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Due to bad Topics
Back in 2013 ...
Party
4.6 Mio.
views per day
60 k
new content items per day
13 Mio.
questions
46 Mio.
answers
Community.
Source: AGOF e.V. / internet facts 2013-12
Reach.
Hangover
Tracking
Community Management
Tagging
Overcome the Reign of Chaos
Michael Stockerl
User Behaviour Tracking
Track each action
● Pageview
● View of content items
● Click event
Remember the user
● ClientId
● Location
● User-Agent
The Journey of a Data Point
Collect incoming events in Kafka
Enrich data points
● Parse arguments
● Join to database ids (e.g. question id)
Batch Processing Pipeline
Stream Processing Pipeline
Learnings
● Kafka is crazy robust
● Parquet accelerates reads
● Spark faster than Hadoop (development and production)
● Backpressure needed for MySQL and Elasticsearch
● Reliable Analytics and A/B Testing pipeline
Analytics ONLY with Devs
Democratize
Data Access
Data Only Available in Primary Stores
Make Data Available with Tableau
THRIFT SERVER
Learnings
● Tableau easy to learn...
● … but hard to master
● Spark Thriftserver crashes all the time ( somebody same experience? )
● Non - Dev Departments love it
Community Management
meets
Machine Learning
Manually search for bad
answers
Goal: Automatically rate answers
● Sort answers for each question
● Hide bad answers
● Report really bad answers
-> Algorithm to rate answers
Solution: Logistic Regression
● Supervised Machine Learning algorithm
● 2000 training & test examples
● Trained first and simple model
● Brought it to production
Users were NOT happy
Problems
● Model not complex enough
● Similar inputs, different outputs
● Not enough training data
● Missing definition of a good answer
Problems
● Model not complex enough
● Similar inputs, different outputs
● Not enough training data
● Missing definition of a good answer
-> Collect more data
-> Use more features
-> More complex model
Project Moria
Moderation Tool
● Definition of Categories
● Rate answers as fast as possible
● Randomly picked (at first)
● Delete answers
Dataset clean?
● See distribution of classification
● Monitor single classifications
● Rate too nice?
● Or too harsh?
Project Angmar
Project Angmar
● Tried a lot of Supervised Learning Methods
● Feature Engineering: Most crucial part
● Analyse the domain, chart everything
Features
Content
length
syntactic complexity
number of links
probability of deletion
Social
votes
most helpful answer
number of comments
answered by expert
Author
gained votes
credibility score
role
ratio of deleted answers
number of answers
number of comments
ratio of reported answers
The winner: A simple Neural Net
Answer
vector
AV
normalized
Input
layer
21
3
1
0.2
0.4
0.1
2 0.8
wordCount
voteUp
voteDown
n
Hidden
layer
Output
layer
2n
Score 0.2
Result on Testdata
Realworld Test
deleted
non-deleted
Problems in Production
Neural Nets in short
Debugging with Radagast
AV
normalized
Input
layer
Hidden
layer
Output
layer
0.2
0.4 -> 0.6
0.1
0.8
wordCount
voteUp
voteDown
n
2n
Score 0.5
+10
Before switching models
Amount of answers
for a score range
Compare Live and Shadow Model
Amount of answers
for a score range
Insights
woman
man
Woman write better answers
Answerscore per Userlevel
Standard deviation
Answerscore per Userlevel
Standard deviation
History of user matters
Quality per Tag
Outlook
Question Rating
New Taxonomy
1.005.994 Tags
Goal: Directed Acyclic Graph
● Model Hierachy
● Use our content
○ Co-occurence with top tags
○ Repeat with those tags
○ Refinement manually
● Use Cases:
○ Recommender
○ Answer Score
○ Search
○ Experts
Computer
root
Sport
Games Fußball
Fifa 17
Prototyp - Related Tags: Umwelt
Prototyp - Related Tags: Polizei
Prototyp - Related Tags: Herz
Prototyp - Related Tags: Wissenschaft
Taxonomy
UI any ideas? Computer
root
Sport
Games Fußball
Fifa 17
Learning
In God we trust;
all others bring data
W. Edwards Deming
Questions?
Questions?
We’re hiring!
Scala Finagle
Typescript
Akka-HTTP
Kafka
Spark
MySQL
Elasticsearch
Mesos
Redis

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