How the Power of Graphs Help Packt Deliver our Strategic Vision

Greg RobertsTwitter: @GregData

How graphs have helped us learn more about our market than we previously

thought possible.

Who am I?Background in Physics and Maths

Joined Packt after graduating

Worked in products before moving into marketing

Packt PublishingSoftware textbook publishing company

Over 3,500 BooksOver 150 VideosOver 6,000 Blogs and Articles

Lots of data to analyse!

Focus on PracticalityActionable KnowledgeEmerging technologies

Subscription platform: Packtlibover 10,000 active users

Upsell Recommendations - VBA● Basket Analysis● Affinity matrix in

Excel● VBA Macro (gross)

Analyse one month of data: 3 minutes

Upsell Recommendations - Vanilla.py● Basket Analysis● Affinity matrix in Numpy array● Optimise to one pass-through

Analyse one month of data: 3 seconds

Upsell Recommendations - Neo4j● Baskets are

Nodes● Affinity matrix

is a Traversal● Already

optimised!!

Analyse onemonth of data: 30 milliseconds

Recommendations are just the beginning

Graphs are an amazing tool for thinking about dataEverything’s a graph

Talk StructureThe data modelInto ProductionApplicationsThe future

What do we know so far?Some products are related to others…...

What does that mean?

Content Filtering - What’s it all about?

● Requires robust metadata

Takes Cost / Time / Resource / Errors

Automate!

How do you automate metadata generation?

Stick it all in the graph!

>10M questions~60k distinct tagsVast coverageReal world usesExcellent API!

Generating a Topic Network

StackOverflow.com

Generating the metadata

Text Extraction

Tag Extraction

Getting More Information: tfidf

Tf_idf : Gives more information!

Moving to an Ontology

What have we achieved?Model so far:

Customer touchpointsAll Content & metadataTopics and their dependencies

How do you add value?

We need to go deeper

Learning Neo4J (again)

Python Machine Learning

Spreading ActivationPython

Machine Learning

KerasScikit-Learn

0.6 0.5

Python

0.2

Spread S

pread

0.8

GPU0.4

How do people consume textbooks?

What have we achieved?Graph Contains:

All content down to the page levelTopics (Stack Overflow)Key Concepts (Ontology)Topic RelationshipsAll touchpointsContent Consumption

Let’s put it into action

Production Environment

Actual Production Environment

Everything is a recommendation

Tech Pages

● Automatic Categorisation● Dynamic filtering

● Drives discovery● Boosts engagement

Customer Profiles

Greg

PythonVBA

Excel

Sigma.js

Neo4j

pandas

?

Greg

PythonVBA

Excel

Sigma.js

Neo4j

Customer Profiles

Ted

RVBA

Excel

Linkurious

Neo4j

RStudio

Apache Spark

The FutureGraph Based Search

FlexiblePowerfulPersonalised

Modularised ContentFocusedConciseBespoke learning paths

Skill MappingProgression TrackingPeer ComparisonCareer development

Everything is a GraphThinking with graphs helps you understand your data better.

Graphs are a natural way of thinking about connected data

Extensible Schema allows for rapid iteration of models

Performance allows you to focus on the important problems