How the power of graphs helps deliver Packt's strategic vision
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Transcript of How the power of graphs helps deliver Packt's strategic vision
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