Talk on Quora recommendation systems for the reading, asking and writing use-cases. I gave this talk at the Airbnb OpenAir Conference in June 2016.

1. Intelligently matching users to
questions for reading and writing
Nikhil Dandekar
@nikhilbd
6/8/2016

2. Quora’s Mission
“To share and grow the world’s knowledge”
● Millions of questions & answers
● Millions of users
● Over a million topics
● ...

3. Matching users to
questions for reading

4. Feed ranking
Goal: Present most interesting stories for a
user at a given time

6. Feed ranking
Challenges:
● Millions of potential stories
● How do you measure and predict interestingness?
● Real-time ranking

7. How do you measure interestingness?
click
upvote
downvote
expand
share

8. Weighted sum of actions =
∑a
va
p(a | x) =
vclick
p(click | x) + vupvote
p(upvote | x) + vdownvote
p(downvote | x) + ….
Predict this value for new stories. Rank stories by this
predicted value.
How do you measure interestingness?

9. Machine Learning for Feed ranking
● Essential for getting good ranking
● Main sets of features:
○ User
○ Story
○ Interactions between the two

10. Matching users to
questions for writing

11. Question Answering
Asker goal: Get a great answer to the
question in the shortest amount of time
Answer writer goal: Find the best questions
that you can write a great answer to

12. Question Answering
Solution: Route the question to the best
person who can answer the question

14. Machine Learning for “Ask to Answer”
● Target
○ 1 if answer request sent and answer has goodness score of above
0.5
○ 0 otherwise
● Main sets of features:
○ Asker
○ Answer writer
○ Question
○ Various interactions between the three

15. Questions?