Covers: theory of Mutual Information
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Questions this item addresses:
  • What is mutual information?
  • How to calculate mutual information?
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Author(s) / creator(s) / reference(s)
Ben Lambert
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Feature Selection For Content-based Recommender Systems

Contributors
Total time needed: ~3 hours
Objectives
This list will teach how to represent items using TF-IDF method and two feature selection methods based on Mutual Information (MI) and Chi-square test.
Potential Use Cases
Content-based Recommender Systems are useful in
Who is This For ?
INTERMEDIATEThose who have gone through the "Introduction to Content Based Recommender Systems" Shortlist.
Click on each of the following annotated items to see details.
Resources5/9
BOOK_CHAPTER 1. Supervised Feature Extraction and Weighing
  • Overview of algorithms to extract discriminative features using labelled data
30 minutes
ARTICLE 2. Intuitive Guide to TF-IDF
  • What is TF-IDF?
  • Why is TF-IDF is good for item representation?
10 minutes
ARTICLE 3. Implementation of TF-IDF
  • How can we use TF-IDF to recommend items?
30 minutes
ARTICLE 4. Video on TF-IDF
  • Why use TF-IDF?
  • How to calculate TF and IDF?
5 minutes
ARTICLE 5. Lecture Notes on TF-IDF and Content-Based RecSys
  • How to calculate TF-IDF?
  • How can the TF-IDF be improved?
15 minutes
ARTICLE 6. Short Video on Mutual Information
  • What is mutual information?
  • How to calculate mutual information?
10 minutes
ARTICLE 7. Mutual Information for Feature Selection
  • How we solve for mutual information?
50 minutes
ARTICLE 8. Chi-square distribution
  • What is Chi-square distribution?
  • What is Chi-square test?
10 minutes
ARTICLE 9. Implementation of Chi-square Feature Selection
  • How can we use Python to implement Chi-square feature selection?
40 minutes

Concepts Covered

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