Large-scale Parallel Collaborative Filtering and Clustering using MapReduce for Recommender Engines
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Large-scale Parallel Collaborative Filtering and Clustering using MapReduce for Recommender Engines
Varad MeruSoftware Development Engineer,Orzota, Inc.
© Varad Meru, 2013
+Outline
Introduction
Introduction to Recommendation Engines
Algorithms for Recommendation Engines
Challenges in Recommendation Engines
What is Hadoop MapReduce?
What is Netflix prize?
Block diagram
System requirement
Conclusion
© Varad Meru, 2013
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Recommender SystemsIntroduction and Project Scope
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+Introduction
Scope of our project is to build a Recommender Engine using Clustering.
Recommender Engine are used in E-Commerce and other settings to recommend items to the end users.
Widely used in companies such as Amazon, Netflix, Flipkart, Google News, and many others.
Collaborative Algorithms, Clustering and Matrix Decomposition is used for finding Recommendations.
© Varad Meru, 2013
+Recommender System Example
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+ Some other Recommender SystemsHere are some snapshots of widely used recommendation engines used in Amazon.
© Varad Meru, 2013
+Collaborative Filtering in Action
Assuming is Every one of the names have seen any of the above movie
Let 1 denote seen
Let 0 denote not seen
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+Collaborative Filtering in Action
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+Collaborative Filtering in Action
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+MinHash Clustering in Action
We will be implementing a variation of algorithm for our Project
It’s a technique to findout how similar two sets are.
The scheme was invented by Andrei Broder (1997)1
The simplest version of the minhash scheme uses k different hash functions, where k is a fixed integer parameter, and represents each set S by the k values of hmin(S) for these k functions.
Google is known to have used this method to cluster news articles for recommending users the news of their tastes2
1Broder, Andrei Z. (1997), "On the resemblance and containment of documents”.2Mayur Datar et. al. (2007), "Google News Personalization: Scalable Online Collaborative Filtering”.© Varad Meru, 2013
+MinHash Clustering Flow
Get a Random Permutation of Product
Catalog, R
Start
Define a hash function h such that
h(Ui)=min. ranked product in R
Ui : All the Interaction performed by the User.An Interaction can be a Click, Purchase, Like, etc.
Pass each user through the Hash function to get
the Cluster Number
After the Clusters have been formed, Use
Covisitation to find out Recommendations
Stop
Cache the Recommendations in
Memory
Memory
© Varad Meru, 2013
+Some Recommender Systems Available
Apache Mahout1
Easyrec2
University of Minnisota’s SUGGEST3
Other, for research, implementations such as UniRecSys and Taste
1 http://mahout.apache.org2 http://easyrec.org/ 3 http://www-users.cs.umn.edu/~karypis/suggest/
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MapReduce ParadigmMapReduce and Hadoop
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+MapReduce Programming Paradigm
A core idea behind MapReduce is mapping your data set into a collection of Key-Value pairs, and then reducing over all pairs with the same key.
Hadoop MapReduce is an Open Source implementation of MapReduce framework on the lines of Google’s MapReduce software framework.
Used for writing applications rapidly process vast amounts of data in parallel on large clusters of compute nodes.
A Hadoop MapReduce job mainly consists of two user-defined functions: map and reduce.
© Varad Meru, 2013
+map() function
A list of data elements are passed, one at a time, to map() functions which transform each data element to an individual output data element.
A map() produces one or more intermediate <key, values> pair(s) from the input list.
k1 V1 k2 V2 k5 V5k4 V4k3 V3
MAP MAP MAPMAP
k6 V6 ……
k’1 V’1 k’2 V’2 k’5 V’5k’4 V’4k’3 V’3 k’6 V’6 ……
Input list
Intermediate output list
© Varad Meru, 2013
+reduce() function
After map phase finish, those intermediate values with same output key are reduced into one or more final values
k’1 V’1 k’2 V’2 k’5 V’5k’4 V’4k’3 V’3 k’6 V’6 ……
Reduce Reduce Reduce
F1 R1 F2 R2 F3 R3 ……
Intermediate map output
Final Result
© Varad Meru, 2013
+Parallelism
map() functions run in parallel, creating different intermediate values from different input data elements
reduce() functions also run in parallel, working with assigned output key
All values are processed independently
Reduce phase can’t start until map phase is completely finished.
Its in a way, data parallel implementation and thus works with humongous amount of data.
© Varad Meru, 2013
+Hadoop
Started by Doug Cutting, and then carried ahead by enterprises such as Yahoo! and Facebook
It’s a collection of three frameworks – Commons, MapReduce and DFS.
Free and Open Source with Apache Software License
Current Largest Cluster size of 4000 nodes. ( at Yahoo! )
Whole Ecosystem build around it to process large amounts of data. (~in GBs, TBs, PBs)
© Varad Meru, 2013
+Evaluation of Recommendation EngineNetflix and Comparison with other frameworks
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+Netflix Dataset
This dataset was release by Netflix October 2, 2006 for SIGKDD challenge to build worlds best recommender for Netflix.
Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies.
Each training rating is a quadruplet of the form <user, movie, date of grade, grade>
Used heavily in Research for Recommender Engine1.
Used in our project to compare the Implementation of our Algorithm with other implementations e.g. Mahout
1Google Scholar : About 3,190 results for the search term “netflix prize”© Varad Meru, 2013
+High-level Architecture
MapReduce implementation of Clustering algorithms such as K-Means and MinHash Clustering.
Comparative Analysis with already present frameworks such as Apache Mahout (Refer Reference no. 1, 2, and 3)
© Varad Meru, 2013
+Requisites
2 Linux Machines (Required, preferred OS - Ubuntu)
Pentium 4 + Machines (Recommended – Core 2 Duo 2.53 GHz+)
RAM 1 GB per machine (Recommended – 4 GB per machine)
Apache Hadoop (from http://hadoop.apache.org )
Apache Mahout (from http://mahout.apache.org)
Java IDE ( Eclipse, Preferred)
Java SDK1.6+
© Varad Meru, 2013
+References
1. “Scalable Similarity-Based Neighborhood Methods with MapReduce” by Sebastian Schelter, Christoph Boden and Volker Markl. – RecSys 2012.
2. “Case Study Evaluation of Mahout as a Recommender Platform” by Carlos E. Seminario and David C. Wilson - Workshop on Recommendation Utility Evaluation: Beyond RMSE (RUE 2012)
3. http://mahout.apache.org/ - Apache Mahout Project Page
4. http://www.ibm.com/developerworks/java/library/j-mahout/ - Introducing Apache Mahout
5. [VIDEO] “Collaborative filtering at scale” by Sean Owen
6. [BOOK] “Mahout in Action” by Owen et. al., Manning Pub.
© Varad Meru, 2013
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Thank You
© Varad Meru, 2013
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