Recommender systems Handbook (2)

Recommender systems Handbook (2)

 

This week, I have read chapter 4. The book consists of 25 chapters.

From the point of view, this book provides a comprehensive introduction to the recommendation system, and also introduces some specificAlgorithm. There are some mathematical symbols in these formulas that I can't remember.

The following is a summary of the first four chapters:
Chapter 1: Introduction to the book;
Chapter 2: Data mining methods used in recommendation systems, divided into: Data Processing (similarity measurement, sampling, dimensionality reduction, and noise reduction), classification (specific algorithms include nearest neighbor, decision tree, rule-based classification, Bayesian classification, artificial neural network, and support vector machine), clustering analysis, and Association Rule Mining
Chapter 3: Content-based recommendation system: State of the Art and trends.
Chapter 4: Overview of neighbor-based recommendation Methods.

The following content is excerpted from Chapter 4:

There are three types of information searches:
1: Search objects are clearly identifiable;
2: The search object cannot be completely described, but can be recognized at a glance;
3: information is obtained by accident or accident;