A user feature extraction method and extraction device based on non-negative alternating direction transformation

Abstract

The invention proposes a user feature extraction method and extraction device based on non-negative alternating direction transformation. The extracting device includes a data receiving module, a data storage module and an execution module, wherein the data receiving module is connected to the data storage module, and the data receiving module is used to receive user behavior statistics data collected by the server, and collect the collected data from the server. The statistical data of user behavior is transmitted to the data storage module for storage, and the data storage module is connected with the execution module, and the execution module executes the user feature extraction instruction sent by the server, and stores the extracted user feature data into the data storage module. The present invention directly acts on the known data set in the user behavior statistical matrix, can process the extremely sparse user behavior statistical matrix with a large number of missing values, has fast convergence speed, high data restoration accuracy, and can solve problems in the big data processing environment User feature extraction problem.

Description

technical field [0001] The invention relates to the technical field of computer big data processing, in particular to a method and device for extracting user features based on non-negative alternating direction transformation in an e-commerce system. Background technique [0002] Modern large-scale e-commerce systems have huge numbers of users and information. In this type of system, various objective behaviors of users, such as clicking, browsing, commenting, searching, etc., accumulate with the operating time of the system and aggregate into a huge user historical behavior data set. The data volume is at least in the TB level, which is a typical big data environment. [0003] In a large-scale e-commerce system, a typical data description structure is a user behavior statistics matrix, in which each row corresponds to a user, and each column corresponds to an item; an item refers to any objective object in the system that may be operated by a user, such as news , pictures...

AI Technical Summary

Problems solved by technology

This is because the graphics and images transformed by the non-negative object feature extraction in computer vision are full-rank matrices and do not have missing values. The non-negative matrix factorization of such matrices can be solved by conventional matrix iterative operations. However, for the non-negative user behavior extraction problem in e-commerce systems, the user behavior statistical matrix processed is usually extremely sparse, with a large number of missing values, which cannot be processed by traditional matrix factorization, and need to be processed with non-negative latent feature analysis that works on sparse matrices

However, the existing non-negative matrix latent feature analysis method has the disadvantages of slow convergence speed and low accuracy of data restoration

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