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A recommended method, device, equipment and storage medium

An item and user technology, applied in the computer field, can solve the problems of poor recommendation effect and low recommendation accuracy, and achieve the effect of improving recommendation efficiency and accuracy.

Active Publication Date: 2021-02-23
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a recommendation method and device, which aims to solve the problem of low recommendation accuracy in the prior art, resulting in poor recommendation effect

Method used

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  • A recommended method, device, equipment and storage medium
  • A recommended method, device, equipment and storage medium
  • A recommended method, device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0026] figure 1 The implementation flow of the recommendation method provided by Embodiment 1 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

[0027] In step S101, the historical rating data of the user, the items to be rated and the text content of the items to be rated are obtained.

[0028] In the embodiment of the present invention, the historical scoring data constitutes a scoring matrix, and the historical scoring data includes the text content of the scored items. The obtained text content of the scored items and the items to be rated is used as the input of the preset model, specifically, such as figure 2 As shown, I in the figure means that there are I users in total, J means that there are J items in total, and X c ∈R J×S A vector denoting a collection of J items, X c It is the original input data of the SDAE part, where the vector...

Embodiment 2

[0039] image 3 The structure of the recommendation device provided by Embodiment 2 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, including:

[0040] The data acquisition unit 31 is configured to acquire the user's historical rating data, items to be rated and text content of the item to be rated, wherein the historical rating data includes the text content of items that have been rated.

[0041] The model training unit 32 is used to train the preset two-way constrained deep collaborative model by using the preset deep stack noise reduction automatic coding learning technology and probability matrix decomposition technology according to the acquired data, and obtain the user feature matrix and the item feature matrix And the corresponding item hidden features and user hidden features.

[0042] In the embodiment of the present invention, the impact of item features and user fe...

Embodiment 3

[0059] Figure 5 The structure of the recommended device provided by the third embodiment of the present invention is shown, and for the convenience of description, only the parts related to the embodiment of the present invention are shown.

[0060] The recommendation device 5 of the embodiment of the present invention includes a processor 50 , a memory 51 and a computer program 52 stored in the memory 51 and operable on the processor 50 . When the processor 50 executes the computer program 52, it realizes the steps in the above-mentioned recommended method embodiment, for example figure 1 Steps S101 to S104 are shown. Alternatively, when the processor 50 executes the computer program 52, the functions of the units in the above-mentioned device embodiments are realized, for example image 3 Function of units 31 to 34 shown.

[0061] In the embodiment of the present invention, when the processor 50 executes the computer program 52 to implement the steps in the above embodim...

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Abstract

The present invention is applicable to the field of computer technology, and provides a recommendation method, device, equipment, and storage medium. The method includes: acquiring the user's historical rating data, items to be rated, and the text content of the item to be rated, and according to the user's historical rating data, The items to be rated and the text content of the items to be rated are trained on the preset stacked noise reduction autoencoder and the preset probability matrix decomposition model to obtain the user feature matrix, item feature matrix, and corresponding item hidden features and user hidden features. Features, according to the user feature matrix, item feature matrix and the corresponding item hidden features and user hidden features, calculate the user's predicted score for the item to be rated, generate a recommendation list according to the predicted score, and output the recommendation list to the user, so as to recommend items to the user Combining item features and user features at the same time can effectively improve the accuracy of recommendation, and then improve the efficiency of item recommendation.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a recommendation method, device, equipment and storage medium. Background technique [0002] With the rapid development of Internet technology, the lifestyle of users has undergone major changes. In the Internet age with a wide variety of information and competitive incentives, how to help users quickly and accurately select the items they are interested in is very important for an Internet company. Based on the above problems, recommender system technology came into being. Collaborative filtering technology is the most widely used and popular technology in recommendation system. Commonly used collaborative filtering techniques are based on nearest neighbor methods and model-based methods. Model-based methods are subdivided into clustering models, Bayesian classification models, latent factor models, and graphical models, among which the research effect on latent...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F16/958G06Q30/02G06Q30/06
CPCG06Q30/0256G06Q30/0631G06F16/951
Inventor 傅向华余冲李坚强
Owner SHENZHEN UNIV
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