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Tailoring Video Streaming Service With Data

Tailoring Video Streaming Service With Data

The story about how KKStream (KKBOX Group) start apply data to improve the service quality. This presentation was given at AWS Taipei Summit on June 28, 2018.

Jing-Kai Lou

June 28, 2018
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  1. © 2018, Amazon Web Services, Inc. or its affiliates. All

    rights reserved. Tailoring Video Streaming Service with Data Jing-Kai Lou, PhD Data Scientist, KKStream, KKBOX Group June, 2018
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    rights reserved. KKStream initiated as KKBOX Video Department, spin off in Mar 2016 au Video Pass (Japan), KKTV (Taiwan), giloo (Taiwan), and more coming...
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    rights reserved. Confront Many Challenges How to organize content personally for each individual?
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    rights reserved. Turn Challenges Into Opportunities People who watch “Midnight Diner (深夜食堂)” also watch? Solutions GET APIs Data Service: finding similar items PUT APIs
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    rights reserved. What is Video Streaming Service?
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    rights reserved. Pick (content) Pack (encoding) Deliver (CDN) Engage (storefront) Consume (video player)
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    rights reserved. What Data Can Support Us? Pick (content) Pack (encoding) Deliver (CDN) Engage (storefront) Consume (video player) social listening, ... perceptual video encoding optimization, ... CDN routing, ... video recommender system, ... adaptive streaming, ... powered with data
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    rights reserved. Ongoing Journey We develop ML models and features along with operating VOD service. Democratization and Gamification “More like this” on KKTV Intelligent Notification “Discover more” on KKTV Content understanding
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    rights reserved. Data Democratization and Gamification
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    rights reserved. Intelligent Notification Time prediction AUC 0.88 Engagement +2% Notify according to watching habits
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    rights reserved. More like this CTR +13% 深夜食堂 seed more like this Organize content using behaviors
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    rights reserved. First bite +7% Discover more Offer feedbacks after interactions
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    rights reserved. Rich Display Motivate Users to Act Avoid blur images Focus on faces (sharpness and position) Automatic Extraction
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    rights reserved. 66% win ratio lessons learned • protagonists are preferred • posture matters Blind Test Result over 150 subjects, 50 episodes
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    rights reserved. How We Run Projects
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    rights reserved. Roles Make Data Projects Run • Project owner
 represent the business interest; champions of product • Client
 represent end-user interest; domain expert • Data scientist
 set and execute analysis strategy; communicate with owner and client • Data architect
 manage data and data storage; sometimes manage data collection • Operations
 manage infrastructure; deploy final project result
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    rights reserved. Data Project Steps 1. Communication / Request 2. Understand / Hypothesis 3. Data Collection 4. Data Exploration 5. Transformation 6. Model 7. Validation and Evaluation 8. Deployment 9. Operation • Project owner, Client • Data architect • Data scientist • Operations
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    rights reserved. Well-Organized Data Store is Desired
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    rights reserved. Our Data Flow Ingestion Modeling Transform Deployment
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    rights reserved. Takeaway 挑戰同時是機會,我們⾯面臨臨的恰好正是其他⼈人想解決的問題。 SageMaker 的出現,表⽰示⼀一股趨勢:「⼀一群資料科學家正苦思 如何加速落落實點⼦子」。
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    rights reserved. Thank You! [email protected] Find “KKStream” on