Key-element-based matrix decomposition and fine tuning method

Abstract

The invention provides a key-element-based matrix decomposition and fine tuning method. The method comprises: scoring information of a commodity by a user is collected and a sparse scoring matrix R is formed based on the scoring information; according to the sparse scoring matrix R, two low-dimensional matrixes P and Q are initialized, multiplying of the low-dimensional matrixes P and Q is carried out to obtain a predicted matrix R^, and an objective function based on minimization of a difference value between the sparse scoring matrix R and the predicted matrix R^; and the objective function is optimized by an iterative process to obtain the low-dimensional matrixes P and Q and the predicted matrix R^, and fine tuning and updating are carried out on the predicted matrix R^, thereby obtaining a final predicted matrix. According to the invention, in order to overcome defects in the existing matrix decomposition algorithm, a quick matrix decomposition and fine tuning algorithm is put forward based on improvement of the algorithm. With the method, the decomposition efficiency and the decomposition speed of the matrix are increased and the recommendation accuracy is enhanced.

Description

technical field [0001] The invention relates to the technical field of matrix decomposition, in particular to a matrix decomposition and fine-tuning method based on key elements. Background technique [0002] In today's era, the Internet is developing rapidly and has become an important part of people's lives and an important carrier for the spread and development of human civilization. In this era, both information producers and information consumers have encountered great challenges. For information consumers, it is very difficult to find the information they are interested in from a large amount of information; for information producers, it is also difficult to make the information they produce stand out and attract the attention of users. The personalized recommendation system is an important tool to solve this contradiction. The recommendation system provides different services for different users, bringing about a transformation from "people looking for information" t...

AI Technical Summary

Problems solved by technology

[0005] However, in the process of learning and optimizing the low-dimensional latent semantic matrix, the traditional algorithm comprehensively considers the influence of the comprehensive score of the entire matrix on the decomposition result in the calculation process, and does not take into account the key influencing factors of the existing score data on the decomposition effect, because in In the initial stage of matrix decomposition, the available elements are only the initial scoring data, so these data should be considered in the decomposition process

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