李仁杰/ Riot Games Head of Data Science
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李仁傑，畢業於中國科學技術大學少年班, 美國 University of Rochester 博⼠。現為 Riot Games 數據科學總負責人。致力於通過開發創新的數據產品來提高用戶的體驗，甚至人類的生活。從⽆到有建立了Riot Games Data Science 部⻔, 帶領團隊從《英雄聯盟》的海量數據平台中用大數據分析大規模提升商業決策和運營效率, 並用機器學習算法開發了精細化運營, 精準分析預測, 個性化推薦, 自然語⾔處理等各種全球領先的數據產品。
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李仁杰/ Riot Games Head of Data Science
- 1. DATA SCIENCE RENJIE LI AT RIOT GAMES 07.16.2016
- 3. BASKETBALL LEAGUE OF LEGENDS
- 5. Players & Data W O R L D W I D E Statistics released Jan 2014 67+ million monthly active players 500+ billion data points per day 26 petabytes data collected since beta
- 6. DATA SCIENCE AT RIOT GAMES EMPOWER RIOTERS TO MAKE BETTER DATA POWERED PRODUCTS TEAM MISSION
- 8. DATA SCIENCE AT RIOT GAMES PLAYER FOCUSED DATA INFORMED, NOT DATA DRIVEN TEAM PHILOSOPHIES
- 9. DATA DRIVEN INFORMED DECISION MAKING DATA DRIVEN DATA INFORMED
- 10. DATA SCIENCE AT RIOT GAMES Data Science
- 11. DATA SCIENCE AT RIOT GAMES Data Science RiskMarketing Ecommerce
- 12. DATA SCIENCE AT RIOT GAMES Data Science Social Play RiskMarketing Ecommerce
- 13. DATA SCIENCE AT RIOT GAMES Data Science Social Play Risk Match Making Marketing Ecommerce
- 14. DATA SCIENCE AT RIOT GAMES Data Science Social Play Risk Match Making Player Onboarding Marketing Ecommerce
- 15. DATA SCIENCE AT RIOT GAMES Data Science Social Play AI Risk R & D Match Making Eco System Player Support Player Onboarding Game Balance Design Marketing Ecommerce
- 16. CASE STUDY #1 UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR
- 17. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR CHAMPION PLAY PATTERNS
- 18. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR CHAMPION PLAY PATTERNS
- 19. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR CHAMPION PLAY PATTERNS TYPE A CHAMPION TYPE B CHAMPION TYPE C CHAMPION TYPE E CHAMPION TYPE F CHAMPION TYPE G CHAMPION TYPE D CHAMPION 7 KEY TYPES OF CHAMPIONS OUR DESIGN PHILOSOPHY ALIGNS WITH PLAYERS PLAY STYLES
- 20. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR 0% 10% 20% 30% 40% 50% 60% Type A Champion Type B Champion Type C Champion Type D Champion Type E Champion Type F Champion Type G Champion Player 1 Player 2 PLAYER SEGMENTATION BASED ON HOW THEY PLAY EACH TYPE OF CHAMPION
- 21. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR PLAYER SEGMENTATION BASED ON HOW THEY PLAY EACH TYPE OF CHAMPION 9 DIFFERENT CHAMPION PLAY BEHAVIOR
- 22. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR Type A Type B Type C Type D Type E Type F Type G IDEAL VS. CURRENT DISTRIBUTION OF CHAMPION CLUSTERS CURRENT IDEAL OVERSERVEDUNDERREPRESENTED
- 23. CASE STUDY #1 TAKEAWAYS CERTAIN CHAMPION ARCHETYPES MAY BE UNDERREPRESENTED OR OVERSERVED HOW DIFFERENTLY PLAYERS PLAY OUR CHAMPIONS WHETHER OUR DESIGN PHILOSOPHY ALIGNS WITH PLAYERS’ PLAY STYLES CHAMPION PLAY PATTERN MODEL HELPS US BETTER UNDERSTAND:
- 24. CASE STUDY #2 UNDERSTANDING PLAYER ENGAGEMENT
- 25. UNDERSTANDING PLAYER ENGAGEMENT week 1 week 2 week 3 week 4 week 5 week 6 week 7 week 8 week 9 week 10 week 11 week 12 week 13 week 14 week 15 week 16 week 17 week 18 week 19 week 20 week 21 week 22 week 23 week 24 Actual TOTALHOURSPLAYED EVENTB EVENTC EVENTD EVENTE EVENTA
- 26. UNDERSTANDING PLAYER ENGAGEMENT week 1 week 2 week 3 week 4 week 5 week 6 week 7 week 8 week 9 week 10 week 11 week 12 week 13 week 14 week 15 week 16 week 17 week 18 week 19 week 20 week 21 week 22 week 23 week 24 Predicted TOTALHOURSPLAYED EVENTB EVENTC EVENTD EVENTE EVENTA
- 27. UNDERSTANDING PLAYER ENGAGEMENT week 1 week 2 week 3 week 4 week 5 week 6 week 7 week 8 week 9 week 10 week 11 week 12 week 13 week 14 week 15 week 16 week 17 week 18 week 19 week 20 week 21 week 22 week 23 week 24 Predicted Actual TOTALHOURSPLAYED EVENTB EVENTC EVENTD EVENTE EVENTA
- 28. CASE STUDY #2 TAKEAWAYS PROVIDE INSIGHTS FOR STRATEGIC DECISION MAKING AND PLANNING QUANTIFY EFFECT OF EVENTS AND CHANGES IN THE GAME FIND AND UNDERSTAND IMPORTANT FACTORS THAT PLAYERS ARE INTERESTED IN WEEKLY ENGAGEMENT PREDICTION MODEL HELPS US :
- 30. CASE STUDY #3 PERSONALIZED RECOMMENDATION
- 31. PERSONALIZED RECOMMENDATION MANY ENTERTAINMENT SERVICES AND ECOMMERCE COMPANIES HAVE MADE RECOMMENDER SYSTEM A PROMINENT PART OF THEIR WEBSITES GOOD RECOMMENDATIONS ADD ANOTHER DIMENSION TO THE USER EXPERIENCE AND BOOST CONTENT ENGAGEMENT
- 32. PERSONALIZED RECOMMENDATION GOOGLE NEWS: RECOMMENDATIONS GENERATE 38% MORE CLICKTHROUGH NETFLIX: 66% OF THE MOVIES WATCHED ARE RECOMMENDED AMAZON: 35% SALES ARE FROM RECOMMENDATIONS
- 37. PERSONALIZED RECOMMENDATION SIMILAR RECOMMENDATION The recommender system is a predictive model that generates recommendations based on similarities between users and items as well as user-item interaction.
- 41. CASE STUDY #4 INSTANT FEEDBACK SYSTEM
- 42. TRIBUNAL
- 45. INSTANT FEEDBACK SYSTEM TRIBUNAL NATURAL LANGUAGE PROCESSING+
- 46. INSTANT FEEDBACK SYSTEM SUPPORTS 14 DIFFERENT LANGUAGES INCREASES CHATLOG COVERAGE FROM 10% TO 100% DECREASES DETECTION TIME FROM WEEKS to 15 MINS
- 47. DATA SCIENCE AT RIOT GAMES PLAYER FOCUSED DATA INFORMED, NOT DATA DRIVEN TEAM PHILOSOPHIES EMPOWER RIOTERS TO MAKE BETTER DATA POWERED PRODUCTS TEAM MISSION
- 48. DATA SCIENCE Renjie Li rli@riotgames.com THANK YOU! TW OFFICE Wayne Lee Julia Su walee@riotgames.com jsu@riotgames.com
- 49. QUESTIONS?