What Aslam covered - ISU535 Spring 2004 - Guest lecture

0.  Introduction

    * Define the problems of classical filtering (e.g., news filtering,
      spam filtering) and collaborative filtering (e.g., recommendation
    * Compare and contrast:  predictions based upon features (spam
      filtering) vs. predictions based upon similar preferences
      (recommendation systems).
    * Look at real-world examples:  SpamAssassin as used by CCIS;
      collaborative filtering at Amazon.

1.  Filtering, e.g. spam.

    * Features and feature selection.
    * SpamAssassin.
    * Linear classifiers in general, and SpamAssassin as
      a linear classifier.
    * Positively and negatively correlated features.
    * Short introduction to training linear classifiers:
      separable data vs. non-separable data.
    * Evaluation:  (1) simple error rates, (2) false positives
      vs. false negatives and the idea behind "utility functions"
      (though I never mentioned that phrase).

2.  Collaborative filtering, e.g. recommendation systems.

    * Weighted combination of recommendations from
      "like minded" individuals.
    * How to assess "like mindedness?"  Linear correlation
    * Positive correlation vs. negative correlation vs.
      no correlation.

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