Training method and system of commodity personalized ranking model

Abstract

The invention relates to a training method and system of a commodity personalized ranking model. The training method comprises the following steps: according to long-term interest characteristics in historical commodity data, carrying out off-line training on the commodity personalized ranking model, obtaining a parameter corresponding to each long-term interest characteristic, i.e. obtaining the commodity personalized ranking model with high precision, and eliminating short-term characteristics in the historical commodity data to reduce time consumption; and at a unit time interval, obtaining real-time commodity data, expanding the commodity personalized ranking model subjected to the off-line training, and carrying out on-line training on the expanded commodity personalized ranking model according to the long-term interest characteristics and the short-term characteristics in the real-time commodity data to obtain an updated parameter corresponding to each long-term interest characteristic and an updated parameter corresponding to each short-term interest characteristic. Therefore the expanded commodity personalized ranking model is updated for one time at the unit time interval to obtain the model with higher timeliness and realize the balance of the precision and the timeliness of the model so as to obtain a better prediction result.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method and system for training a personalized sorting model of commodities. Background technique [0002] At present, recommending products online to users is a common method to increase product sales. It mainly trains the product personalized ranking model, and then uses the trained product personalized ranking model to predict the output, and recommends products based on the prediction results. Among them, the product personalized ranking model is the product personalized recommendation model. The product personalized recommendation model includes model input, model parameters, and prediction output. The prediction output can be obtained by simulating according to the model input and model parameters. The model training process is based on the prediction The error between the output and the actual output is the process of continuously adjusting the model parameters to...

AI Technical Summary

Problems solved by technology

[0004] Based on this, it is necessary to address the problem that the existing models cannot take both real-time and precision into account at the same time, and provide a method and system for training a personalized sorting model for goods that can improve both timeliness and precision.

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