Collaborative filtering and recommender systems.

1. Presentation for the Data Mining course
Collaborative filtering and
recommender systems
Presented by:
Falitokiniaina RABEARISON 30-10-2014
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2. Collaborative filtering and Recommender Systems
Life is too short!
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3. Collaborative filtering and Recommender Systems
Recommender systems
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4. Collaborative filtering and Recommender Systems
AGENDA
Recommender systems
Algorithms
o Content based
o Collaborative Filtering (User Based / Item Based)
Challenges & Comparison
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5. RECOMMENDER SYSTEMS (RS)
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6. Collaborative filtering and Recommender Systems
• RS seen as a function
• Given:
– User model (e g. . ratings ratings, preferences preferences,
demographics demographics, situational situational context)
context)
– Items (with or without description of item characteristics).
• Find:
• - Relevance score. Used for ranking.
• Finally:
– Recommend items that are assumed to be relevant
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7. Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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8. Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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9. Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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10. Collaborative filtering and Recommender Systems
RS >Paradigms of recommender systems
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11. Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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12. Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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13. Collaborative filtering and Recommender Systems
RS > Results
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14. Collaborative filtering and Recommender Systems
Recommender approaches
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15. ALGORITHMS
CONTENT BASED FILTERING (CB)
COLLABORATIVE FILTERING (CF)
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16. Collaborative filtering and Recommender Systems
CB > Content based algorithms
• These rely on the implicit data on the domain
« in a movie recommendation site, this could be the director information,
movie length, PG rating, cast etc. »
« For the song recommendation this could be song date, other
albums/songs from the same group, type of the song (jazz, classi, rock, etc.) »
• Implicit data is used in generating recommendations
« You see that a user has rated high to Brad Pitt movies, so you
recommend her Babel »
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17. Collaborative filtering and Recommender Systems
CB > OBJECT
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18. Collaborative filtering and Recommender Systems
CB > OBJECT INFORMATION
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19. Collaborative filtering and Recommender Systems
CB > FEATURE SET
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21. Collaborative filtering and Recommender Systems
CB > SIMILARITY MATRIX
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22. Collaborative filtering and Recommender Systems
CB > SIMILARITY MEASURE
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23. Collaborative filtering and Recommender Systems
CB > SIMILARITY MEASURE
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24. Collaborative filtering and Recommender Systems
CB > SIMILARITY MATRIX
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25. Collaborative filtering and Recommender Systems
CB > SIMILARITY SORTING
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26. Collaborative filtering and Recommender Systems
CB > K-NEAREST NEIGHBOR (knn)
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27. ALGORITHMS
CONTENT BASED FILTERING (CB)
COLLABORATIVE FILTERING (CF)
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28. Collaborative filtering and Recommender Systems
CF > Collaborative Filtering algorithms
• Other users have impact on the recommendations,
users generate recommendation implicitly.
• Similar users to the active user (user that recommendations
are prepared for) are found.
• By weighting the users, a recommendation list is
prepared from other user data.
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29. Collaborative filtering and Recommender Systems
CF > Basic idea

30. Collaborative filtering and Recommender Systems
CF > Basic idea

31. Collaborative filtering and Recommender Systems
CF > Collaborative Filtering Techniques

32. Collaborative filtering and Recommender Systems
CF > USER & ITEM

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CB > ORDER DATA

34. Collaborative filtering and Recommender Systems
CF > ORDER DATA (cont.)

35. Collaborative filtering and Recommender Systems
CF > ORDER DATA (cont.)

36. Collaborative filtering and Recommender Systems
CF > VECTOR & DIMENSION

37. Collaborative filtering and Recommender Systems
CF > VECTOR & DIMENSION

38. Collaborative filtering and Recommender Systems
CF > VECTORS

39. Collaborative filtering and Recommender Systems
CF > VECTORS

40. Collaborative filtering and Recommender Systems
CF > SIMILARITY CALCULATION

41. Collaborative filtering and Recommender Systems
CF > USER SIMILARITY MATRIX

42. Collaborative filtering and Recommender Systems
CF > SIMILARITY CALCULATION

43. Collaborative filtering and Recommender Systems
CF > SIMILARITY CALCULATION

44. Collaborative filtering and Recommender Systems
CF > SIMILARITY CALCULATION EXAMPLE

45. Collaborative filtering and Recommender Systems
CF > K-NEAREST-NEIGHBOR

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CF > K-NEAREST-NEIGHBOR

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CF > NEIGHBORS’ ORDER

48. Collaborative filtering and Recommender Systems
CF > REMOVE BOUGHT ITEMS

49. Collaborative filtering and Recommender Systems
CF > CALCULATING FINAL SCORE

50. Collaborative filtering and Recommender Systems
CF > OTHER SIMILARITY MEASURES
More at: http://favi.com.vn/wp-content/uploads/2012/05/pg049_Similarity_Measures_for_Text_Document_Clustering.pdf

51. Collaborative filtering and Recommender Systems
CF > Collaborative Filtering Techniques

52. Collaborative filtering and Recommender Systems
CF > ITEM SIMILARITY MATRIX

53. CHALLENGES AND COMPARISON
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54. Collaborative filtering and Recommender Systems
CHALLENGES
• Dimensionality reduction (eg. Use PCA)
• Input data sparsity
• Overfitting to training data set
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55. Collaborative filtering and Recommender Systems
Advantages of CF over CF
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Content based
Recommender
Collaborative
based
Recommender

56. Collaborative filtering and Recommender Systems
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