An algorithmic framework for performing cf
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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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