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MLConf Seattle 2015 - ML@Quora

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Xavier Amatriain
Xavier Amatriain

Talk on how Quora uses Machine Learning. Presented at the MLConf Seattle Conference in 2015

Technology
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ML @ Quora ML Algorithms for Growing the World’s Knowledge Seattle, 05/01/2015Xavier Amatriain (@xamat)
About Quora
Our Mission “To share and grow the world’s knowledge” • Millions of questions & answers • Millions of users • Thousands of topics • ...
Lots of data relations
Complex network propagation effects
Importance of topics & semantics
Demand What we care about Quality Relevance
Machine Learning @Quora
Ranking - Answer ranking What is a good Quora answer? • truthful • reusable • provides explanation • well formatted • ...
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)
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
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”
Recommendations - Topics Goal: Recommend new topics for the user to follow • Based on • Other topics followed • Users followed • User interactions • Topic-related features • ...
Recommendations - Users Goal: Recommend new users to follow • Based on: • Other users followed • Topics followed • User interactions • User-related features • ...
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
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 • ...
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
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
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
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
Models ● Logistic Regression ● Elastic Nets ● Gradient Boosted Decision Trees ● Random Forests ● Neural Networks ● LambdaMART ● Matrix Factorization ● LDA ● ...
Conclusions
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
We’re Hiring! http://www.quora.com/careers/

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MLConf Seattle 2015 - ML@Quora

  • 1. ML @ Quora ML Algorithms for Growing the World’s Knowledge Seattle, 05/01/2015Xavier Amatriain (@xamat)
  • 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
  • 6. Importance of topics & semantics
  • 7. Demand What we care about Quality Relevance
  • 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 ● ...
  • 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