Mahout Becomes a Researcher: Large Scale Recommendations at Mendeley

Mahout becomes a researcher

Kris Jack, PhD

Senior Data Mining Engineer

➔ What's Mendeley?

➔ Applications of Mahout's Recommender

➔ Under Mahout's Bonnet

➔ Mahout's Research Career so Far

➔ Conclusions

Overview

What's Mendeley?

➔ Mendeley is a data platform for researchers

➔ We're bringing together researchers and the research that they produce from all over the world

➔ We're structuring this data in a machine readable format

➔ We're opening this data up for you to build applications on top of it using our API

➔ These applications help researchers to do even better research and become more productive

➔ How are we building our community?

...organise their research

Mendeley provides tools to help users...


➔ Reference management

➔ Cite-as-you-write

➔ Full-text article search

➔ Digitalised annotations


...collaborate with one another



➔ Research network

➔ Professional research groups



...discover new research



➔ Mendeley Suggest

➔ Personalised article recommendations

➔ Weekly batch of 10 recommended articles

➔ Collaborative Filtering

➔ The more data, the better

1.5 million+ users; the 20 largest user bases:

University of CambridgeStanford University

MITUniversity of Michigan

Harvard UniversityUniversity of OxfordSao Paulo University

Imperial College LondonUniversity of Edinburgh

Cornell UniversityUniversity of California at Berkeley

RWTH AachenColumbia University

Georgia TechUniversity of Wisconsin

UC San DiegoUniversity of California at LA

University of FloridaUniversity of North Carolina50m research articles



...discover new research



We need a recommender that scales up, coping with our data and future growth

Applications of Mahout's Recommender

http://www.slideshare.net/kryton/the-data-layer

Mahout use cases:

➔ Retrieve related items in large collections

http://www.slideshare.net/kryton/the-data-layer

http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/

Mahout use cases:


➔ Discover relevant items that you may have overlooked

http://www.speeddate.com/apps/site/views/mp/technology.php

Mahout use cases:



➔ Find love!➔ Mahout implements collaborative

filtering, a surprisingly powerful algorithm

http://krisjack.blogspot.co.uk/2012/02/your-very-own-personalised-research.html

Mahout use cases:



➔ Find love!➔ Mahout implements collaborative

filtering, a surprisingly powerful algorithm

➔ Mendeley Suggest➔ Discover new research➔ Fill in gaps in your library➔ Your personal advisor

Under Mahout's Bonnet

Generating recommendations through matrix multiplication

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. Piscataway, NJ, USA.

This is item-based recommendations as similarity is based on items, not users

http://www.slideshare.net/srowen/collaborative-filtering-at-scale-2

http://krisjack.blogspot.co.uk/2012/04/under-bonnet-of-mahouts-item-based.html

Not convinced? Try reading these...

Turing Babbage Einstein Newton

Comp Sci 1

Physics 1

Res

earc

h A

rtic

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Researchers

Physics 2

Comp Sci 2

Input (all user preferences)

Turing Babbage Einstein Newton

Comp Sci 1

Physics 1

Res

earc

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Researchers

Physics 2

Comp Sci 2

1.5M

50M


300M prefs

Res

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Art

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s

Researchers

All User Preferences (item x user)

1. Prep. pref. matrix (1-3)2. Gen. sim. matrix (4-6)3. Multiply matrices (7-10)

item.RecommenderJob

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sTuring

A User's Preferences(item x user)

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Researchers



item.RecommenderJob

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sTuring


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2 11 10 00 0

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Item Similarity (item x item)


item.RecommenderJob

Res

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Art

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Researchers

Comp Sci 1

Physics 1

Res

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Research Articles

Physics 2

Comp Sci 2

Comp Sci 1Comp Sci 2

Physics 1Physics 2

2

1

2

2

1

1

2

2

0 0

0 0

0 0

0 0


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sTuring


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2 11 10 00 0

2 22 2

0 00 0

Item Similarity (item x item)R

esea

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Turing

Recommendations(item x user)

X =


item.RecommenderJob

Running on Amazon's Elastic Map Reduce

On demand use and easy to cost

Mahout's Research Career so Far

Mendeley Suggest

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No. Good Recommendations/10

Mahout'sPerformance

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Costly & Bad Costly & Good

Cheap & Bad Cheap & Good

Mahout'sPerformance

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Mahout'sPerformance

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Mahout'sPerformance

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0

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6.5K, 1.5Orig. item-based

3

Mahout'sPerformance

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Cust. item-based➔2.4K, 1.5

3

Mahout'sPerformance

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3

-4.1K(63%)

Mahout'sPerformance

Reducing processing time and cost

➔ Mahout's recommender is already efficient➔ but your data may have unusual properties

➔ We got improvements by:➔ tuning Hadoop's mapper and reducer allocation over the 10

steps in the RecommenderJob➔ using an appropriate partitioner

Task Allocation 37 hours to complete

1 reducer allocated, despite having 48 available...

Task Allocation

job.getConfiguration().set("mapred.max.split.size",String.valueOf(splitSize));

Allocating more mappers on a per job basis

job.getConfiguration().setInt("mapred.reduce.tasks",numMappers);

Allocating more reducers on a per job basis

Task Allocation 37 hours to complete14 hours

From 1 → 40 reducers

Partitioners 14 hours to complete


~50KB

~500MB

InputSampler.Sampler<IntWritable, Text> sampler =new InputSampler.RandomSampler<IntWritable, Text>(...);

InputSampler.writePartitionFile(conf, sampler);conf.setPartitionerClass(TotalOrderPartitioner.class);

http://www.philippeadjiman.com/blog/2009/12/20/hadoop-tutorial-series-issue-2-getting-started-with-customized-partitioning/


2 hours

Evenly distributed

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Mahout'sPerformance

-4.1K(63%)

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2 11 10 00 0

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item.RecommenderJob

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2 11 10 00 0

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item.RecommenderJob

user

User Similarity (user x user)

Researchers

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0

1K

2K

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Orig. user-based➔1K, 2.5

3

Mahout'sPerformance

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0

1K

2K

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3

-1.4K(58%)

+1 (67%)

Mahout'sPerformance

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0

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Cust. user-based➔0.3K, 2.5

Mahout'sPerformance

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0

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-0.7K(70%)

Mahout'sPerformance

-4.1K(63%)

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3


-6.2K(95%)

Mahout'sPerformance

+1 (67%)

Conclusions

Conclusions

➔ Mahout is doing a great job of powering Mendeley Suggest➔ Large scale data set➔ Excellent for batch processing requirements

➔ We'll soon be feeding our user-based implementation into Mahout

➔ User-based can outperform item-based➔ Makes Mahout's offering more rounded

➔ Save resources and money by understanding your data➔ Help Hadoop with task allocation if necessary➔ Paritition your data appropriately

We're Hiring!

➔ Hadoop Data Architect➔ design a coherent data model across the company

➔ take ownership of our data

➔ hands on Hadoop administration

➔ Marie Curie Senior Research Fellow ➔ ensure that Mendeley’s research catalogue is of high quality

➔ research and development opportunity

➔ £500 Finder's Fee if you find someone who we hire➔ http://www.mendeley.com/careers/

www.mendeley.com

Education

Mahout Becomes a Researcher: Large Scale Recommendations at Mendeley