Training method and system of commodity personalized ranking model

A sorting model and training method technology, applied in the field of data processing, can solve the problem that the model cannot take into account real-time performance and accuracy at the same time

Active Publication Date: 2016-01-20
唯品会(广州)软件有限公司
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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 t

Method used

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  • Training method and system of commodity personalized ranking model
  • Training method and system of commodity personalized ranking model
  • Training method and system of commodity personalized ranking model

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Experimental program
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Embodiment Construction

[0023] see figure 1 , providing an embodiment of a method for training a product personalized ranking model, including the following steps:

[0024] S100: Obtain historical commodity data within a preset time period.

[0025] When the user clicks, purchases or collects the product on the page, product data will be generated, and these product data will be stored to form historical product data. By obtaining the historical product data within a preset time, it will be used for subsequent personalized sorting of products The model is trained offline to provide training samples. For example, the historical commodity data of the previous 2 months is acquired every other day, that is, the historical commodity data of the previous 2 months is acquired every morning to obtain training samples, and then the personalized sorting model of commodities is offline based on the training samples train.

[0026] S200: According to the long-term interest characteristics in the historical pr...

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q30/02
CPCG06F16/9535G06Q30/0202
Inventor 王晓丹
Owner 唯品会(广州)软件有限公司
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