Machine Learning at PeerIndex
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Slides for talk given at London Machine Learning Meetup on 29 Feb about machine learning behind measuring people's influence at PeerIndex.
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Machine Learning at PeerIndex
- 1. Machine Learning at PeerIndex @fhuszar Ferenc Huszár Wednesday, 16 May 12
- 2. PeerIndex.com: understand your influence Wednesday, 16 May 12
- 3. PeerPerks.com: free stuff for influencers Wednesday, 16 May 12
- 4. PeerPerks: free stuff for influencers Wednesday, 16 May 12
- 5. Machine Learning @ PeerIndex Wednesday, 16 May 12
- 6. Machine Learning @ PeerIndex • The usual stuff Wednesday, 16 May 12
- 7. Machine Learning @ PeerIndex • The usual stuff • topic modelling/classification of tweets/statuses/URLs Wednesday, 16 May 12
- 8. Machine Learning @ PeerIndex • The usual stuff • topic modelling/classification of tweets/statuses/URLs • identity resolution across twitter, facebook, linkedIn Wednesday, 16 May 12
- 9. Machine Learning @ PeerIndex • The usual stuff • topic modelling/classification of tweets/statuses/URLs • identity resolution across twitter, facebook, linkedIn • spambot/fraud detection: identify people gaming the system Wednesday, 16 May 12
- 10. Machine Learning @ PeerIndex • The usual stuff • topic modelling/classification of tweets/statuses/URLs • identity resolution across twitter, facebook, linkedIn • spambot/fraud detection: identify people gaming the system • sentiment classification: happy/sad/neutral Wednesday, 16 May 12
- 11. Machine Learning @ PeerIndex • The usual stuff • topic modelling/classification of tweets/statuses/URLs • identity resolution across twitter, facebook, linkedIn • spambot/fraud detection: identify people gaming the system • sentiment classification: happy/sad/neutral • The really exciting stuff Wednesday, 16 May 12
- 12. Machine Learning @ PeerIndex • The usual stuff • topic modelling/classification of tweets/statuses/URLs • identity resolution across twitter, facebook, linkedIn • spambot/fraud detection: identify people gaming the system • sentiment classification: happy/sad/neutral • The really exciting stuff • inferring networks of influence - more about this later Wednesday, 16 May 12
- 13. Machine Learning @ PeerIndex • The usual stuff • topic modelling/classification of tweets/statuses/URLs • identity resolution across twitter, facebook, linkedIn • spambot/fraud detection: identify people gaming the system • sentiment classification: happy/sad/neutral • The really exciting stuff • inferring networks of influence - more about this later • visualise different aspects of influence, in an engaging way Wednesday, 16 May 12
- 14. Machine Learning @ PeerIndex • The usual stuff • topic modelling/classification of tweets/statuses/URLs • identity resolution across twitter, facebook, linkedIn • spambot/fraud detection: identify people gaming the system • sentiment classification: happy/sad/neutral • The really exciting stuff • inferring networks of influence - more about this later • visualise different aspects of influence, in an engaging way • influence maximisation - submodular optimisation Wednesday, 16 May 12
- 15. Inferring networks of influence Wednesday, 16 May 12
- 16. Inferring networks of influence Social network Wednesday, 16 May 12
- 17. Inferring networks of influence Social network Propagation probabilities pi,j Wednesday, 16 May 12
- 18. Inferring networks of influence Social network Propagation probabilities pi,j Information cascade logs http://www.pcworld.com/article/239719 http://techcrunch.com/2011/11/21/... 1079306 2011-08-25T00:03:06+01:00 259725 2011-10-24T03:32:19+01:00 4549198 2011-08-25T04:32:25+01:00 76539 2011-10-24T03:32:23+01:00 2662975 2011-08-25T00:35:11+01:00 1922351 2011-10-24T04:28:47+01:00 2333224 2011-08-25T01:43:18+01:00 9183 2011-10-24T03:30:57+01:00 3141371 2011-08-25T01:52:06+01:00 3335398 2011-10-24T03:34:01+01:00 3482720 2011-08-25T07:18:24+01:00 1616885 2011-10-24T03:48:16+01:00 1403682 2011-08-25T03:52:58+01:00 82198 2011-10-24T03:48:29+01:00 4679657 2011-08-25T01:07:48+01:00 906390 2011-10-24T23:13:51+01:00 32460 2011-08-25T01:11:43+01:00 1051322 2011-10-24T03:40:02+01:00 Wednesday, 16 May 12
- 19. Heurisric approaches to estimate pi,j Wednesday, 16 May 12
- 20. Heurisric approaches to estimate pi,j • purely based on local network structure 1 pi,j din (j) Wednesday, 16 May 12
- 21. Heurisric approaches to estimate pi,j • purely based on local network structure 1 pi,j din (j) • trivalency “model” my personal favourite :) pi,j {0.1, 0.01, 0.01} randomly Wednesday, 16 May 12
- 22. Heurisric approaches to estimate pi,j • purely based on local network structure 1 pi,j din (j) • trivalency “model” my personal favourite :) pi,j {0.1, 0.01, 0.01} randomly • data-driven heuristics number of items shared by j after i shared it pi,j number of items shared by i Wednesday, 16 May 12
- 23. Heurisric approaches to estimate pi,j • purely based on local network structure 1 pi,j din (j) • trivalency “model” my personal favourite :) pi,j {0.1, 0.01, 0.01} randomly • data-driven heuristics number of items shared by j after i shared it pi,j number of items shared by i How do you solve this with machine learning? Wednesday, 16 May 12
- 24. The likelihood Wednesday, 16 May 12
- 25. The likelihood P( D | ✓ ) Wednesday, 16 May 12
- 26. The likelihood P( D | ✓ ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 Wednesday, 16 May 12
- 27. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j Wednesday, 16 May 12
- 28. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j what’s the probability of the cascade u1 , u2 , u3 , . . . , un Wednesday, 16 May 12
- 29. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j what’s the probability of the cascade u1 , u2 , u3 , . . . , un for subsequent users in cascade Wednesday, 16 May 12
- 30. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j what’s the probability of the cascade u1 , u2 , u3 , . . . , un for subsequent users in cascade p0,u1 Wednesday, 16 May 12
- 31. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j what’s the probability of the cascade u1 , u2 , u3 , . . . , un for subsequent users in cascade p0,u1(1 (1 p0,u2 ) (1 pu1 ,u2 )) Wednesday, 16 May 12
- 32. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j what’s the probability of the cascade u1 , u2 , u3 , . . . , un for subsequent users in cascade p0,u1(1 (1 p0,u2 ) (1 pu1 ,u2 ))· · · Wednesday, 16 May 12
- 33. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j what’s the probability of the cascade u1 , u2 , u3 , . . . , un for subsequent users in cascade 0 1 n Y i 1 Y = @1 (1 puj ,ui )A i=1 j=1 Wednesday, 16 May 12
- 34. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j what’s the probability of the cascade u1 , u2 , u3 , . . . , un for subsequent users in cascade 0 1 n Y i 1 Y = @1 (1 puj ,ui )A i=1 j=1 for users that are not in cascade Wednesday, 16 May 12
- 35. The likelihood P( D | ) http://www.pcworld.com/article/239719 1079306 2011-08-25T00:03:06+01:00 4549198 2011-08-25T04:32:25+01:00 2662975 2011-08-25T00:35:11+01:00 2333224 2011-08-25T01:43:18+01:00 3141371 2011-08-25T01:52:06+01:00 3482720 2011-08-25T07:18:24+01:00 1403682 2011-08-25T03:52:58+01:00 4679657 2011-08-25T01:07:48+01:00 32460 2011-08-25T01:11:43+01:00 pi,j what’s the probability of the cascade u1 , u2 , u3 , . . . , un for subsequent users in cascade 0 1 n Y i 1 Y = @1 (1 puj ,ui )A i=1 j=1 for users that are not in cascade Y Y (1 pu,v ) u2{u1 ...un } v2users / Wednesday, 16 May 12
- 36. Maximum likelihood at scale Wednesday, 16 May 12
- 37. Maximum likelihood at scale • data too sparse to learn one parameter per edge Wednesday, 16 May 12
- 38. Maximum likelihood at scale • data too sparse to learn one parameter per edge • large scale gradient-based optimisation is costly Wednesday, 16 May 12
- 39. Maximum likelihood at scale • data too sparse to learn one parameter per edge • large scale gradient-based optimisation is costly • Solution: combine ensemble of heuristics with ML Wednesday, 16 May 12
- 40. Maximum likelihood at scale • data too sparse to learn one parameter per edge • large scale gradient-based optimisation is costly • Solution: combine ensemble of heuristics with ML • use heuristics to compute probabilities at scale Wednesday, 16 May 12
- 41. Maximum likelihood at scale • data too sparse to learn one parameter per edge • large scale gradient-based optimisation is costly • Solution: combine ensemble of heuristics with ML • use heuristics to compute probabilities at scale • use ML to tune parameters on small-scale data Wednesday, 16 May 12
- 42. Influence maximisation Wednesday, 16 May 12
- 43. Influence maximisation • Select a set of users to maximise outreach Wednesday, 16 May 12
- 44. Influence maximisation • Select a set of users to maximise outreach • Influence of people combines non-linearly Wednesday, 16 May 12
- 45. Influence maximisation • Select a set of users to maximise outreach • Influence of people combines non-linearly • In many models it combines sub-modularly Wednesday, 16 May 12
- 46. Influence maximisation • Select a set of users to maximise outreach • Influence of people combines non-linearly • In many models it combines sub-modularly A ✓ B =) f (A [ {x}) f (A) f (B [ {x}) f (B) Wednesday, 16 May 12
- 47. Influence maximisation • Select a set of users to maximise outreach • Influence of people combines non-linearly • In many models it combines sub-modularly A ✓ B =) f (A [ {x}) f (A) f (B [ {x}) f (B) • these functions are fun to optimise Wednesday, 16 May 12
- 48. Influence maximisation • Select a set of users to maximise outreach • Influence of people combines non-linearly • In many models it combines sub-modularly A ✓ B =) f (A [ {x}) f (A) f (B [ {x}) f (B) • these functions are fun to optimise • pops up many times in machine learning Wednesday, 16 May 12
- 49. Wrap up Wednesday, 16 May 12
- 50. Wrap up • two lines of ‘data’ products: PeerIndex, PeerPerks Wednesday, 16 May 12
- 51. Wrap up • two lines of ‘data’ products: PeerIndex, PeerPerks • lots of ‘standard’ machine learning tasks Wednesday, 16 May 12
- 52. Wrap up • two lines of ‘data’ products: PeerIndex, PeerPerks • lots of ‘standard’ machine learning tasks • some uniquely exciting problems Wednesday, 16 May 12
- 53. Wrap up • two lines of ‘data’ products: PeerIndex, PeerPerks • lots of ‘standard’ machine learning tasks • some uniquely exciting problems • inferring propagation probabilities Wednesday, 16 May 12
- 54. Wrap up • two lines of ‘data’ products: PeerIndex, PeerPerks • lots of ‘standard’ machine learning tasks • some uniquely exciting problems • inferring propagation probabilities • compute expected number of users one reaches out to Wednesday, 16 May 12
- 55. Wrap up • two lines of ‘data’ products: PeerIndex, PeerPerks • lots of ‘standard’ machine learning tasks • some uniquely exciting problems • inferring propagation probabilities • compute expected number of users one reaches out to • putting all aspects together into a single number, and visualise Wednesday, 16 May 12
- 56. Wrap up • two lines of ‘data’ products: PeerIndex, PeerPerks • lots of ‘standard’ machine learning tasks • some uniquely exciting problems • inferring propagation probabilities • compute expected number of users one reaches out to • putting all aspects together into a single number, and visualise • influence maximisation Wednesday, 16 May 12
- 57. Thanks We’re hiring ML scientists, interns and engineers... @fhuszar fh@peerindex.com Wednesday, 16 May 12
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