Internet information product recommending method based on matrix decomposition

Abstract

The invention discloses an internet information product recommending method based on matrix decomposition. The method comprises the following steps of: 1) obtaining the user scoring record to the information product; 2) obtaining the social relationship record between internet users; 3) respectively building a scoring matrix and a social matrix according to the types of a target user and a target product; 4) learning a user feature vector and a product feature vector through a matrix decomposition technology; 5) calculating the scores of different products scored by the target user according to the feature vectors, so as to recommending the favorite products of the user according to the scores. In the method, analysis on user social relationship is introduced, and personalized product recommendation is provided for the target user based on the production type information. The calculation is simple and quick, and the method has better expandability and adaptability, so that the method is suitable for highly dynamic and immense amount of product-oriented recommendation for the internet users.

Description

technical field [0001] The present invention relates to the recommendation of Internet information products, especially for the social information between users and the rating information of users for products in the website system, how to effectively use the social relationship between different users, and according to the historical rating data of users for products, quickly and accurately Predict the possible ratings of users for their unrated products, and recommend personalized information products for target users. Background technique [0002] Internet information products refer to various commodities and electronic products listed on Internet sites, including movies, music, books, home appliances, and clothing. With the rapid development of the Internet, users can access a large number of information products through the Internet. For example, there are hundreds of millions of products on Taobao, and millions of movies on Douban. Faced with a large number of produc...

AI Technical Summary

Problems solved by technology

The traditional recommendation method does not take into account the social relationship between users, and cannot provide accurate recommendations for users; at the same time, there is a "cold start" problem, which cannot provide effective recommendations for new users and new products

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