Sequence recommendation method and system based on adaptive network depth

Abstract

The invention discloses a sequence recommendation method and system based on adaptive network depth. The method comprises the steps that a sequence recommendation model is constructed, and the sequence recommendation model is provided with a plurality of cavity convolution residual blocks as a main body network and is provided with a strategy network used for managing the depth of the main body network; taking a set loss function as a target, training the sequence recommendation model by using a sample set to obtain a trained main body network, and for each of the plurality of hole convolutionresidual blocks, outputting a decision indication used for representing reservation or skipping of the hole convolution residual block by the strategy network; and inputting the historical browsing sequence of the to-be-recommended user into the trained sequence recommendation model, and determining a hole convolution residual block needing to be skipped according to the decision indication of the strategy network so as to output a prediction result of the user recommendation item at the subsequent moment. According to the invention, the depth of the main network can be adaptively adjusted byusing the strategy network, and quick and accurate recommendation services can be provided for users.

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 based on adaptive network depth. 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 recommendations to...

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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