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Using causal Inference to better understand the search intent

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Using causal Inference to better understand the search intent

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Causal inference helps us understand the question of why. In this talk, I will demonstrate the power of causality in understanding user intent during keyword research and performance analysis. The user intent is beyond transactional, informational, and navigational classifications

Causal inference helps us understand the question of why. In this talk, I will demonstrate the power of causality in understanding user intent during keyword research and performance analysis. The user intent is beyond transactional, informational, and navigational classifications

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Using causal Inference to better understand the search intent

  1. 1. Using causal inference to better understand the search intent. Dateme Tubotamuno | SemanticGeek SLIDESHARE.NET/DATEMETUBOTAMUNO @DATEMET
  2. 2. Graph Theory Family Marathon Knowledge Graphs Knowledge Representation & Reasoning Cognitive semantics Arsenal F.C Commonsense Knowledge Graph Semantic Geek
  3. 3. @datemeT #BrightonSEO The search intent is complex
  4. 4. @datemeT #BrightonSEO Complex like the Theory of Yawning
  5. 5. @datemeT #BrightonSEO Search Intent & User Intent are Identical
  6. 6. @datemeT #BrightonSEO Is Sometimes Mistaken for Query Classification Intent Classification
  7. 7. @datemeT #BrightonSEO CROCODILE ALLIGATOR
  8. 8. @datemeT #BrightonSEO Classification of Location Queries
  9. 9. @datemeT #BrightonSEO E.g: How do you get to Jersey from london Source: https://www.vs.inf.ethz.ch/edu/SS2006/DS/slides/01_location_models.pdf 1 E.g: Where is the Brighton Centre? Position Queries
  10. 10. @datemeT #BrightonSEO E.g: How do you get to Jersey from london Source: https://www.vs.inf.ethz.ch/edu/SS2006/DS/slides/01_location_models.pdf 2 E.g: Hotels near Brighton Centre? Nearest Neighbour Queries
  11. 11. @datemeT #BrightonSEO E.g: How do you get to Jersey from london Source: https://www.vs.inf.ethz.ch/edu/SS2006/DS/slides/01_location_models.pdf 3 E.g: How do you get to Brighton from London? Navigation Queries
  12. 12. @datemeT #BrightonSEO E.g: How do you get to Jersey from london Source: https://www.vs.inf.ethz.ch/edu/SS2006/DS/slides/01_location_models.pdf 4 E.g: Caesars Palace top floor suite ? Range Queries
  13. 13. @datemeT #BrightonSEO Source: https://www.thinkwithgoogle.com/consumer-insights/consumer-journey/i-want-to-go-micro- moments/
  14. 14. @datemeT #BrightonSEO or Intent Classification Intent Mining?
  15. 15. @datemeT #BrightonSEO
  16. 16. @datemeT #BrightonSEO Intent ‘What’ Intent ‘Why’
  17. 17. @datemeT #BrightonSEO
  18. 18. @datemeT #BrightonSEO Some sources for intent mining CRM
  19. 19. @datemeT #BrightonSEO Source: Trey Grainger: https://bit.ly/3ArGoLJ Dimensions of User Intent Content Understanding User Understanding Domain Understanding
  20. 20. @datemeT #BrightonSEO Content Understanding
  21. 21. @datemeT #BrightonSEO User Understanding
  22. 22. @datemeT #BrightonSEO Domain Understanding
  23. 23. @datemeT #BrightonSEO "Search is far from a solved Problem" Pandu Nayak - Head of Search Ranking, Google https://www.youtube.com/watch?v=tFq6Q_muwG0
  24. 24. @datemeT #BrightonSEO SEARCH INTENT IS INDEED COMPLEX
  25. 25. @datemeT #BrightonSEO Poor result for ‘homely animals.’
  26. 26. @datemeT #BrightonSEO Google Translate is not right for query
  27. 27. @datemeT #BrightonSEO CAUSAL INFERENCE
  28. 28. @datemeT #BrightonSEO The power of ‘Cause and Effect’
  29. 29. @datemeT #BrightonSEO Cause and Effect Causal Inference Causal Calculus https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi- de2cde8ea599 Inferring Causes from data
  30. 30. @datemeT #BrightonSEO Source: https://github.com/commonsense/conceptnet5/wiki/Relations P(x|do(y)) i.e. the probability of x given that y is done
  31. 31. @datemeT #BrightonSEO Cause Effect Cause THE SEARCH INTENT IS LIKE A SEQUENCE Purpose HasEvent Search Query HasFirstSubevent Action HasLastSubevent
  32. 32. @datemeT #BrightonSEO Estimation of Unobserved Quantities
  33. 33. @datemeT #BrightonSEO CI questions around a vacation query Source: https://www.kdnuggets.com/2020/08/microsoft-dowhy-framework-causal-inference.html Would we have fun? How would we feel after? What should we do on vacation?
  34. 34. @datemeT #BrightonSEO Observing effects of going on vacation Or not going on vacation. NOT BOTH
  35. 35. @datemeT #BrightonSEO Confounder Result/ Action Search Query Intent Confounder is a common cause for cause and effect
  36. 36. @datemeT #BrightonSEO E.g. of CAUSE relation on ConceptNet
  37. 37. @datemeT #BrightonSEO Intent as a CAUSE relation Source: https://github.com/commonsense/conceptnet5/wiki/Relations
  38. 38. @datemeT #BrightonSEO Quora as an intent research resource Source: https://github.com/commonsense/conceptnet5/wiki/Relations
  39. 39. @datemeT #BrightonSEO
  40. 40. @datemeT #BrightonSEO
  41. 41. @datemeT #BrightonSEO
  42. 42. @datemeT #BrightonSEO List of Keywords that converted
  43. 43. @datemeT #BrightonSEO Dimension One of the User Intent
  44. 44. @datemeT #BrightonSEO Dimension Two of the User Intent
  45. 45. @datemeT #BrightonSEO Dimension Three of the User Intent
  46. 46. @datemeT #BrightonSEO Causal Inference in Action
  47. 47. @datemeT #BrightonSEO Definition of variables
  48. 48. @datemeT #BrightonSEO Modules Installation
  49. 49. @datemeT #BrightonSEO Reading of the CSV Table in Pandas
  50. 50. @datemeT #BrightonSEO Generating a Causal Graph View
  51. 51. @datemeT #BrightonSEO Generating a Causal Estimate
  52. 52. @datemeT #BrightonSEO A Tiptoe into Intent Analysis using Causal Inference.
  53. 53. @datemeT #BrightonSEO More Intent Analysis are required in our Industry.
  54. 54. @datemeT #BrightonSEO Beyond > Transactional, Navigational and Informational
  55. 55. @datemeT #BrightonSEO Resources ● https://medium.com/analytics-vidhya/causal-inference-an-introduction- f424df7c76ef ● https://medium.com/data-science-at-microsoft/causal-inference-part-1-of-3- understanding-the-fundamentals-816f4723e54a ● https://www.kdnuggets.com/2020/08/microsoft-dowhy-framework-causal- inference.html ● https://github.com/microsoft/EconML/blob/master/notebooks/CustomerSc enarios/Case%20Study%20- %20Customer%20Segmentation%20at%20An%20Online%20Media%20Com pany%20-%20EconML%20%2B%20DoWhy.ipynb ● https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for- causal-inference/ ● https://www.slideshare.net/treygrainger/balancing-the-dimensions-of-user- intent
  56. 56. @datemeT #BrightonSEO

Editor's Notes

  • caesars palace top floor suite
  • caesars palace top floor suite
  • caesars palace top floor suite
  • caesars palace top floor suite

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