Slides for Machine Learning Indonesia Talk in Kumparan

1. Recommender System
Infrastructure in Kumparan
Yosua Michael Maranatha

2. - About Kumparan
- Intro to Recommendation System
- Building From Scratch
- Getting user’s behaviour
- Analyze user behaviour
- Processing the data and deploy
- Serving the recommendation
- Iterate and improve
- Questions?
Content

3. About Kumparan
- A startup that focus in both media and
technology
- We want to become a scalable and yet credible
media platform
- Media platform means that it is a platform
where people can publish their content on
Kumparan

4. Intro to Recommendation System
Recommendation System is basically a system that
manage what content that each user will see in
Kumparan

5. Building From Scratch
- Imagine you need to build a recommendation
system from scratch.
- Then you will realize that no data available...
- First Challenge: Gather the data!

6. Building From Scratch
- Once we get the data, now the problem is how
do we use the data.
- Second Challenge: Process the data!

7. Building From Scratch
- Lastly, after we process the data and use it.
Now we should ask on how do we improve it
- Third Challenge: Iterate and improve!

8. Getting Users Behaviour Data
- We need to build a tracker to get the users
behaviour data
- Challenges:
- High velocity data stream
- High volume data to process
- We have a burst traffic in a certain period
in media and the tracker system need to be
able to autoscale in this case
- (optional) Having a real-time tracking
system

9. Getting Users Behaviour Data

10. Getting Users Behaviour Data
Further challenges:
- Define the format to define the events
- Create documentations on what to track
- Implement the tracking code on the frontend
that will later call the tracker-api

11. Analyze user behaviour
- Usually the challenge of analysing user
behaviour is the problem of processing a big
data
- We solved it by using BigQuery as our
DataWarehouse
- We can basically use SQL to collect as well as
process in BigQuery
- And for the more detailed analysis, we will
aggregate the data on BigQuery and further
process it with Python on Jupyter notebook

12. Analyze user behaviour

13. Processing the data deployment
- Similar with the analysis, we use both BigQuery
and Python for deployment
- We use our own system for managing the
BigQuery queries on top of Airflow
- For the Python, it is deployed on Kubernetes as
a CronJob
- The result will be stored in serving database
(Elasticsearch, MySQL, Redis, or BigTable)

14. Processing the data deployment

15. Serving the Recommendation
- Need to explain the infra for serving the API
- After we get the result in the serving database,
the last step would be serving the API
- We serve the API using Python (sanic or flask) in
kubernetes as well
- The endpoint in kubernetes would be all
combined in one subdomain with NginX reverse
proxy for integration
- We can cache the endpoint to have a faster
latency

16. Serving the Recommendation

17. Iterate and Improve
- To iterate and improve is basically to change
the parameter, algorithm, or possibly the UX
- One of the challenge is to actually know
whether the changes have a positive
improvement
- We can use AB-test to test our hypothesis that
the new idea have a significant improvement
statistically!

18. Iterate and Improve
- We build our AB-test platform in Kumparan
- It is divided into two things:
- AB-test system
- AB-test analysis
- We use the open source library Planout for the
AB-test system
- For the AB-test analysis, we use python dash
plotly library for visualization

19. Iterate and Improve
AB-test System

20. Iterate and Improve
AB-test Analysis

21. Conclusion
- We explain the recommender infrastructure in
Kumparan
- The infrastructure include the data gathering,
processing, serving, and how to iterate and
improve
- I hope this presentation is useful :)

22. Recommender Infrastructure Diagram

23. AB-Test Infrastructure Diagram

24. THANK YOU!

25. We are hiring!
1. Data Engineer
2. Data Scientist
3. BI Engineer
4. BI Analyst
5. Software Engineer (Frontend, Backend & Mobile
Application)
Email Us on joindev@kumparan.com

26. QUESTIONS ?