Method for constructing sequence recommendation model and sequence recommendation method

Abstract

The invention discloses a method for constructing a sequence recommendation model and a sequence recommendation method. The method for constructing the sequence recommendation model comprises the steps: constructing a first sequence recommendation model and a second sequence recommendation model, wherein the first sequence recommendation model is provided with a plurality of cavity convolution residual blocks, and the second sequence recommendation model comprises lightweight cavity convolution residual blocks in one-to-one correspondence with the first sequence recommendation model; trainingthe first sequence recommendation model to obtain a pre-trained first sequence recommendation model; performing cooperative training on the second sequence recommendation model and a pre-trained firstsequence recommendation model, and enabling the second sequence recommendation model to learn behaviors of the pre-trained first sequence recommendation model; and constructing a sequence recommendation model for predicting recommendation items based on the second sequence recommendation model subjected to collaborative training. By utilizing the method, the calculation overhead can be reduced, the inference process is accelerated, and rapid and accurate recommendation services are provided for users.

Description

technical field [0001] The present invention relates to the technical field of sequence recommendation, and more specifically, to a method for constructing a sequence recommendation model and a sequence recommendation method. Background technique [0002] The recommendation system is a field that has been researched very hotly and developed very rapidly 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 should be purchased, simulating sales staff to help customers complete the purchase process, and personalized recommendation is to recommend information and products that users are interested in based on the user's interest characteristics and purchase behavior. Sequence recommendation system is an important branch of recommendation system. Its purpose is to make accurate...

AI Technical Summary

Problems solved by technology

[0008] However, when using the existing sequence recommendation model for recommendation service, there are problems such as the amount of model parameters, high computational overhead required by the model, and long inference time.

For example, 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, so It is difficult to deploy the trained model in actual application, the calculation overhead is high, and it takes a long time to infer, which is difficult to meet the actual needs of users

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