Author: Zen and the Art of Computer Programming
With the increasing number of Internet website users, there is a need for a better recommendation system to improve user experience and provide better content services. How to design an effective recommender system is very challenging, especially in the changing environment. The goal of the recommendation system is to recommend the most suitable products or services to users by analyzing various information such as user behavior data and viewing habits. In these environments, how to quickly and accurately recommend accurate content is also very important.
In recent years, many scholars have studied recommendation systems based on multi-view and multi-sensor data fusion in various ways. For example, by using the similarity of text, images, videos and other information to perform association modeling, by analyzing user historical behaviors to build user portraits, by using machine learning and deep learning technology to achieve cross-sorting, and by using reinforcement learning methods for user personalized Guidance etc. However, most research is often limited to a certain type of technology or model, and lacks a comprehensive explanation of the basic principles and advantages and disadvantages of different recommendation systems. This article attempts to analyze the basic principles, theoretical foundations and core algorithms of the recommendation system from the perspective of multi-perspective and multi-sensory data fusion, and conduct an in-depth discussion based on practical application scenarios and methodology.
2. Explanation of basic concepts and terms
2.1 User
Refers to the end user accessing the website, which can be a normal person or a power user. Generally, there are two types: ordinary users and enterprise users.
2.2 Content
Refers to a series of related products, services or knowledge recommended to users, such as movies, music, news, sports games, etc. Usually, a piece of content consists of multiple attributes, such as name, description, cover, author, tags, price, etc.
2.3 Behavioral data
That is, user feedback data on content, including clicks, favorites, shares, comments, etc. Generally speaking, behavior data can be obtained directly, or through logs, behavior tracking and other means.
2.4 Recommendation system
refers to the ability to