Recommendation model training method and device

Abstract

The invention provides a recommendation model training method, and the method comprises the steps: carrying out the training of a training sample set through employing a single-class domain perceptionlow-rank model, so as to obtain a model parameter matrix, and enabling the model parameter matrix to be used for generating a recommendation model. The scheme of the invention can be applied to the field of artificial intelligence recommendation. Various domain characteristics and positive and negative samples can be introduced into the single-class domain perception low-rank model, so that morefactors related to user selection and negative example information related to the user can be considered in the training process, a recommendation model with more excellent performance is generated, and the recommendation result better conforms to user requirements. In addition, the specific training mode of model training can greatly simplify the complexity of model training, so that the model training efficiency is improved, and introduction of more information becomes possible.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a recommendation model training method and a related device. Background technique [0002] Artificial Intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is the branch of computer science that attempts to understand the nature of intelligence and produce a new class of intelligent machines that respond in ways similar to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making [0003] Among them, how to make recommendations to use...

AI Technical Summary

Problems solved by technology

[0006] However, the single-class matrix factorization model, because it only considers the user ID and product ID in the training process, does not consider the characteristics of the user and the product itself, so it will cause less information to be considered in the model training process, and thus the recommended model obtained Insufficient performance

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