Adaptive output sequence recommendation method and system

Abstract

The invention discloses a self-adaptive output sequence recommendation method and a self-adaptive output sequence recommendation system. The method comprises the steps that a sequence recommendation model is constructed, the sequence recommendation model comprises N hole convolution residual error blocks and a teacher classifier connected with the Nth hole convolution residual error block to serveas a backbone network, the first N-1 hole convolution residual error blocks are independently connected with student classifiers to serve as branch networks, and N is an integer larger than or equalto 2; taking a set loss function as a target, and training the sequence recommendation model by utilizing the sample set; and inputting the historical browsing sequence of the to-be-recommended user into the trained sequence recommendation model, and judging and selecting the output of the teacher classifier or the student classifier according to the uncertainty of the student classifier to serveas a prediction result of the user recommendation item at the subsequent moment. By utilizing the method and the system, the inference process can be remarkably accelerated, quick and accurate recommendation services are provided for users, and the method and the system have very important practical significance and wide application prospects.

Description

technical field [0001] The present invention relates to the technical field of sequence recommendation, and more particularly, to a sequence recommendation method and system for adaptive output. Background technique [0002] The recommendation system is a field that has developed very prosperously in recent years. It has attracted much attention because of its broad application scenarios and huge commercial value. It is defined as using e-commerce websites to provide customers with product information and suggestions to help users decide what products to buy. The simulated salesperson helps customers complete the purchase process, while personalized recommendation is to recommend information and products of interest to users based on their interest characteristics and purchase behavior. Sequence recommendation system is an important branch of recommendation system. Its purpose is to make accurate recommendations to users by analyzing the user's historical browsing sequence. ...

AI Technical Summary

Problems solved by technology

[0008] The shortcomings of the existing technology are mainly that when performing recommendation services, the number of model parameters is large, and the inference time is long, which makes it difficult to meet the needs in the real world

NextItNet needs to stack a large number of empty convolution residual blocks to achieve better results, resulting in a huge amount of model parameters, and for each input user history browsing sequence, a complete model is required to complete the output prediction, which will train well. It is difficult to deploy the model in actual application, the calculation cost is large, and it takes a long time to infer, so it is difficult to meet the actual needs of users

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