Recipe
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Introduction to Knowledge Based Recommender Systems

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Total time needed: ~2 hours
Learning Objectives
Learn high level theory of basic techniques that go into building Knowledge Based Recommender Systems
Potential Use Cases
Product recommendations, Finding threat actors based on certain criterion, Friend recommendations in a social network
Target Audience
INTERMEDIATEData Scientists and ML Engineers with some high level knowledge of recommender system basics
Go through the following annotated items in order:
VIDEO 1. Conjunctive normal form
  • What is a conjunctive normal form
5 minutes
VIDEO 2. Disjunctive Normal Form
  • What is a disjunctive normal form
5 minutes
BOOK_CHAPTER 3. Search techniques in knowledge based recommender systems
  • How to search and rank relevant results in knowledge based recommender systems
30 minutes
BOOK_CHAPTER 4. Similarity Metrics for Case Based Recommendations
  • How to measure similarity between items in case based recommendations and incorporate diversity in recommendations
30 minutes
BOOK_CHAPTER 5. Critiquing methods in case based recommendations
  • How to incorporate feedback to refine results of case based recommender systems
30 minutes

Concepts Covered

Previewing stream  Recommender Systems
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