[final]collaborative filtering and recommender systems

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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