如何使观众读者们满意

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