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Collaborative filtering and recommender systems.
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Collaborative filtering and recommender systems
Presented by:
Falitokiniaina RABEARISON 30-10-2014
Presentation for the Data Mining course
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Collaborative filtering and Recommender Systems
Life is too short!
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Collaborative filtering and Recommender Systems
Recommender systems
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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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RECOMMENDER SYSTEMS (RS)
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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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Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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Collaborative filtering and Recommender Systems
RS > Paradigms of recommender systems
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Collaborative filtering and Recommender Systems
RS > Results
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Collaborative filtering and Recommender Systems
Recommender approaches
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CONTENT BASED FILTERING (CB)
ALGORITHMS
COLLABORATIVE FILTERING (CF)
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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, otheralbums/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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Collaborative filtering and Recommender Systems
CB > OBJECT
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Collaborative filtering and Recommender Systems
CB > OBJECT INFORMATION
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Collaborative filtering and Recommender Systems
CB > FEATURE SET
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Collaborative filtering and Recommender Systems
CB > SIMILARITY MATRIX
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Collaborative filtering and Recommender Systems
CB > SIMILARITY MEASURE
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Collaborative filtering and Recommender Systems
CB > SIMILARITY MEASURE
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Collaborative filtering and Recommender Systems
CB > SIMILARITY MATRIX
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Collaborative filtering and Recommender Systems
CB > SIMILARITY SORTING
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Collaborative filtering and Recommender Systems
CB > K-NEAREST NEIGHBOR (knn)
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COLLABORATIVE FILTERING (CF)
ALGORITHMS
CONTENT BASED FILTERING (CB)
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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 isprepared from other user data.
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Collaborative filtering and Recommender Systems
CF > Basic idea
Collaborative filtering and Recommender Systems
CF > Basic idea
Collaborative filtering and Recommender Systems
CF > Collaborative Filtering Techniques
Collaborative filtering and Recommender Systems
CF > USER & ITEM
Collaborative filtering and Recommender Systems
CB > ORDER DATA
Collaborative filtering and Recommender Systems
CF > ORDER DATA (cont.)
Collaborative filtering and Recommender Systems
CF > ORDER DATA (cont.)
Collaborative filtering and Recommender Systems
CF > VECTOR & DIMENSION
Collaborative filtering and Recommender Systems
CF > VECTOR & DIMENSION
Collaborative filtering and Recommender Systems
CF > VECTORS
Collaborative filtering and Recommender Systems
CF > VECTORS
Collaborative filtering and Recommender Systems
CF > SIMILARITY CALCULATION
Collaborative filtering and Recommender Systems
CF > USER SIMILARITY MATRIX
Collaborative filtering and Recommender Systems
CF > SIMILARITY CALCULATION
Collaborative filtering and Recommender Systems
CF > SIMILARITY CALCULATION
Collaborative filtering and Recommender Systems
CF > SIMILARITY CALCULATION EXAMPLE
Collaborative filtering and Recommender Systems
CF > K-NEAREST-NEIGHBOR
Collaborative filtering and Recommender Systems
CF > K-NEAREST-NEIGHBOR
Collaborative filtering and Recommender Systems
CF > NEIGHBORS’ ORDER
Collaborative filtering and Recommender Systems
CF > REMOVE BOUGHT ITEMS
Collaborative filtering and Recommender Systems
CF > CALCULATING FINAL SCORE
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
Collaborative filtering and Recommender Systems
CF > Collaborative Filtering Techniques
Collaborative filtering and Recommender Systems
CF > ITEM SIMILARITY MATRIX
CHALLENGES AND COMPARISON
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Collaborative filtering and Recommender Systems
CHALLENGES
• Dimensionality reduction (eg. Use PCA)
• Input data sparsity
• Overfitting to training data set
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Collaborative filtering and Recommender Systems
Advantages of CF over CF
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Content based Recommender
Collaborative based
Recommender
Collaborative filtering and Recommender Systems
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