Covers: theory of Constraint-based Recommendations
Estimated time needed to finish: 30 minutes
Questions this item addresses:
- How to search and rank relevant results in knowledge based recommender systems
How to use this item?
Read Section 5.2.1 and 5.2.3 of Recommender Systems Book in the url
Author(s) / creator(s) / reference(s)
Charu Aggarwal
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Introduction to Knowledge Based Recommender Systems
Contributors
- 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
- Who is This For ?
- INTERMEDIATEData Scientists and ML Engineers with some high level knowledge of recommender system basics
Click on each of the following annotated items to see details.
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
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