Commodity scoring model construction and recommendation method and system based on time perception

A construction method and product technology, which is applied in business, equipment, sales/lease transactions, etc., can solve the problems that affect the accuracy of recommendation methods, the accuracy of product scoring model is not high, and the inability to fully understand users and projects, etc., to achieve It has the effect of explainability, performance improvement and accuracy improvement

Inactive Publication Date: 2019-11-22
NORTHWEST UNIV(CN)
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method and system for constructing and recommending product rating models based on time perception, so as to solve the problem that the product rating model construction method in the prior art cannot fully understand users and items, so that the ratings of the product rating models are accurate The rate is not high, which affects the accuracy of the recommended method

Method used

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  • Commodity scoring model construction and recommendation method and system based on time perception
  • Commodity scoring model construction and recommendation method and system based on time perception
  • Commodity scoring model construction and recommendation method and system based on time perception

Examples

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Effect test

Embodiment 1

[0061] A method for constructing a time-aware commodity rating model provided by the present invention, such as figure 1 As shown, in the first stage of the model, the super-strong feature mining capability of the convolutional neural network is used to learn the high-dimensional feature vectors of users, products, and rating information. The present invention also introduces the key rating time feature for product rating prediction at this stage; In the second stage of the model, the random forest regressor is trained through the high-dimensional feature vector learned by the convolutional neural network to predict the user's rating of the product, so as to make recommendations based on the predicted rating.

[0062] Follow the steps below:

[0063] Step 1. Obtain an evaluation information set, the evaluation information set includes a plurality of evaluation information, and the input information includes user information, product information, user text, product text, and us...

Embodiment 2

[0113] A product recommendation method based on time perception, which is used to obtain a product recommendation sequence, is implemented according to the following method:

[0114] Step A, obtaining user information and user text of the user;

[0115] Obtain product information and product text for each product;

[0116] Obtain the user's evaluation time for each product;

[0117] Step B. Collect the product information, product text, user's evaluation time for each product, user information and user text of each product to obtain the evaluation information of each product;

[0118] Step C, input the evaluation information of each commodity into the commodity scoring model obtained by the time-aware-based commodity scoring model construction method in Embodiment 1, and obtain the scoring value of each commodity;

[0119] Step D. Arrange the score values ​​of each product from large to small to obtain a product recommendation sequence.

[0120] In this example, the user sc...

Embodiment 3

[0122] A system for constructing a commodity scoring model based on time perception, including a data obtaining device and a model constructing device;

[0123] The data obtaining device is used to obtain an evaluation information set, the evaluation information set includes a plurality of evaluation information, and the input information includes user information, product information, user text, product text, and user evaluation time for the product;

[0124] Obtain the user's rating of the product, obtain the rating value, and obtain the label set;

[0125] The model building device is used to use the evaluation information set as input and the label set as output to train the network;

[0126] The network includes an input layer, a feature extraction layer, a feature fusion layer, a prediction result layer and a result output layer sequentially connected in series;

[0127] The input layer includes 5 parallel input modules, which are respectively used to input user informa...

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Abstract

The invention discloses a commodity scoring model construction and recommendation method and system based on time perception. The method comprises the following steps of in the first stage of a model,using super-strong feature mining capability of a convolutional neural network to mine high-dimensional feature vectors for capacity learning users, commodities and rating information, introducing rating time features critical to project rating prediction; in the second stage of the model, training a random forest regression model through the high-dimensional feature vector learned by the convolutional neural network to predict the user rating on the project , so that recommendation is performed according to the predicted rating. Data of various forms can be utilized and recommended at the same time, the scoring accuracy of the scoring model is improved, and therefore accuracy of the recommendation method is improved.

Description

technical field [0001] The present invention relates to a commodity recommendation method, in particular to a time-aware-based commodity scoring model construction, recommendation method and system. Background technique [0002] In recent years, with the development of science and technology, many businesses have developed from offline to online, and the data generated online has grown explosively. Facing the huge amount of data on the Internet, how users can quickly select items that meet their interests is the main problem faced by users, and it is also the main challenge of recommendation methods. [0003] With the successful application of deep learning technology in the fields of natural language processing and image / video processing, product rating model construction methods and recommendation methods based on deep learning technology have gradually become a trend. Compared with the traditional product rating model construction method, the product rating model constru...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06Q30/02
CPCG06Q30/0282G06Q30/0631
Inventor 宋小磊陈春芳贺小伟王宾郝军张翔
Owner NORTHWEST UNIV(CN)
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