Covers: theory of Graph Models for Neighbourdhood Based Methods
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- Graph Mdoels for Neighbourhood Methods
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Neighbourhood Collaborative Filtering Approaches for Recommender Systems
Contributors
- Objectives
- Various neighbourhood based approaches for collaborative filtering
- Potential Use Cases
- Recommender systems, Discovering friends in a Social Media Application, Clustering similar threat actors by behaviour
- Who is This For ?
- INTERMEDIATE
Click on each of the following annotated items to see details.
VIDEO 1. Pearson Coefficient for Similarity
- Measuring similarity between two entities
10 minutes
VIDEO 2. Cosine Similarity of Vectors
- Measure similarity between two vectors
10 minutes
BOOK_CHAPTER 3. Overview of user-user and item-item similarity
- Various approaches to measure user-user and item-item similarity
10 minutes
VIDEO 4. Overview of K-Means Clustering
- Overview of K-Means Clustering
10 minutes
BOOK_CHAPTER 5. Clustering based Neighbourhood Methods
- Optimizing recommender system runtime performance with clustering based methods
10 minutes
ARTICLE 6. Introduction to Graph Structure
- Graph Mdoels for Neighbourhood Methods
10 minutes
VIDEO 7. BiPartite Graphs
- Graph Mdoels for Neighbourhood Methods
10 minutes
BOOK_CHAPTER 8. Katz Similarity Measure
- Similarity measure in Graph Models
10 minutes
BOOK_CHAPTER 9. User-user Graphs
- User-user graphs for User Similarity
10 minutes
Concepts Covered
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