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An Algorithmic Framework for Performing CF. Jonathan L. Herlocker. (University of Minnesota). Introduction (1/2). Content-based filtering. Compares contents in the documents to contents interesting the user. Locating textual documents relevant to a topic using techniques.

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an algorithmic framework for performing cf

An Algorithmic Framework for Performing CF.

Jonathan L. Herlocker.

(University of Minnesota).

introduction 1 2
Introduction (1/2).
  • Content-based filtering.
    • Compares contents in the documents tocontents interesting the user.
    • Locating textual documents relevant toa topic using techniques.

☞ vector-space queries, “intelligent” agents, information visualization.

introduction 2 2
Introduction (2/2).
  • Auto collaborative filtering.
    • Collects human judgments (rating).
    • Matches together people in same tastes.
  • What collaborative filtering provides.
    • Supporting automated processes.
    • Filtering items based on quality and taste.
    • Useful personalized recommendations.
problem space 1 3
Problem Space (1/3).
  • Prediction.
    • how well a user will like an item not been rated.
    • Formulated problem space as a matrix.
problem space 2 3
Problem Space (2/3).
  • Algorithms for Collaborative filtering.
    • Neighborhood-based methods.
      • A subset of users are chosen.
      • A weighted aggregate of ratings is used.
    • Bayesian networks[5],singular value decomposition with neural net classification[4],induction rule learning[3].
weighting possible neighbors
Weighting Possible Neighbors.
  • Similarity Weighting.
    • Weight all users with respect to similarity withthe active user.
    • Pearson Correlation.
    • Prediction.
weighting possible neighbors1
Weighting Possible Neighbors.
  • Calculation of Prediction (Nathan, Titanic).
    • Nomalized rating.
weighting possible neighbors2
Weighting Possible Neighbors.
  • Weight (variance = 1 )
weighting possible neighbors7
Weighting Possible Neighbors.
  • Prediction
    • 3.69+0.02 = 3.69
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