Deep neural networks can be used for YouTube recommendations by feeding the networks vast amounts of video data so they can learn to recognize patterns and provide personalized recommendations. Specifically, YouTube's algorithm ingests video data in real time, ranks videos based on signals like quality and user interests, and provides recommendations from the ranked candidates to improve the user experience.

1. Deep neural networksDeep neural networks
forfor
YouTube recommendationsYouTube recommendations

2. Deep neural networks
• Neural networks is a systems that approximate the web of neurons in
the human brain.
• feed these networks vast amounts of data, and they learn to perform a
task.
• Example: Feed them myriad photos of breakfast, lunch, and dinner,
and they can learn to recognize a meal.

3. Biological Neuron
A neuron is a computational unit in the neural network that
exchanges messages with each other.

4. The Neuron Metaphor
• Neurons
• accept information from multiple inputs,
• transmit information to other neurons.
• Artificial neuron
• Multiply inputs by weights along edges
• Apply some function to the set of inputs at each node

5. Limitations
• A single “neuron” is still a linear decision boundary
• What to do?
• Idea: Stack a bunch of them together!

6. Deep neural networks
• Cascade Neurons together
• The output from one layer is the input to the next
• Each Layer has its own sets of weights

7. What is a Recommmendation System?
Recommendation system is an information filtering technique, which
provides users with information, which he/she may be interested in.
Examples:

8. Types of Recommendation System
In General, two types of recommender system.
1. Personalized

9. Types... Cont'd
2. Non-Personalized

10. Types... cont'd
- Personalized
- Non-Personalized

11. Techniques : Data Acquisition
1. Explicit Data
- Customer Ratings
- Feedback
- Demographics
- Physiographics
- Ephemeral Needs
2. Implicit Data
- Purchase History
- Click or Browse History

12. Case StudyCase Study
1.1. YouTubeYouTube
• using Deep Learning to power its algorithm
• Algorithm is ingesting data in real time, ranking videos, and then providing
recommendations based on those rankings.
2.YouTube2.YouTube

14. Neural Networks

15. Related Videos

16. Generating Recommendation Candidates

17. Generating Recommendation Candidates
Generating Recommendation Candidates

18. Ranking
Generating Recommendation Candidates• Using the linear combinations of three kinds of signals
 video quality
 user specialty
 Diversification
we generate a ranked list of the candidate videos.

19. References
• Deep Neural Networks for YouTube Recommendations Paul Covington, Jay Adams,
Emre Sargin Google Mountain View, CA {pcovington, jka, msargin}@google.com