Covers: theory of Hybrid Recommendation Systems
Estimated time needed to finish: 5 minutes
Questions this item addresses:
  • What are hybrid Systems?
  • Why do we need hybrid systems?
How to use this item?

Read Section 6.1 It would be useful if you are able to understand the following

  1. What is cold-start problem (https://www.coursera.org/lecture/collaborative-filtering/the-cold-start-problem-8MtoR) and why knowledge-based systems outperform content-based or collaborative systems?
  2. What kind of models perform better with various kinds of data that are available.
  3. Have a basic understanding of bias and variance of a machine learning system. In short bias represents how restrictive a particular model is whereas variance represents how finicky the model is to random variations of the data. (https://www.youtube.com/watch?v=EuBBz3bI-aA&list=PLblh5JKOoLUIcdlgu78MnlATeyx4cEVeR - explains this in detail)
Author(s) / creator(s) / reference(s)
Charu Aggarwal, internet
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Ensemble Based And Hybrid Recommender Systems

Contributors
Total time needed: ~5 minutes
Objectives
Gives a basic idea of combining multiple individual recommendation systems to build a robust high-performing Recommendation System
Potential Use Cases
Build a Hybrid Recommendation System that combines the strengths and avoids the weakness of different techniques for building a Recommendation System.
Who is This For ?
INTERMEDIATEPeople with familiarity with various Recommendation System Building techniques like collaborative filtering, knowledge and content based recommendations,
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Resources4/4
ARTICLE 1. Hybrid Recommendation Systems
  • What are hybrid Systems?
  • Why do we need hybrid systems?
5 minutes
ARTICLE 2. Ensemble Based Recommendation Systems
  • What are ensemble based systems and why do we need them?
  • What is a Weighted Ensemble?
  • What is a Switching Ensemble?
  • What is a Cascade Ensemble?
  • What is a Feature Augmentation Ensemble?
10 minutes
ARTICLE 3. Monolith Recommendation Systems
  • What are monolith based Recommendation Systems?
  • What is a Meta-level Hybrid Recommendation System?
  • What is Feature Combination Hybrid Recommendation System?
10 minutes
ARTICLE 4. Mixed Recommendation Systems
  • What are mixed Hybrids? Why do we need them?
10 minutes

Concepts Covered

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