2016
IntegratingApache Mahout with AEM
Ankit Gubrani & RimaMittal

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Speakers Bio

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Agenda

4. Agenda
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 Introduction to Apache Mahout
 MachineLearning
 Recommendations
 AEM with Apache Mahout
 Demo
 ExtensionPoints

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Introduction to Apache Mahout

6. Whatis Apache Mahout?
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 Project of the Apache Software
Foundation.
 Producing free implementations of
scalablemachine learning algorithms,
written in Java.

7. History
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 Started as a Lucenesub-project.
 Became Apache TLPin April 2010.
 Latest version – 0.12.2 – Released on 13th June 2016.

8. Why ApacheMahout?
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 Increasing volume of data!
 Traditional Data miningalgorithms struggle to process very
large datasets.
 Apache Mahoutto therescue!

9. TraditionalMachine Learning
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10. Machine LearningwithMahout
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11. Applications
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 Adobe, Facebook, LinkedIn,Twitter and Yahoo use Mahout
internally.
 Twitter uses Mahoutfor interest modelling.
 Yahoo! Uses Mahoutfor patternmining.

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MachineLearning

13. Machine Learning
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 Programmingcomputers to optimize a Performance Criterion
usingExample Data or PastExperience
 Branch of ArtificialIntelligence.
 Computers evolve behavior based on Empirical data.

14. Techniques
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 Supervised Learning
 Use Labelled training data to create a classifier that can predict
output for unseen inputs.
 Unsupervised Learning
 Use Unlabeled training data to create a function that can predict
output.

15. Machine LearningwithApache Mahout
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 DataScience use cases Mahoutsupports:
 Collaborative Filtering
 Clustering
 Classification

16. CollaborativeFiltering
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 User behavior mining tomake product
recommendations.

17. Clustering
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 Organizing items into naturally
occurring groups, such that items
belonging to same group are similar to
each other

18. Classification
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 Learning from existing categorizations
and assigningunclassified items to the
best category

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Recommendations

20. ApacheMahout RecommendationEngine
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 Helps users find items they mightlike based on historical
behavior and preferences.
 Mahoutprovides a rich set of componentsfrom which a
customizedrecommender system can be constructed usinga
selection ofAlgorithms.

21. Architecture
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 Top Level Packages
 DataModel
 UserSimilarity
 ItemSimilarity
 UserNeighboorhood
 Recommender

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AEM with Apache Mahout

23. Checklist
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 AEM 6.2
 Mahout as a Maven Dependency
<dependency>
<groupId>org.apache.mahout</groupId>
<artifactId>mahout-mr</artifactId>
<version>0.10.0</version>
</dependency>

24. JCRDataModel
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 DataModel
 Implementations representing a repository of information about
users and their associated preferences.
 AbstractDataModel, JDBCDataModel, FileDataModel,
GenericBooleanPrefDataModel, GenericDataModel.
 AEM - JCRDataModel.

25. Code(1/2)
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 Using The AEM JCRDataModel
public JSONArray getUserBasedRecommendations(ResourceResolver
resourceResolver, String userId, int numberOfRecommendations) {
//Creating JCRDataModel to fetch information from JCR
DataModel model = JCRDataModel.createDataModel(resourceResolver);
}

26. Code(2/2)
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 AEM-Mahout Recommendation steps
UserSimilarity userSimilarity = getSimilarity(model);
UserNeighborhood neighborhood = getNeighbourHood(N_NEIGHOBUR_HOOD,
userSimilarity, model);
GenericUserBasedRecommender recommender = new
GenericUserBasedRecommender(model, neighborhood, userSimilarity);
recommendations = recommender.recommend(userIdHash,
numberOfRecommedations, null, false);

27. AEMProduct Recommendation
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 User Based Recommendation
 Takesuser ratingsinto consideration
 Based on PearsonCorrelationSimilarity
 Uses NearestNUserNeighborhood

28. ConfiguringJCRDataModel
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 Configurations
 User Generated Content Path
 /content/usergenerated/asi/jcr
 Product Path
 /etc/commerce/products/geometrixx-outdoors
 Rating Resource Type
 Defaults to social/tally/components/response

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Demo

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Appendix

31. Appendix
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 https://mahout.apache.org/
 http://www.slideshare.net/VaradMeru/introduction-to-
mahout-and-machine-learning
 https://www.youtube.com/watch?v=iMAMYzfRiS4

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Clone thecode!

33. CodeRepository
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 https://github.com/rimamittal/AEMMahout.git

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Questions.

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Thankyou.