Recommender systems handbook 6

Recommender systems handbook 6

This week I saw Chapter 1. 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 eight 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.
Chapter 5: Improvement in Collaborative Filtering;
Chapter 6: Develop a constraint-based recommender;
Chapter 7: context-aware Recommendation Systems: conventional recommendation systems only consider user and item, while context-aware recommendation systems consider "context information" as well. For example, the recommendation of travel websites should be very different in winter and summer. For example, the recommendation of news websites should take time into consideration. users prefer to pay attention to current affairs news and stock market information on weekdays, over the weekend, I would like to pay more attention to movie reviews and shopping information;
Chapter 8: evaluation and recommendation system
Chapter 9: A recommendation system for IPTV service providers: a large-scale product environment. This section describes a recommendation system in a VoD System. The challenge is that the identity of the user who operates the remote control needs to be determined in real time (the solution is to differentiate the user based on the time period)
Chapter 10: how to obtain a recommendation system outside the laboratory: Describes the considerations for building a recommendation system for practical applications;
Chapter 2: matching recommendation technology and fields: Introduce the applicable recommendation technology and Algorithms in different application scenarios;
Chapter 2: Recommendation System in technology enhanced learning;