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Recommender system

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introducing to you about behind the scene of recommender engine.

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Recommender system

  1. 1. TechTalk #51 Behind the sceene a RECOMMENDER SYSTEM Arif Akbarul Huda
  2. 2. increasing information data
  3. 3. filtering content user perspektive
  4. 4. are you familiar.. ?
  5. 5. why do we need a recommender engine? • Increase the number of items sold • Sell more diverse items • Increase the user satisfaction • Increase user fidelity • Better understand what the user wants
  6. 6. a recommendation system... how its work?
  7. 7. Recommender system (RS) help users find items (e.g., news items, movies) that meet their specific needs.
  8. 8. 3 common approach 1.collaborative filtering 2.content-based filtering 3.hybrid recommender system
  9. 9. Content Based Filtering
  10. 10. collaborative filtering a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating)
  11. 11. USER & ITEM http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  12. 12. 13 ORDER DATA http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  13. 13. 14 ORDER DATA (cont.) http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  14. 14. 15 ORDER DATA (cont.) http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  15. 15. 16 VECTOR & DIMENSION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  16. 16. 17 VECTOR & DIMENSION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  17. 17. 18 VECTORS http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  18. 18. 19 VECTORS http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  19. 19. 20 SIMILARITY CALCULATION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  20. 20. 21 USER SIMILARITY MATRIX http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  21. 21. 22 SIMILARITY CALCULATION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  22. 22. 23 SIMILARITY CALCULATION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  23. 23. 24 SIMILARITY CALCULATION EXAMPLE http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  24. 24. 25 K-NEAREST-NEIGHBOR http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  25. 25. 26 K-NEAREST-NEIGHBOR http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  26. 26. 27 NEIGHBORS’ ORDER http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  27. 27. 28 REMOVE BOUGHT ITEMS http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  28. 28. 29 CALCULATING FINAL SCORE http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  29. 29. Content Based Filtering
  30. 30. Content Based Filtering based on a description of the item and a profile of the user’s preference (Brusilovsky Peter , 2007)
  31. 31. OBJECT http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
  32. 32. 33 OBJECT INFORMATION http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  33. 33. 34 FEATURE SET http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  34. 34. 35 http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  35. 35. 36 SIMILARITY MATRIX http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  36. 36. 37 SIMILARITY MEASURE http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  37. 37. 38 SIMILARITY MEASURE http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  38. 38. 39 SIMILARITY MATRIX http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  39. 39. 40 SIMILARITY SORTING http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  40. 40. 41 K-NEAREST NEIGHBOR (knn) http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
  41. 41. Hybrid
  42. 42. Hybrid • CF+CB • CF+ context-aware • CF+CB+Demographic • .....
  43. 43. my research....
  44. 44. a food food has characteristic of taste (measure by level) : - sweet - bitter - savory - salty - sour - spicy - sauce - meat - vegetable
  45. 45. user item • previous taste preference a model... • rating • comment • comment • current location • Restoran => foods feedback recommended item - Restoran with foods that meet user taste preferences
  46. 46. end
  • BhatNeha

    Jun. 6, 2018
  • fanqie123

    Apr. 22, 2016
  • alireza19330

    Jul. 19, 2015
  • WidiRia

    Apr. 25, 2015
  • hanaauliana

    Dec. 5, 2014

introducing to you about behind the scene of recommender engine.

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