Author: Zen and the Art of Computer Programming
1 Introduction
Recommendation System (Recommendation System) is a technical system that makes personalized recommendations based on user interests and preferences. By analyzing user behavior data, the recommendation system can quickly and accurately recommend products or services to users and improve user experience. Its applications range from e-commerce, Internet social networking to media search engines can draw on its advantages. The recommendation system based on user behavior data generally includes three models: collaborative filtering model, matrix decomposition model, and probability model. The main goal of the recommendation system is to help users find the goods or services they are interested in. The user's willingness to purchase is based on the degree of liking for a certain product or service, while the recommendation system is based on the user's past purchase history, browsing history, search Records and other behavioral data are comprehensively analyzed to obtain a collection of favorite items for each user and recommended to the user. Therefore, the recommendation system has the following characteristics:
Based on user interests: The recommendation system mainly makes recommendations based on various behavioral data of users. By analyzing these data, the recommender system can help users discover their favorite genres and recommend products of similar genres. For example, after a user purchases a product, he may also like to buy another product, and the recommendation system will recommend two products;
Personalized recommendation: The recommendation system makes personalized recommendations for different users based on various factors such as the user's personal information and preference settings. For some users, it recommends products that meet their specific preferences, such as shirts, shoes, etc. that women may like to wear with fashionable clothing;
Incremental learning: The recommender system is real-time, it can quickly adapt to new products or services. When a new product or service is launched, the recommendation system can immediately update its recommendation results to make it closer to user needs;
Intelligent: The recommendation system should not only consider the personal situation of the user, but also take into account the current trend and the advantages of competitors. For example, when a new product comes out, it should give priority to meeting the consumer demand of the market and not blindly follow the trend;
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