Spam user detection report

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Pixnet Hackathon 2015

Spam User Detection
Andy, Len, Petertc

Agenda
 Machine Learning Tools
 AWS Machine Learning
 Azure Machine Learning
 Spam Detection Algorithm
 Detect spam user by time frequency.
 Detect spam user by article correlation.

Machine Learning Tools

AWS Machine Learning

AWS Machine Learning

Azure Machine Learning
 https://manage.windowsazure.com

Spam Detection Algorithm

Detect spam user by time frequency.

Detect spam user by time frequency.
500,000 actions

Detect spam user by time frequency.
1,000,000 actions
500,000 actions

Detect spam user by time frequency.
1,000,000 actions
3,000,000 actions

Detect spam user by article correlation.
Spam User
Spam User
Spam User
High correlation
High correlation
High correlation

Detect spam user by article correlation.
Title_count
Article_count
High correlation

Thanks!

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Spam user detection report

1. Spam User Detection Andy, Len, Petertc

2. Agenda  Machine Learning Tools  AWS Machine Learning  Azure Machine Learning  Spam Detection Algorithm  Detect spam user by time frequency.  Detect spam user by article correlation.

3. Machine Learning Tools

4. AWS Machine Learning

5. AWS Machine Learning

6. Azure Machine Learning  https://manage.windowsazure.com

7. Spam Detection Algorithm

8. Detect spam user by time frequency.

9. Detect spam user by time frequency. 500,000 actions

10. Detect spam user by time frequency. 1,000,000 actions 500,000 actions

11. Detect spam user by time frequency. 1,000,000 actions 3,000,000 actions

12. Detect spam user by article correlation. Spam User Spam User Spam User High correlation High correlation High correlation

13. Detect spam user by article correlation. Title_count Article_count High correlation

Notes de l'éditeur

* Content / tag take out Loss data (article -> user) : 20000 -> 2000 It’s result
* Content / tag take out Loss data (article -> user) : 20000 -> 2000 It’s result