Covers: theory of Ensemble Based Systems
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Questions this item addresses:
  • 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?
How to use this item?

Read Sections 6.3 - 6.5 1. Ensemble systems are generally employed to increase the robustness of the system.

Section 6.3 2. Weighted Ensemble: We build various recommendation systems in parallel and combine their outputs by weighing and adding them. The weights are learned using Linear Regression or some form of Gradient Descent that minimises the error combining the results.

  1. Section 6.3.2. Explains how bagging in classification can be applied to Recommendation Systems.

Section 6.4 4. Switching Ensemble: The idea here is that we use model M1 initially that addresses cold-start problem and later switch to a better model as we gather more data. For ex: one can start with knowledge based recommendation system at the start and later switch to collaborative recommender.

Section 6.5 5. Cascade Ensemble: The basic idea is something like a successive improvement in output by improving upon the previous technique. Boosting (https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote19.html) is an example of such a method where error in predictions of one model is used build a refined model that reduces that error.

Section 6.6 6. Feature Augmentation Ensemble: This is similar to stacking (https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/) where the output of a model is used as a feature in a subsequent model. Note that unlike cascade models the error in prediction is not considered.

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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,
Click on each of the following annotated items to see details.
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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