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如何使观众读者们满意
poppy
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我们的任务是通过利用我们每天制作的数百篇原创文章、视频和图形来最大限度地吸引我们的社交媒体受众。拥有庞大的社交媒体分销渠道网络,我们拥有一支由人类策展人组成的团队,他们精心挑选对每个渠道的受众来说最相关和最有趣的内容。当然,随着我们的内容生产规模的扩大,策划变得越来越困难——越来越多的内容从裂缝中溜走,没有达到他们的全部潜在受众。为此,我们建立了一个由自然语言处理和深度学习技术驱动的策展服务,
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1 .Matchmaking Audiences to Content Lucy X. Wang, BuzzFeed #AI8SAIS

2 .BuzzFeed #AI8SAIS 2

3 .#AI8SAIS 3

4 .A vast network of social channels • 10+ platforms • 400+ accounts • 1200+ pieces of content published a week #AI8SAIS 4

5 .But how do we scale our curation? #AI8SAIS 5

6 .By mimicking human curation #AI8SAIS 6

7 .3 use cases for AI 1. Surface relevant content for each channel 2. Surface evergreen content 3. Automate publishing #AI8SAIS 7

8 .Case 1: How do we recommend relevant content for each channel? #AI8SAIS 8

9 .Content too often slips through the cracks #AI8SAIS 9

10 .Training on human curation data #AI8SAIS 10

11 .A language-based relevance model #AI8SAIS 11

12 .Predicting relevance from titles #AI8SAIS 12

13 .Consolidated into one model #AI8SAIS 13

14 .Examples #AI8SAIS 14

15 .Impact unlike rate remains stable #AI8SAIS 15

16 .Case 2: How do we find evergreen content to re-circulate? #AI8SAIS 16

17 .Taking advantage of evergreen posts 2016 2018 55K clicks 36K clicks #AI8SAIS 17

18 .A model based on content and traffic performance #AI8SAIS 18

19 .Not much human curation data #AI8SAIS 19

20 .Our labelling proxy timely evergreen #AI8SAIS 20

21 .Handling poorly-labeled data timely? #AI8SAIS 21

22 .Modeling with uncertainty ! 1 !"#$"%&'() = + + 234 ,(.( )+ +,(.( ) + #AI8SAIS 22

23 .Examples #AI8SAIS 23

24 .Impact median clicks per post doubled (+108%) with addition of evergreen recommendations unlike rate remains stable #AI8SAIS 24

25 .Case 3: How do we automate publishing? #AI8SAIS 25

26 .Mixing human and AI curation #AI8SAIS 26

27 .Optimal scheduling model #AI8SAIS 27

28 .Trying all scheduling times #AI8SAIS 28

29 .How do we validate these models? #AI8SAIS 29

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