Recommender Systems play a crucial role in a variety of businesses in today`s world. From E-Commerce web sites to News Portals, companies are leveraging data about their users to create a personalizes user experience, gain competitive advantage and eventually drive revenue. Dealing with the sheer quantity of data readily available can be a daunting task by itself. Consider applying machine learning algorithms on top of it and it makes the problem exponentially complex. Fortunately, tools like Hadoop and HBase make this task a little more manageable by taking out some of the complexities of dealing with a large amount of data. In this talk, we will share our success story of building a recommender system for Bloomberg.com leveraging the Hadoop ecosystem. We will describe the high level architecture of the system and discuss the pros and cons of our design choices. Bloomberg.com operates at a scale of 100s of millions of users. Building a recommendation engine for Bloomberg.com entails applying Machine Learning algorithms on terabytes of data and still being able to serve sub-second responses. We will discuss techniques for efficiently and reliably collecting data in near real-time, the notion of offline vs. online processing and most importantly, how HBase perfectly fits the bill by serving as a real-time database as well as input/output for running MapReduce.

1. Dhaval Shah
R&D Software Engineer, Bloomberg L. P.
Recommender Systems at scale
using HBase and Hadoop
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2. Agenda
 Introduction to Recommender Systems
 Types of Recommender Systems
 Building a Recommender System
 Summary
 (Hopefully) Lots of Q&A
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3.  What is a Recommender System?
 Wikipedia1 – Recommender systems are a subclass of
information filtering system that seek to predict the
‘rating’ or ‘preference’ that user would give to an item or
social element they had not yet considered, using a model
built from the characteristics of an item (content-based
approaches) or the user’s social environment
(collaborative filtering approaches).
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Introduction to Recommender Systems
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4. Introduction to Recommender Systems
 Where are Recommender Systems used?
 Everywhere! (Well almost!)
 E-Commerce
 Web Portals
 Online Radio
 Streaming Movies
 Media/News
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8. Introduction to Recommender Systems
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11.  Why do you need a Recommender System?
 Too much useful information
 Bloomberg.com statistics
o 500-1000 stories, 100-200 videos published per day
o Average user consumption << Articles published
o Satisfied user = Content Quality + User preference
o Double digit increases in CTR
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Introduction to Recommender Systems
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12. Types of Recommender Systems
 Content-Based
 Collaborative filter based
 User-based
 Item-based
 Hybrid
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13. Building a Recommender System
 Collect/Generate metadata about stories/videos
 Identify and track users
 Track user activity
 Store user activity
 Generate user models
 Serve recommendations
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14.  Collect metadata about stories/videos
 URLs, Headlines, etc.
 Sqoop, Custom Scripts
 Generate features for stories
 LDA from Mahout
 Custom extensions
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Building a Recommender System

15.  Identify and track users
 Registered
 Anonymous
o Cookie based tracking
o IP based tracking
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Building a Recommender System

16.  Types of user activity
 Explicit interactions
 Implicit interactions
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Building a Recommender System

17. Tracking user activity
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Building a Recommender System
Browser
(Javascript)
HTTP
Server
D
Flume HBase

18.  Tracking : Key Features
 1000s of ppm
 Asynchronous - Instantaneous responses to client
 Reliability
 Multiple HTTP Servers → Multiple Clusters
 Client to HBase in milliseconds
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Building a Recommender System

19.  Why HBase?
 Scalable
 Fault-tolerant
 Auto-sharding
 Schema-less and sparse
 Real-time queries
 MR integration
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Building a Recommender System

20.  Store user activity
 100s of millions of users
 Millions of stories/videos
 TBs of data
 Wide Tables – 1 row per user
 High load
 Sub-second response times
 Multiple MR jobs every few mins
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Building a Recommender System

21.  Generate user models using ML
 100s of millions of users
 High IO/Processing power
 Train multiple times an hour
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Building a Recommender System

22.  Content-based Recommender Models
 User model independent of other users
 Train only when user has new interaction
 Easily parallelizable
 No Reducer
 Incremental training
 Train 1000 user models a minute
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Building a Recommender System

23.  Collaborative filter based Recommender Models
 User model dependent of other users
 Train all models frequently
 Map side self join
 No Reducer
 Batch training
 Train 10s of millions of user models on each batch
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Building a Recommender System

24.  Serve recommendations
 Query HBase
 Evaluate articles against user models
 In-memory cache
 1000s of requests per minute
 50ms responses
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Building a Recommender System

25. Summary
 Recommender System are important
 Content based and Collaborative filter based
 Cross domain expertise – Big Data, Machine Learning
 Hadoop/MapReduce for offline components
 HBase as a hybrid data store
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26. Hiring
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Email: dshah100@bloomberg.net
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27. Questions?
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