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Google RankBrain: What It Does, How It Works, What It Means By Marcus Tober

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From the SMX West Conference in San Jose, California, March 1-3, 2016. SESSION: RankBrain: What Do We Know About Google's New Machine-Learning System?. PRESENTATION: Google RankBrain: What It Does, How It Works, What It Means - Given by Marcus Tober, @marcustober - Searchmetrics Inc, Founder & CTO. #SMX #22A

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Google RankBrain: What It Does, How It Works, What It Means By Marcus Tober

  1. 1. #SMX #22A @MarcusTober Google  RankBrain   -­‐  WHAT  IT  DOES   -­‐  HOW  IT  WORKS   -­‐  WHAT  IT  MEANS     MARCUS  TOBER   SMX  West   March  2,  2016  
  2. 2. #SMX #22A @MarcusTober Searchmetrics   Made  with  love  in  Berlin   More  than  220  passionate  people   Innovator  in  SEO  Software  since  2005  
  3. 3. #SMX #22A @MarcusTober Marcus  Tober   Founder  and    CTO  of   Searchmetrics   In  love  with  SEO  and  SEARCH   since  2001   Study  of  computer  science   In  Berlin,  so  I´m  the  Techie!  
  4. 4. #SMX #22A @MarcusTober Machine Learning or AI?
  5. 5. #SMX #22A @MarcusTober Machine Learning (ML)  ≠  AI  
  6. 6. #SMX #22A @MarcusTober Machine  Learning   (ML)   Deep  Learning   Artificial   Intelligence   An  algorithm  that   improves  over   time   Aims  to  bridge  the   gap  between  ML   and  AI  –  solves   more  complex   problems   Human  like   intelligence   Spectrum of Intelligence
  7. 7. #SMX #22A @MarcusTober
  8. 8. #SMX #22A @MarcusTober •  Facebook  photo  recognition   •  Email  spam  filters   •  Database  mining   •  Music  or  movie  recommendations   iTunes  /  Spotify  /  Netflix   •  Solving  games  (e.g.  chess)  –  IBM‘s  Deep   Blue  vs  Garry  Kasaparov   Common Applications of Machine Learning
  9. 9. #SMX #22A @MarcusTober •  More  than  2,500  years  old   •  Played  by  40m  worldwide   •  Deemed  uncrackable  by  Machine   Learning/AI  (until  AlphaGo)   Go – The limits of machine learning
  10. 10. #SMX #22A @MarcusTober Is Go really that hard? (well actually, yes…) 1050   4x1079  –  4x1081   10171   Number  of...   in  a  chess  game   in  the  observable  universe   in  game  of  Go   …possible  moves   …possible  moves  …atoms  
  11. 11. #SMX #22A @MarcusTober Not so simple That’s…   1,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,00 0,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,00 0,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000     …possible  positions!  
  12. 12. #SMX #22A @MarcusTober •  Traditional  A.I  methods  -­‐  which  analyze  all   possible  positions  –failed   •  AlphaGo  users  uses  deep  neural  networks   across  12  different  network  layers   •  One  neural  network,  selects  the  next  move  to   play.  The  other  neural  network,  the  “value   network”,  predicts  the  winner  of  the  game.   Deep learning - AlphaGo
  13. 13. #SMX #22A @MarcusTober Understanding RankBrain
  14. 14. #SMX #22A @MarcusTober Legend  in  Neural  Network   Research.  World   renowned  A.I.  researcher.   Invented  some  of  the  core   algorithms  of  Deep   Learning  back  in  the  80s   Working  with  Google   since  2011   New  novel  concept:   Thought  Vectors   Geoffrey  Hinton   Capturing thoughts…
  15. 15. #SMX #22A @MarcusTober IMAGINE EMPTY SPACE
  16. 16. #SMX #22A @MarcusTober Every  word  gets  a  position  in  space   Oregon   Salem   Sacramento   California   Visualizing RankBrain
  17. 17. #SMX #22A @MarcusTober Query  sentences  get  a  position  in  space.   What’s  the  weather  going  to  be  like  in  California?   Weather  Forecast  California   Visualizing RankBrain Using  training  data,  similar  query  sentences   (with  similar  results)  are  closely  positioned  
  18. 18. #SMX #22A @MarcusTober Queries  and  results  get  a  position  in  space   Q   Result  Scoring   R   R   Good  results  rank  better  based  on  proximity   Visualizing RankBrain
  19. 19. #SMX #22A @MarcusTober Traditonal  Ranking  Factors  can  no  longer   make  sense  of  organic  rankings.   Searchmetrics Hypothesis
  20. 20. #SMX #22A @MarcusTober Searchmetrics Hypothesis We  said  that:   •  RankBrain  concentrates  on  relevant  content   •  RankBrain  uses  thought  vectors  to  map  relevant   results  to  queries   •  This  relevance  score  is  then  used  to  help  order  rankings   Q   R   R  
  21. 21. #SMX #22A @MarcusTober Brief study background •  Top  30  Results  for  Google  US   •  ~400,000  datapoints   •  3  keyword  sets:  Loan,  E-­‐Commerce,  Health   •  Approach:  Discover  which  ranking  factors  are   most  important   •  Emulate  RankBrain  by  adding  scores  to     understand  the  content  relevance.  
  22. 22. http://www.google.com/ Cash advance fresno ca Examples: Backlinks & Internal Links https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ Rank:  5   Rank:  12   17  backlinks   21  internal  links   52,000  backlinks   443  internal  links   Keyword:  “Cash  advance  fresno  ca”    LOAN  
  23. 23. #SMX #22A @MarcusTober 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 E-Commerce Health Loan Backlinks Positive  correlation   Negative  correlation  
  24. 24. #SMX #22A @MarcusTober 0 50 100 150 200 250 300 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 E-Commerce Health Loan Number of Internal Links
  25. 25. http://www.google.com/ fast  cash  credit  card https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ Rank:  6   Rank:  17   5,685  Backlinks   No  KW  in  Title   59,780  Backlinks   KW  in  Title   Keyword:  “fast  cash  credit  card”    LOAN   Keyword in Title
  26. 26. #SMX #22A @MarcusTober 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 E-Commerce Health Loan Keyword in Title 10%  of  pages  in  “loans”  only  have  the  keyword  in  title-­‐tag  
  27. 27. http://www.google.com/ natural  detox   https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ Rank:  5   Rank  26   Word  count  =  3180   Internal  links  count  =  375   Interactive  elements  =  398     Word  count  =  6087   Internal  links  count  =  395   Interactive  elements  =  625   Keyword:  “natural  detox”    HEALTH   Word Count & Interactive Elements
  28. 28. #SMX #22A @MarcusTober 1,000 1,500 2,000 2,500 3,000 3,500 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 E-Commerce Health Loan Word Count
  29. 29. #SMX #22A @MarcusTober 0 50 100 150 200 250 300 350 400 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 E-Commerce Health Loan Number of Interactive Elements
  30. 30. #SMX #22A @MarcusTober Why do “traditional” ranking factors fail to explain these examples?
  31. 31. #SMX #22A @MarcusTober •  We  emulated  RankBrain  and  gave  search   results  a  relevance  score  Q   R   R   •  This  score  is  based  on  how  relevant  a  result  is  to   a  query   •  We  used  around  25  relevance  ranking  factors   to  assess  relevance   Relevance Ranking Factors
  32. 32. #SMX #22A @MarcusTober •  9  out  of  top  10  e-­‐commerce   websites  for  this  keyword  have   an  add  to  cart  function    above   the  fold     Keyword:  “security  camera  system”    E-­‐COMM   Introducing Relevance
  33. 33. https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ Keyword:  “security  camera  system”    E-­‐COMM   •  Rank  9  does  not.   •  However,  it  has  the  highest   relevance  score  of  the  top  30  -­‐  and   thats  why  it  ranks.   Introducing Relevance
  34. 34. http://www.google.com/ Best  bluetooth  headphones   https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ Rank:  2   Rank  26   Internal  links  count  =  170   Relevance  =  Highest  in  Top  30   Internal  links  count  =  412   Relevance  =  low     Keyword:  “best  bluetooth  headphones”     E-­‐COMM   Introducing Relevance
  35. 35. http://www.google.com/ natural  detox   https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/ Rank:  5   Rank  26   Word  count  =  3180   Internal  links  count  =  375   Interactive  elements  =  398   Word  count  =  6087   Internal  links  count  =  395   Interactive  elements  =  625   Keyword:  “natural  detox”    HEALTH   Introducing Relevance
  36. 36. Content  with  a  high  relevance  score:   •  matches  user  intention   •  Is  logically  structured  and   comprehensive   •  Offers  a  good  user  experience   •  Deals  with  topics  holistically     Being Relevant https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/
  37. 37. #SMX #22A @MarcusTober •  Top  ranking  factors  are  different  depending  on   keyword  set     Relevance  Ranking   Factor   Traditional  Ranking   Factor   Top  10  Ranking  Factors  by  Category   •  Relevance  ranking  factors  dominate  results   across  all  keywords  sets   •  All  previous  examples  can  be  explained  by   having  a  higher  relevance  score   •  This  score  overpowered  other  ranking  factors,   meaning  these  pages  ranked  highly   Key Findings
  38. 38. #SMX #22A @MarcusTober Outlook: this is where we are going
  39. 39. #SMX #22A @MarcusTober •  SEO  is  as  important  as  ever,  but  it’s  changing   This  is  where   we  are  going   •  RankBrain  is  not  used  on  all  queries:  for  example   short/popular  queries  with  well  known  results   are  not  filtered  by  RankBrain  your  content   matches  user/query  intent   •  Relevance  is  crucial  for  good  rankings  –   RankBrain  can  detect  how  relevant  your  content   is   •  Make  sure  your  content  matches  user/query   intent   Outlook for SEO
  40. 40. #SMX #22A @MarcusTober The Future of Search: An abundance of redundance   •  Incremental  improvements  through  powerful   data  insights   Machine  learning  and  Searchmetrics  Share  a  Philosophy:   •  Using  Machine  and  Deep  Learning  to  make   sense  of  complex  data.   •  A  data  driven  approach  is  only  way  to  make   sense  of  the  abundance  of  redundance  online.   •  This  applies  for  content,  too.  
  41. 41. #SMX #22A @MarcusTober Thank you for your attention!
  42. 42. #SMX #22A @MarcusTober SEE YOU AT THE NEXT #SMX! THANK YOU!

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