Personalized recommendation method based on probability matrix decomposition combined with similarity

Abstract

The invention discloses a personalized recommendation method based on probability matrix decomposition combined with similarity. The method comprises the specific steps that S1, an article information and historical score database is established; S2, a similarity matrix of users and articles is generated; S3, row vectors of the matrix are arranged according to a descending order; S4, an objective function is generated based on a probability matrix decomposition model; S5, a final potential feature matrix of the users and the articles is generated; S6, a prediction score matrix is generated according to the final potential feature matrix of the users and the articles; and S7, personalized recommendation is made to the users. According to the method, prediction scores can be more approximate to true scores of the users according to the condition that potential feature vectors of the users are relevant to user potential feature vectors with high similarity, and therefore the recommendation accuracy of a current recommendation system is improved.

Description

technical field [0001] The invention belongs to the field of data processing systems and methods, in particular to a personalized recommendation method based on probability matrix decomposition combined with similarity. Background technique [0002] With the rapid development of web2.0 technology, the creation and sharing of information has become easier and easier, so that all kinds of information have exploded, and the scale of the Internet has also continued to expand, resulting in the so-called "information overload" problem. However, it is very difficult for users to find the information they are interested in in the massive amount of information. How to help users accurately obtain valuable information for themselves in the shortest time and improve the utilization rate of information is a challenge faced by Internet technicians. big challenge. [0003] As we all know, among traditional Internet products, portals and search engines are representative means to solve th...

AI Technical Summary

Problems solved by technology

The traditional collaborative filtering algorithm can effectively solve the above problems and improve the utilization rate of information. However, with the large-scale growth of users and items and their access to the Internet, while users rarely rate items, it leads to cold start and sparse matrix problems. , resulting in very low recommendation efficiency of the collaborative filtering algorithm; while the probability matrix factorization algorithm effectively solves the cold start and sparse matrix problems, but its recommendation accuracy is not high

Images