This presentation discusses recommender systems and collaborative filtering algorithms. It covers two main types of recommender systems: content-based filtering and collaborative filtering. Content-based filtering uses item attributes and user preferences to recommend similar items, while collaborative filtering relies on user ratings and purchases to find similar users and recommend items they liked. The presentation outlines the key steps and algorithms for each approach, including calculating similarity matrices and using k-nearest neighbors. It also discusses challenges for recommender systems like data sparsity and overfitting.

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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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

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CF > USER & ITEM

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CB > ORDER DATA

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CF > ORDER DATA (cont.)

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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

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CF > SIMILARITY CALCULATION

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CF > USER SIMILARITY MATRIX

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CF > SIMILARITY CALCULATION

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CF > SIMILARITY CALCULATION

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CF > SIMILARITY CALCULATION EXAMPLE

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CF > K-NEAREST-NEIGHBOR

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CF > K-NEAREST-NEIGHBOR

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CF > NEIGHBORS’ ORDER

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CF > REMOVE BOUGHT ITEMS

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CF > CALCULATING FINAL SCORE

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CF > OTHER SIMILARITY MEASURES
More at: http://favi.com.vn/wp-content/uploads/2012/05/pg049_Similarity_Measures_for_Text_Document_Clustering.pdf

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CF > Collaborative Filtering Techniques

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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

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