hail, hybrid! a better recommendations engine...trends social graphs ... the recommendations engine...
TRANSCRIPT
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HAIL, HYBRID!
May 2019
A BETTER RECOMMENDATIONS ENGINE
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WHAT WE’LL COVER TODAY
• Who is SoftServe
• Why We Started This Journey
• The Modern Hybrid Recommendations Engine
• Data Science Behind It
• What We Learned
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8,000+EMPLOYEES
4,500+LARGE SCALE CUSTOMER PROJECTS
Europe & USA
30+OFFICES
6,000+SOFTWARE ENGINEERS
With SoftServe Clients
12COUNTRIES
700+CLOUD EXPERTS
300+ Cloud DevOps Focused
We hire 10-12 daily
Founded in 1993
25YEARS BUILDING ENTERPRISE SOFTWARE
70NET PROMOTER SCORE (NPS)
More than twice as high as
our competition
ABOUT SOFTSERVE
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KEY TECHNOLOGIES AND PRACTICE AREAS
experience design
cybersecurity
mixed reality
internet of things
cloud/devops
blockchain
big data, artificial intelligence, and machine learning
digital experience platforms
robotics
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PROBLEMS WE SOLVE IN MEDIA
Video Content Supply Chain Capture and Ingest
Analyze and Manage
Transcode and Distribute
Data AnalyticsData Workflow / Data Pipeline Design
Business Solutions (recommendations, churn prediction, attribution)
Machine Learning & AI
DevOps and AutomationServerless Architecture
Cloud Native and Containerization
Cloud Based WorkflowsRendering Farms
Transcoding
MAM / DAM Solutions
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WHY WE EMBARKED ON THIS JOURNEY
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DATA TYPES
Content Data
Player Data
Geographic Data
Conversion Data
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I AM AN INDIVIDUAL
NOT A DEMOGRAPHIC
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INTEGRATED DATA TYPESTrends
Social graphs
Linear past consumption habits
Interests in celebrities, topics, genre, etc…
Gender
Demographics
Time user is active
Device type(s)
Other interests
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SoftServe Confidential
Innovative retail personalization approach that leverages OCEAN psychometric concept to help retailers identify customer personality profiles and suggest the best way to communicate and engage with the consumers.
CONSUMERS PSYCHOMETRIC PROFILING
LEARN MORE
Shopping Habits Verbatim Datafrom Social Media
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It combines data analytics with Ocean/BIG5 approach and technology to reach a deep understanding of the customer.
BIG FIVE PERSONALITY TRAITS
Openness
Extent to which person is open to experiencing a variety of activities
Conscientiousness
Person’s tendency to act in an organized or thoughtful way
Agreeableness
Person’s tendency to be compassionate and cooperative towards others
Neuroticism
Person’s emotions are sensitive to the individual’s environment
Extraversion
Person’s tendency to seek stimulation in the company of others
penness
onscientiousness
xtraversionAgreeableness
Neuroticism
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WHAT PROBLEMS ARE WE SOLVING
Customers have more choices than ever to watch content
Keep them on your platform, and keep them engaged and coming back.
Create a competitive advantage
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CREATING A COMPETITIVE ADVANTAGERevenue Driven
Customer Experience Driven
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CREATING A COMPETITIVE ADVANTAGE
1. Increase viewer engagement
• Longer time spent viewing content on platforms
• Increase advertising impressions
2. Change viewer behavior
• Change from passive viewer to engaged viewer
• Engage in a fair exchange of data for a better experience
• Be willing to accept recommendation. Build trust on content
Revenue Driven
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CREATING A COMPETITIVE ADVANTAGE
1. The ability to identity viewers by persona along with associated content tastes, preferences, and viewing habits
2. The ability for business users to glean key insights from data through intuitive visuals and other mechanisms that enable them to make business decisions
Customer Experience Driven
3. Reactivate dormant viewers
• Have the ability to make tailored recommendations to viewers who signed up to watch a specific piece of content
• Drive platform adoption and position provider as a source of entertainment across multiple genres
Revenue Driven Cont….
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HAILHYBRID.
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How Does It All Work?
Mixture of Recommenders (Unlimited)
• Content-Based
• Collaborative-Based
• Hybrid
User Profile
• User Feedback
• User Interaction
Global Probabilistic Electors (Thompson Sampling)
Recommendation Engine Components
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THE MATH BEHIND IT ALL
• In artificial intelligence, Thompson sampling is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi one-armed bandit problem
• It consists in choosing the action that maximizes the expected reward with respect to a randomly drawn belief
• It has been proved that Thompson sampling is instantaneously self-correcting.
Thompson Sampling
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DATA SCIENCE ALGORITHMS IN ACTION
Recommendation Service
Applications
Kubernetes Cluster
CSV Data
StreamingData
Structured Data
Data Processing
Data Processing
ML Algorithm 1
ML Algorithm 2
ML Algorithm 3
ML Algorithm N
Recommendation Service
Recommendation Service
Recommendation Service
Application Insights Component
(e.g. Google Stackdriver)
RecommendationOnline Learning
Agent(Thompson Sampling)..
.
...
...
...
Time SeriesTables
ApplicationGatewayData
Processing
Data Processing
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WHY HAIL HYBRID?
• The engine can evaluate an unlimited number of data sources used in the recommenders – machine learning algorithms that compare data sets, user actions, social activity, retail data, etc.
• All of these various recommenders are processed and sent to the Recommendation Learning Agent to use probability sampling algorithms in order to find the most likely matches
• As this process is repeated over time, the data and machine "knowledge" becomes even more accurate and rewarding
Hybrid Approach
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MAKING IT PERSONAL
• User feedback such as ratings and likes
• Direct user activity with the application/recommendation engine
• Consumption habits – duration and time of day
• Social data
• Retail data
• Geographic/demographic data
User Insights
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MAKING IT OPEN & SCALABLE
• Taking a containerized approach allowed extreme burst capacity enabling us to chunk through huge amounts of data in micro-seconds and even nano-seconds
• The architecture is built on an open source codebase and open and with open source technologies. The recommendations engine framework does not need to be purchased or licensed. It is also cloud agnostic
• The inputs are unlimited and customizable. You can perfect the engine to your customer base or target viewership based on data inputs and algorithm calculations
Additional Highlights
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KEY TAKEAWAYS
• An incredible amount of data is available: Put it to use
• Transparency and Trust: People want to know why recommendations were made and what data was used
• You’ll need a test group: Be nice to your friends and co-workers
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