ML algorithms are crucial to Quora's mission of growing the world's knowledge. They help rank answers by quality, relevance, and other factors. They also personalize the question feed, recommend new topics and users to follow, find related questions, and detect duplicate or spam content. Models used include logistic regression, gradient boosted trees, neural networks and more. ML helps scale moderation and optimize for user contributions in addition to consumption. Quora believes ML is key to its success in understanding complex data relations to improve the user experience.

1. ML @ Quora
ML Algorithms for Growing the World’s Knowledge
Seattle, 05/01/2015Xavier Amatriain (@xamat)

2. About Quora

3. Our Mission
“To share and grow the world’s
knowledge”
• Millions of questions & answers
• Millions of users
• Thousands of topics
• ...

4. Lots of data relations

5. Complex network propagation effects

6. Importance of topics & semantics

7. Demand
What we care about
Quality
Relevance

8. Machine Learning
@Quora

9. Ranking - Answer ranking
What is a good Quora answer?
• truthful
• reusable
• provides explanation
• well formatted
• ...

10. Ranking - Answer ranking
How are those dimensions translated
into features?
• Features that relate to the text
quality itself
• Interaction features
(upvotes/downvotes, clicks,
comments…)
• User features (e.g. expertise in topic)

11. Ranking - Feed
• Personalized learning-to-rank
approach
• Goal: Present most interesting stories
for a user at a given time
• Interesting = topical relevance +
social relevance + timeliness
• Stories = questions + answers

12. Ranking - Feed
• Features
• Quality of question/answer
• Topics the user is interested on/
knows about
• Users the user is following
• What is trending/popular
• …
• Different temporal windows
• Multi-stage solution with different
“streams”

13. Recommendations - Topics
Goal: Recommend new topics for the
user to follow
• Based on
• Other topics followed
• Users followed
• User interactions
• Topic-related features
• ...

14. Recommendations - Users
Goal: Recommend new users to follow
• Based on:
• Other users followed
• Topics followed
• User interactions
• User-related features
• ...

15. Related Questions
• Given interest in question A (source) what other
questions will be interesting?
• Not only about similarity, but also “interestingness”
• Features such as:
• Textual
• Co-visit
• Topics
• …
• Important for logged-out use case

16. Duplicate Questions
• Important issue for Quora
• Want to make sure we don’t disperse
knowledge to the same question
• Solution: binary classifier trained with
labelled data
• Features
• Textual vector space models
• Usage-based features
• ...

17. User Trust/Expertise Inference
Goal: Infer user’s trustworthiness in relation
to a given topic
• We take into account:
• Answers written on topic
• Upvotes/downvotes received
• Endorsements
• ...
• Trust/expertise propagates through the network
• Must be taken into account by other algorithms

18. Trending Topics
Goal: Highlight current events that are
interesting for the user
• We take into account:
• Global “Trendiness”
• Social “Trendiness”
• User’s interest
• ...
• Trending topics are a great discovery mechanism

19. Spam Detection/Moderation
• Very important for Quora to keep quality of
content
• Pure manual approaches do not scale
• Hard to get algorithms 100% right
• ML algorithms detect content/user issues
• Output of the algorithms feed manually
curated moderation queues

20. Content Creation Prediction
• Quora’s algorithms not only optimize for
probability of reading
• Important to predict probability of a user
answering a question
• Parts of our system completely rely on
that prediction
• E.g. A2A (ask to answer) suggestions

21. Models
● Logistic Regression
● Elastic Nets
● Gradient Boosted Decision
Trees
● Random Forests
● Neural Networks
● LambdaMART
● Matrix Factorization
● LDA
● ...

22. Conclusions

23. Conclusions
• At Quora we have not only Big, but also “rich” data
• Our algorithms need to understand and optimize complex aspects
such as quality, interestingness, or user expertise
• We believe ML will be one of the keys to our success
• We have many interesting problems, and many unsolved challenges

24. We’re Hiring!
http://www.quora.com/careers/