Recommendation and Discovery for Digital TV and Media

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1 Recommendation and Discovery for Digital and Media 1

2 The «Discovery challenges»: 3 perspectives The «User» Wants interesting and relevant content through a natural, effortless interaction for a superior user experience The Operator Wants to attract and keep viewers, optimizing the monetization of licensed assets, through efficient and lean operations. Content The IT System Must provide top-level performance and scalability, end-2-end support for operations and seamless integration into the existing IT ecosystem 2

3 The ContentWise products suite ContentWise Discovery Engine «Netflix in a box» one-stop-shop for all «content discovery» use cases ContentWise Knowledge Factory «Metadata on steroids» data collection, mash-up, reconciliation, entity recognition, enrichment, semantic reasoning 3

4 User-centric discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Editorial Semantic Gamification Personalization Personalized User Preferences Multi-Profile Cross-Device Context Aware 4

5 Operator-centric discovery (re)defined Service Modeling Metadata management IT Ops A/B Testing Business Rules Analytics Publishing Device (context) adaptation 5

6 «Metadata on steroids» Knowledge Factory (KF) Next-generation metadata Issue: metadata is language-bound and sparse language-independent mapping metadata augmentation, reconciliation, aggregation / deep linking, tagging Knowledge-graph Issue: traditional metadata is boring semantic reasoning contextual & crossdomainn recs Next generation Search Issue: you cannot «google» with a remote «knowledge graph» search natural language search User data «mashups» Issue: no obvious questions please! user profile augmentation user context (FB s what s on your mind ) 6

7 Discovery is NOT an option A must-have business tool for operators. Engagement lift of ~15 to 25% Customer revenue lift of ~35% Churn reduction of ~40% *300+kusers, ARPU 3.5 EUR monthly, 20% GM, +15% reco revenue lift 7

8 The User perspective Relevant content and superior user experience 8

9 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Editorial Semantic Gamification Personalization Adaptive User Preferences Multi-Profile Cross-Device Context Aware 9

10 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Editorial Semantic Gamification Personalization Adaptive User Preferences Multi-Profile Cross-Device Context Aware 10

11 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Editorial Semantic Gamification Personalization Adaptive User Preferences Multi-Profile Cross-Device Context Aware NetGem ContentWise Confidential 11

12 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Editorial Semantic Gamification Personalization Adaptive User Preferences Multi-Profile Cross-Device Context Aware 12

13 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Static Semantic Gamification Personalization Adaptive User Preferences Multi-Profile Cross-Device Context Aware 13

14 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Static Semantic Gamification Personalization Adaptive User Preferences Multi-Profile Cross-Device Context Aware 14

15 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Static Semantic Gamification Personalization Adaptive User Preferences Multi-Profile Cross-Device Context Aware 15

16 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Multi-modal Search Content-based Collaborative Social Engagement Assisted Browsing Static Semantic Gamification Adaptive User Preferences Multi-Profile Cross-Device Context Aware 16

17 Discovery (re)defined Cross-service Video On-Demand Linear Catch-up Personal Video Recorder Search Content-based Collaborative Social Engagement Assisted Browsing Static Semantic Gamification User Preferences Multi-Profile Cross-Device Context Aware 17

18 The Operator perspective Leveraging discovery to increase business performance 18

19 The «Operator» perspective: monetization A set of integrated business tools under a unified administration console Administration Console Analytics Optimization (A/B testing) Event Processing API Data Management Recommender Business Rules Device Adaptation Knowledge Factory Task-flow scheduler External sources ingestion Document store Semantic Store (Linked Data) Semantic Enrichment 19

20 Business Rules Control recommendations to optimize specific business objectives on demographic, pricing, behavioral and time-of-day criteria. Actions Filter- Filter in/out content Push - Boost selected content Update - Change priorities Balance - Increase recommendation variety Business Rules Rule = execute an Action on items satisfying a For users within a Condition, Target Target Demographic criteria Taste preferences Behavioral criteria Condition Metadata : genre, cast, rating, publisher Time campaign period, time of day License type and status Type of content: Live, VOD, Pricing: upper/lower/band price 20

21 Layouts Layouts allow complex presentation strategies that stimulate discovery and exploration along different paths Device Adaptation Definition Result Slot 1 Top Rated Slot 2 Most popular Others Personalized 21

22 Analytics Actionable analytics cover all relevant dimensions of a discovery engine performance and its effect on the business KPI «families» Analytics Dimensions Service Content User Activity Financials Engagement Effectiveness Real-time Interactive exploration by: time device / application Audience Content Planning Business Rules «open» to external BI Tools BIRT operational reporting Search OLAP 22

23 Analytics: few examples 23

24 The IT System perspective Strong architecture, integration and control 24

25 Deployment and scalability Standard 3-tier J2EE application: Frontend Rec servers : manage the Web Services layer and in-memory real-time recommendations generation Backend Proc servers : gather and manage the data, create recommendation models Backend Reporting servers : manage the warehouse processes and the Analytics portal Database: MySql RDBMS for operational and analytical data stores Built for scalability: Multiple Rec servers : a load balancer shares the load among multiple front-end servers. Backend Proc servers : parallel computation (MPI) to speed up data ingestion and model computation Database: MySQL cluster Standard HW/SW: Linux Red Hat/CentOs servers Virtualization supported Jboss container 25

26 Deployment options Cloud «reserved» Cloud «multi-tenant» On Premises infrastructure Dedicated instances Multi-tenant «share-all» custom target Tier 1-2 operators Small MSOs Tier 1-2 operators support (5x8, 24x7) platform management data management on request on request on request business operations on request on request on request Flexible deployment options to target specific markets and market tiers 26

27 27

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