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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). 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.

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  1. An Algorithmic Framework for Performing CF. Jonathan L. Herlocker. (University of Minnesota).

  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.

  3. 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.

  4. Problem Space (1/3). • Prediction. • how well a user will like an item not been rated. • Formulated problem space as a matrix.

  5. 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].

  6. Weighting Possible Neighbors. • Similarity Weighting. • Weight all users with respect to similarity withthe active user. • Pearson Correlation. • Prediction.

  7. Weighting Possible Neighbors. • Calculation of Prediction (Nathan, Titanic). • Nomalized rating.

  8. Weighting Possible Neighbors. • Weight (variance = 1 )

  9. Weighting Possible Neighbors.

  10. Weighting Possible Neighbors.

  11. Weighting Possible Neighbors.

  12. Weighting Possible Neighbors.

  13. Weighting Possible Neighbors. • Prediction • 3.69+0.02 = 3.69

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