3. Our Mission
âTo share and grow the worldâs
knowledgeâ
⢠Millions of questions & answers
⢠Millions of users
⢠Thousands of topics
⢠...
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,
commentsâŚ)
⢠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
⢠âŚ
⢠Different temporal windows
⢠Multi-stage solution with different
âstreamsâ
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 âinterestingnessâ
⢠Features such as:
⢠Textual
⢠Co-visit
⢠Topics
⢠âŚ
⢠Important for logged-out use case
16. Duplicate Questions
⢠Important issue for Quora
⢠Want to make sure we donât 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 userâs 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 âTrendinessâ
⢠Social âTrendinessâ
⢠Userâs 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
⢠Quoraâs 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
23. Conclusions
⢠At Quora we have not only Big, but also ârichâ 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