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From Sally Khudairi ...@apache.org>
Subject Success at Apache: All My Roads Led to Apache
Date Mon, 02 Oct 2017 10:00:51 GMT
[this announcement is available online at https://s.apache.org/l9OO ]

by Pat Ferrel

I became involved with Apache in 2011. After several years in startups
where, as CTO, I felt too removed from building things. Looking for a
change, I was keenly aware that the most interesting thing about the
startups was our early use of Machine Learning techniques and I wanted
to see if building ML solutions, for companies new to the field might
not be more satisfying. I started by spending nearly a year in
researching the type of applications we had needed in the startups:
Natural Language Processing (NLP), text analysis, clustering, and
classification. In those days Apache Mahout http://mahout.apache.org/
had several good solutions that were designed for Big Data and
approachable by an individual. These ideas seem fairly commonplace now
but were in early days only 6 years ago.

Given a great platform to experiment with, I built a web site to
advertise expertise in ML but also to showcase many examples from my
experiments, including a topic-oriented content site based on clustered
and classified text that used NLP to add entities to text. I blogged
about things I had learned and techniques that produce results.

Then I got the first contact about a project and it was from a
completely unexpected direction: recommenders. Fortunately Apache Mahout
then had the state-of-the-art OSS suite of recommenders so I took the
consulting job. The company had rolled their own recommender and was
selling it as a service but it was old and they wanted to investigate
replacing it. 
Welcome to Big Data

The nature of recommenders means you deal with huge amounts of data
because you have to track several million people’s actions over years.
We had data from a large online retailer and were tasked with using this
data to beat the in-house recommender. Specifically they wanted to see
if they could improve performance (better results and faster compute
times) and get something easier to maintain. 

The first job of a good consultant is to define the problem and outline
a path to resolution that fits with the company’s competencies. To me
this meant looking at the current system and the expertise of the people
working on it. We had Data Scientists and Java Software Developers who
knew what it was like to deal with Big Data. They had a highly
performant method for gathering data and were quite good at running
Apache Hadoop-based analytics. This was seldom the case back then but
happily allowed me to look at less turnkey applications and assume the
use of important Apache tools.

We agreed on a plan and the basic building blocks including a method for
comparing results. I did the research and proposed several candidates
for the tests including the Apache Mahout recommenders. It was pretty
easy to rank the recommender engines we had and do some exploration of
parameter tuning and choices to get our best "challenger" results. The
nice thing is that we beat the old threadbare in-house recommender by a
significant amount (12%). The winner was the Apache Mahout Cooccurrence
Recommender using the Log-Likelihood Ratio as the core cooccurrence
metric. This even though we had tested against several Matrix
Factorization recommenders, including Mahout's. 
We need something new 

Up till this time I was only a user of Apache projects (discounting a
few minor code contributions) but what I found in all recommenders we
studied is a fundamental problem that is still mostly unsolved today. We
had data from a retailer that included user "buys" but also 100 times
more user "views". None of the recommenders could deal with this
multimodal data. I consulted the authors and maintainers of the Mahout
recommenders and several others we had targeted. We got some suggestions
added them to our own ideas and set out to test them. For various
reasons, that are beyond the scope of this post, none of the easy
solutions helped and actually produced worse results so I had fulfilled
the contract and left with a feeling of unfinished business.
One of the mentors of Apache Mahout, Ted Dunning, had suggested a new
idea during this time. There was something about it that seemed very
intriguing. He had proposed a way to use one type of user behavior to
predict another. This was an aha moment for me because it codified
intuition. I remember the first time he wrote in email on the Mahout
user mailing list the equation that crystallized it all. I began to
imagine the implications; all sorts of new data that could be useful,
not just "views" but contextual data like location, and enrichment data
like tag or category preferences. These all seem to obviously have a
bearing on recommendations but now we had a beautiful simple equation to
test the intuition.

Becoming a Committer

I set out to hack the Mahout Cooccurrence Recommender to become a
Correlated Cross-Occurrence (CCO) recommender. But without some way of
testing the algorithm and code we couldn’t be sure it was worth
including in Mahout. The datasets publicly available at the time did not
have the kind of data we needed (there had been no direct use for it
until then) so I scraped the film review site rottentomatoes.com to
collect "fresh" and "rotten" reviews of movies. This gave us two
different behaviors with very different meanings. Naively you might
think, weight one positive and the other negative and so did I but that
produced worse results than ignoring the "dislikes". However when I ran
cross-validation tests comparing the Mahout Cooccurrence Recommender
using likes only, to CCO using both user actions, we got some quite
interesting results. The question was: do "dislikes" predict "likes" and
when I got 20% lift in predictive precision we could conclude that they
do. Not only was intuition right but the new algorithm could tease out
the data to make use of it.
The hack was accepted into Mahout Examples and I was invited to become a
committer. Then the world changed.

Apache Spark and Mahout-Samsara

When I became a committer Mahout was written on Apache Hadoop MapReduce
in Java (as was my hack). But it had also become obvious to most Mahout
committers that the future was with much more performant engines like
Apache Spark. Committers Dmitriy Lyubimov and Sebastian Schelter had
been working on a Spark version of Mahout. In an instant of project time
virtually all committers saw this as the future of Mahout, if also a
major pivot. 

In retrospect I'm not sure I've ever seen an Apache project change so
much in so little time. Today Mahout is deprecating lots of old Hadoop
MapReduce code as it falls from use and the new Mahout is truly new. The
Mahout subtitle Samsara, references the cycle of life, death, and
rebirth in the Hindu tradition. Mahout started as algorithms written
specifically for MapReduce, now Mahout-Samsara is a linear algebra DSL
in Scala used to roll-your-own algorithms but with most interesting
algorithms in very simple DSL-based implementations. Mahout eventually
took this transformation even further to include other compute engines
like Apache Flink and is now running on GPUs. But I get ahead of
things...
Those were exciting times and though I helped with the DSL I remained
fixed on implementing CCO, which was first included in Mahout 0.10.0 in
October 2014.

PredictionIO

Now we have the CCO algorithm implemented on modern compute engines but
several other problems remained in order to actually deploy a
recommender. This is because CCO creates a model that needs to be
deployed on a special type of server that computes similarity in real
time. In Machine Learning terms this is a K-Nearest Neighbors engine,
known in concrete terms as Lucene, or it's scalable server derivatives
like Solr and Elasticsearch. A turnkey recommender also requires a
highly performant massively scalable DB, like HBase. Putting these
together we could get a nearly turnkey recommendation server that made
use of multimodal real time user behavior. But I didn't see a candidate
for all these in Apache and so looked elsewhere. This required an
integration project, not Mahout, which integrated with other services
but provided none of its own.
I found a project that included everything I needed and was Apache
licensed but was run by a small startup called PredictionIO. They had a
Machine Learning Server that was a framework for Templates that could
implement a wide range of Algorithms. The Server also included nice
high-level integrations with Elasticsearch (Lucene server), Spark, and
HBase. In May of 2015 I had the first running CCO Server build on Mahout
and a whole list of other Apache projects.

Back to Apache

PredictionIO was at the right place to get swept up in a major move to
embrace ML/AI by Salesforce Inc. who bought them as part of the Einstein
initiative. Since PIO was Apache licensed OSS it was still available and
so was the Template I was calling the Universal Recommender. But there
was a question now about the future of PIO; what would Salesforce do
with it? The old team, that I had worked closely with, wanted to see the
project move forward in OSS and Salesforce seemed to agree, but large
corporations often have a mixed record in promoting their own OSS
projects. In this case Salesforce decided to remove the question by
submitting PredictionIO to the Apache Incubator.

The old team was joined by people like me from outside Salesforce to
create a project that follows the Apache Way and is free of corporate
dominance. I am a committer to PredictionIO, which has three releases
under Apache Incubator vigilance and the Universal Recommender is now at
v0.6.0, the most popular of PredictionIO Template Algorithms.
With the 3rd release of PIO from Apache we are now in the process of
graduation to an Apache Top-Level Project, hatched by the Apache
Incubator. I fully expect that we'll be celebrating soon.

Postscript

My journey began with a specific problem to solve. Each step to produce
the solution has led back to Apache in one way or another, through
mentors, collaboration, use of, and commitment to several projects. But
I now have my mature scalable, performant, state-of-the-art nearly
turnkey Universal Recommender.  Now we can ingest and get improvements
from many types of behavior, enrichment data, and context--using it in
real time to serve recommendations subject to robust business rules. My
small consulting company ActionML actionml.com now has a powerful tool
to solve real problems and we make a living (at least partly) by helping
people deploy and tune it for their data.
This is a story of someone single mindedly following a goal over several
years. There are many ways to do this in the Software Development world,
but not all OSS projects are open to bringing people in. The Apache
Software Foundation most certainly is and openly recruits as diverse a
group of committers and members as possible. If you want to make a
difference and influence the course of an OSS project Apache is a good
place to look. Start by getting involved with a project of interest,
make contributions, get involved in discussions. If the match is good
you'll be invited in as a committer and move on from there. I think of
Apache as a do-ocracy, if you do something of value it goes a long way
towards being invited in.  

References

Slides describing the CCO Algorithm:
https://www.slideshare.net/pferrel/unified-recommender-39986309

IBM DevWorks Post on "Making one thing Predict Another":
https://developer.ibm.com/dwblog/2017/mahout-spark-correlated-cross-occurences/

Apache Mahout CCO Implementation:
http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html

Apache PredictionIO: http://predictionio.incubator.apache.org/

The Universal Recommender Template:
http://predictionio.incubator.apache.org/gallery/template-gallery/

Professional Support for the Universal Recommender:
http://actionml.com/universal-recommender

# # #

"Success at Apache" focuses on the processes behind why the ASF "just
works". 1) Project Independence https://s.apache.org/CE0V 2) All Carrot
and No Stick https://s.apache.org/ykoG 3) Asynchronous Decision Making
https://s.apache.org/PMvk 4) Rule of the Makers
https://s.apache.org/yFgQ 5) JFDI --the unconditional love of
contributors https://s.apache.org/4pjM 6) Meritocracy and Me
https://s.apache.org/tQQh 7) Learning to Build a Stronger Community
https://s.apache.org/x9Be 8) Meritocracy. https://s.apache.org/DiEo 9)
Lowering Barriers to Open Innovation https://s.apache.org/dAlg

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