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Using causal Inference to better understand the search intent
- 1. Using causal inference to better understand the search intent. Dateme Tubotamuno | SemanticGeek SLIDESHARE.NET/DATEMETUBOTAMUNO @DATEMET
- 4. @datemeT #BrightonSEO Complex like the Theory of Yawning
- 6. @datemeT #BrightonSEO Is Sometimes Mistaken for Query Classification Intent Classification
- 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. @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. @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. @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
- 18. @datemeT #BrightonSEO Some sources for intent mining CRM
- 19. @datemeT #BrightonSEO Source: Trey Grainger: https://bit.ly/3ArGoLJ Dimensions of User Intent Content Understanding User Understanding Domain Understanding
- 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. @datemeT #BrightonSEO SEARCH INTENT IS INDEED COMPLEX
- 25. @datemeT #BrightonSEO Poor result for ‘homely animals.’
- 26. @datemeT #BrightonSEO Google Translate is not right for query
- 28. @datemeT #BrightonSEO The power of ‘Cause and Effect’
- 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. @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. @datemeT #BrightonSEO Cause Effect Cause THE SEARCH INTENT IS LIKE A SEQUENCE Purpose HasEvent Search Query HasFirstSubevent Action HasLastSubevent
- 32. @datemeT #BrightonSEO Estimation of Unobserved Quantities
- 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. @datemeT #BrightonSEO Observing effects of going on vacation Or not going on vacation. NOT BOTH
- 35. @datemeT #BrightonSEO Confounder Result/ Action Search Query Intent Confounder is a common cause for cause and effect
- 36. @datemeT #BrightonSEO E.g. of CAUSE relation on ConceptNet
- 37. @datemeT #BrightonSEO Intent as a CAUSE relation Source: https://github.com/commonsense/conceptnet5/wiki/Relations
- 38. @datemeT #BrightonSEO Quora as an intent research resource Source: https://github.com/commonsense/conceptnet5/wiki/Relations
- 42. @datemeT #BrightonSEO List of Keywords that converted
- 43. @datemeT #BrightonSEO Dimension One of the User Intent
- 44. @datemeT #BrightonSEO Dimension Two of the User Intent
- 45. @datemeT #BrightonSEO Dimension Three of the User Intent
- 47. @datemeT #BrightonSEO Definition of variables
- 49. @datemeT #BrightonSEO Reading of the CSV Table in Pandas
- 50. @datemeT #BrightonSEO Generating a Causal Graph View
- 51. @datemeT #BrightonSEO Generating a Causal Estimate
- 52. @datemeT #BrightonSEO A Tiptoe into Intent Analysis using Causal Inference.
- 53. @datemeT #BrightonSEO More Intent Analysis are required in our Industry.
- 54. @datemeT #BrightonSEO Beyond > Transactional, Navigational and Informational
- 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
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