Xavier Amatriain,Quora Engineering VP. Take a look at his shared Quora recommendation system (REcommender Systems, which will also be shortened to Recsys) Build experience.
The mission of the site
Review images
Quora data
Review images
Lots of high quality text
Review images
A lot of data correlation
Review images
Quora’s recommendation system
Review images
On Quora, recommendations are used in many places.
Review images
model
Review images
Lessons learned from the recommendation system construction process
-
implicit signals beat explicit ones (almost always)
Review images
Review images
(Note: Explicit signals refer to feedback collected directly, such as asking users to rate, or clicking on a thumbs down/thumbs down button. Implicit signals refer to information analyzed by user behavior, such as user logs.)
Review images
Review images
2.be thoughtful about your training data
Review images
Review images
3. your model will learn what you teach it to learn
Review images
Review images
Review images
4.explanations might matter more than the prediction
Review images
Review images
5. if you have to pick one single approach, matrix factorization is your best bet
Review images
Review images
6. everything is an ensemble
Review images
Review images
Review images
Review images
7. building recommender systems is also about feature engineering
Review images
Review images
Review images
Review images
Review images
8. why you should care about answering questions (about your recsys)
Review images
Review images
Review images
9. Data and models are great. You know what’s even better? The right evaluation approach!
Review images
Review images
Review images
Review images
10. You don’t need to distribute your recsys
Review images
(Does it have to be distributed? Not really)
Review images
Review images
conclusion
Review images
Slide link: http://www.slideshare.net/xamat/recsys-2016-tutorial-lessons-learned-from-building-reallife-recommender-systems
(after)
The recent hot,
Eliminate small Huang Tu big sword | X yuan pocket
Yahoo open source Pulsar large-scale Pub-sub messaging system
YouTube recommendation system based on deep neural network
Full explanation of the mark-sweep garbage collection algorithm
Recommended 10 architecture, big data aspects of dry goods