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Collaborative Filtering Qiang Yang HKUST Thanks: Sonny Chee Motivation Question: A user bought some products already what other products to recommend to a user? Collaborative Filtering (CF) Automates “circle of advisors”. + Example Which movie would Sammy watch next? Ratings 1--5

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Collaborative filtering l.jpg

Collaborative Filtering

Qiang Yang

HKUST

Thanks: Sonny Chee


Motivation l.jpg
Motivation

  • Question:

    • A user bought some products already

    • what other products to recommend to a user?

  • Collaborative Filtering (CF)

    • Automates “circle of advisors”.

+


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Example

  • Which movie would Sammy watch next?

    • Ratings 1--5

  • If we just use the average of other users who voted on these movies, then we get

    • Matrix= 3; Titanic= 14/4=3.5

    • Recommend Titanic!

  • But, is this reasonable?


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Types of Collaborative Filtering Algorithms

  • Collaborative Filters

    • Statistical Collaborative Filters

    • Probabilistic Collaborative Filters [PHL00]

    • Bayesian Filters [BP99][BHK98]

    • Association Rules [Agrawal, Han]

  • Open Problems

    • Sparsity, First Rater, Scalability


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Statistical Collaborative Filters

  • Users annotate items with numeric ratings.

  • Users who rate items “similarly” become mutual advisors.

  • Recommendation computed by taking a weighted aggregate of advisor ratings.


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Basic Idea

  • Nearest Neighbor Algorithm

  • Given a user a and item i

    • First, find the the most similar users to a,

      • Let these be Y

    • Second, find how these users (Y) ranked i,

    • Then, calculate a predicted rating of a on i based on some average of all these users Y

      • How to calculate the similarity and average?


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Statistical Filters

  • GroupLens [Resnick et al 94, MIT]

    • Filters UseNet News postings

    • Similarity: Pearson correlation

    • Prediction: Weighted deviation from mean



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Pearson Correlation

  • Weight between users a and u

    • Compute similarity matrix between users

      • Use Pearson Correlation (-1, 0, 1)

      • Let items be all items that users rated


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Prediction Generation

  • Predicts how much a user a likes an item i

    • Generate predictions using weighted deviation from the mean

  • : sum of all weights

(1)


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Error Estimation

  • Mean Absolute Error (MAE) for user a

  • Standard Deviation of the errors


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Example

Correlation

Sammy

Dylan

Mathew

Sammy

1

1

-0.87

Dylan

1

1

0.21

Users

Mathew

-0.87

0.21

1

=0.83


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Open Problems in CF

  • “Sparsity Problem”

    • Many entries are missing with ?

  • “First Rater Problem” (cold start problem)

    • The first person to rate an item has no data to rely on