Covers: theory of User-User and Item-Item Similarity
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Questions this item addresses:
  • Measure similarity between two vectors
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Wath the entire youtube video in the link

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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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