11. Recommender Systems

Sections

Navigate through the learning materials and practice exercises.

1. 11

   Recommender Systems

   Learn Practice

   Recommender systems are algorithms designed to suggest relevant items to...

2. 11.1

   What Are Recommender Systems?

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   Recommender systems are algorithms that suggest relevant items to users...

3. 11.2

   Types Of Recommender Systems

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   This section discusses the major types of recommender systems, namely...

4. 11.2.1

   Content-Based Filtering

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   Content-based filtering recommends items similar to those previously liked...

5. 11.2.2

   Collaborative Filtering

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   Collaborative filtering is a method that recommends items to users based on...

6. 11.2.2.a

   User-Based Collaborative Filtering

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   User-based collaborative filtering recommends items to users based on the...

7. 11.2.2.b

   Item-Based Collaborative Filtering

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   Item-based collaborative filtering recommends items to users based on the...

8. 11.2.3

   Hybrid Methods

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   Hybrid methods in recommender systems combine content-based filtering and...

9. 11.3

   Data Requirements

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   This section outlines the essential data required to build effective...

10. 11.4

    Core Algorithms

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    Core algorithms are the backbone of recommender systems, including methods...

11. 11.4.1

    Nearest Neighbor Models

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    Nearest Neighbor Models are algorithms used in collaborative filtering to...

12. 11.4.2

    Matrix Factorization

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    Matrix factorization techniques decompose user-item interaction data into...

13. 11.4.3

    Deep Learning Approaches

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    Deep learning approaches enhance recommender systems by modeling complex...

14. 11.4.4

    Association Rule Mining

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    Association Rule Mining is a key algorithm in recommender systems that helps...

15. 11.5

    Cold Start And Sparsity Problems

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    The section covers cold start and sparsity issues in recommender systems,...

16. 11.5.1

    Cold Start

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    Cold Start refers to the challenge faced by recommender systems when new...

17. 11.5.2

    Sparsity

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    Sparsity in recommender systems refers to the challenge encountered when...

18. 11.6

    Evaluation Of Recommender Systems

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    This section discusses methods for evaluating the performance of recommender...

19. 11.6.1

    Offline Evaluation

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    This section discusses offline evaluation methods for recommender systems,...

20. 11.6.2

    Online Evaluation

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    Online evaluation of recommender systems involves assessing their...

21. 11.7

    Building A Simple Recommender In Python (Collaborative Filtering)

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    This section presents a practical implementation of a simple recommender...

22. 11.8

    Real-World Case Studies

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    This section analyzes real-world applications of recommender systems,...

23. 11.8.1

    Netflix

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    Netflix uses advanced recommender systems to personalize viewing experiences...

24. 11.8.2

    Amazon

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    Amazon utilizes item-to-item collaborative filtering for product...

25. 11.8.3

    Spotify

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    Spotify employs a hybrid approach to its recommender systems, utilizing both...

26. 11.9

    Trends And Future Directions

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    This section discusses emerging trends and future directions in recommender...

27. 11.9.1

    Context-Aware Recommender Systems

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    Context-aware recommender systems enhance personalization by considering...

28. 11.9.2

    Reinforcement Learning

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    Reinforcement Learning models recommendations as actions over time, adapting...

29. 11.9.3

    Federated Learning

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    Federated learning is a privacy-focused approach that enables machine...

30. 11.9.4

    Explainable Recommendations

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    Explainable recommendations enhance user trust by clarifying why certain...