Author: Zen and the Art of Computer Programming
1 Introduction
overview
The recommendation system (English Recommendation System) is a system formed based on various factors such as information given by users, behavioral data, and similarity relationship analysis. Its purpose is to provide users with a series of products or services that match their interests and preferences. Since users are constantly acquiring new information and behavioral data when purchasing, communicating or consuming, the recommendation system helps users quickly find content of interest, discover new products, improve user satisfaction and Promote popularization of products or services. With the rapid development of Internet technology, Internet-based recommendation systems have gradually become a part of the "network world".
feature
The recommendation system mainly has the following characteristics:
Personalized recommendation: The recommendation system pushes suitable products or services based on the user's history, preferences, habits, etc. For example, for shopping websites, the recommendation system will recommend products of the same category to users; for Weibo, the recommendation system will recommend the most popular topic posts for users; for music, movies, and e-commerce websites , the recommendation system will recommend the user's favorite genre.
Content-based: The recommendation system makes recommendations based on the user's hobbies, preferences, etc. For example, the user's gender, age, living location, education level and other information will affect the recommendation results; the user's favorite movies, music, games and other content will also affect the recommendation results.
Scalability: The recommendation system can customize the recommendation results according to the user's usage habits, needs and system resource constraints. For example, if the user frequently uses certain types of products, the recommendation system may recommend this type of product; if the user has no idle time, the recommendation system will recommend some leisure puzzle applications.
Automated recommendation: The recommendation system can generate recommendation results in real time. For example, when a user visits an e-commerce website, the recommendation system can generate personalized recommendations in real time based on the user's shopping records and browsing behavior&#x