Deep Neural networks for Youtuberecommendationsystem
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Deep Neural networks for Youtuberecommendationsystem
- 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
- 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
- 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