Intelligently matching users to questions for reading and writing

Nikhil Dandekar @nikhilbd

6/8/2016

Quora’s Mission

“To share and grow the world’s knowledge”

● Millions of questions & answers

● Millions of users

● Over a million topics

● ...

Matching users to questions for reading

Feed ranking

Goal: Present most interesting stories for a

user at a given time

Feed ranking

Challenges:

● Millions of potential stories

● How do you measure and predict interestingness?

● Real-time ranking

How do you measure interestingness?

click

upvote

downvote

expand

share

Weighted sum of actions =

∑a va p(a | x) =

vclick p(click | x) + vupvotep(upvote | x) + vdownvote p(downvote | x) + ….

Predict this value for new stories. Rank stories by this

predicted value.

How do you measure interestingness?

Machine Learning for Feed ranking

● Essential for getting good ranking

● Main sets of features:

○ User

○ Story

○ Interactions between the two

Matching users to questions for writing

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

Question Answering

Solution: Route the question to the best

person who can answer the question

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

Questions?