Speaker: Yaz Santissi, GDG Cloud Title: Tensorflow and Graph Recommender Networks Abstract: Recommender systems are a core part of the web landscape - from social media, e-commerce, transport, search, and content networks to name just a few. But, compared to other areas such as image recognition, success in Tensorflow for recommender systems has lagged behind. Yaz will provide an overview of the latest research in the field, how it compares to traditional approaches, and discuss the implications for future developments. Bio: Despite working in cloud and machine learning, Yaz believes creativity or power is still not measured in terms of numbers of GPUs or scalable servers. While appreciating what they can do, he remains a steadfast fan of human learning. In fact, he organises the largest number of TensorFlow events in the world. Usually, in a hack format, participants not only share what they have learned, they also draw inspiration from each other, build projects together, and experiment with the process of coding collaboratively itself in order to nurture that sometimes elusive developer flow.

1. TensorFlow +
Graph
Recommender
Networks
Yaz Santissi
@yazdotai

2. Machine Learning
AI

3. It depends ?

5. 5

7. 7

8. 8

11. Movie Lens ( 1 million )
Reddit ( 11 million )

12. Is Graph Data
Not Important ?

15. 1 billion hours
per day
80 %

17. $140 billion
10x

18. 44 trillion GB by 2020
300x than in 2005
90% in past two years
1.7MB per human per sec

19. So why has
TensorFlow not taken
over graph data?

20. Graph Data does not
need TensorFlow to
be useful

23. Limits of
Feature
Engineering

24. Can we not port
established TF
algorithms to graph
data?

25. Euclidean
mathematics
domain

26. - Two points determine a unique line
- Any terminated line may be
extended indefinitely
- A circle may be drawn with any
given point as center and any given
radius
- All right angles are equal

27. - Shortest distance bet two points
will always be a straight line
- Interior angles of a triangle add up
to 180 degrees
- Two parallel lines will never cross

32. Deep Walk
+
Node2Vec

33. GraphSAGE

34. 3 billion nodes
18 billion edges

35. Future Directions

36. Yaz Santissi
@yazdotai
Thank You
https://github.com/yazdotai/
graph-networks