Recommender system
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The document discusses recommender systems which help users find items that meet their needs. It describes three common recommendation approaches: collaborative filtering, content-based filtering, and hybrid recommender systems. Collaborative filtering makes predictions about a user's interests based on preferences of many users, while content-based filtering relies on descriptions of items and a user's preference profile. The document also outlines the speaker's research into a hybrid food recommender system that recommends restaurants and foods based on a user's taste preferences and location.
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Recommender system
- 1. TechTalk #51 Behind the sceene a RECOMMENDER SYSTEM Arif Akbarul Huda
- 3. filtering content user perspektive
- 4. are you familiar.. ?
- 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. a recommendation system... how its work?
- 7. Recommender system (RS) help users find items (e.g., news items, movies) that meet their specific needs.
- 8. 3 common approach 1.collaborative filtering 2.content-based filtering 3.hybrid recommender system
- 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. USER & ITEM http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 12. 13 ORDER DATA http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 13. 14 ORDER DATA (cont.) http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 14. 15 ORDER DATA (cont.) http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 15. 16 VECTOR & DIMENSION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 16. 17 VECTOR & DIMENSION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 19. 20 SIMILARITY CALCULATION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 20. 21 USER SIMILARITY MATRIX http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 21. 22 SIMILARITY CALCULATION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 22. 23 SIMILARITY CALCULATION http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 23. 24 SIMILARITY CALCULATION EXAMPLE http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 26. 27 NEIGHBORS’ ORDER http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 27. 28 REMOVE BOUGHT ITEMS http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 28. 29 CALCULATING FINAL SCORE http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
- 30. Content Based Filtering based on a description of the item and a profile of the user’s preference (Brusilovsky Peter , 2007)
- 32. 33 OBJECT INFORMATION http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
- 33. 34 FEATURE SET http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
- 35. 36 SIMILARITY MATRIX http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
- 36. 37 SIMILARITY MEASURE http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
- 37. 38 SIMILARITY MEASURE http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
- 38. 39 SIMILARITY MATRIX http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
- 39. 40 SIMILARITY SORTING http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
- 40. 41 K-NEAREST NEIGHBOR (knn) http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
- 41. Hybrid
- 42. Hybrid • CF+CB • CF+ context-aware • CF+CB+Demographic • .....
- 43. my research....
- 44. a food food has characteristic of taste (measure by level) : - sweet - bitter - savory - salty - sour - spicy - sauce - meat - vegetable
- 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. end