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Recommended method, apparatus and device, and storage medium

A technology for recommending lists and items, applied in the computer field, can solve problems such as low recommendation accuracy and poor recommendation effect, and achieve the effect of improving recommendation efficiency and accuracy

Active Publication Date: 2017-10-20
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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  • Recommended method, apparatus and device, and storage medium
  • Recommended method, apparatus and device, and storage medium
  • Recommended method, apparatus and device, and storage medium

Examples

Experimental program
Comparison scheme
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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 technical field of computers, and provides a recommended method, apparatus and device, and a storage medium. The method comprises: obtaining history score data of a user, a to-be-scored item, and text content of the to-be-scored item; according to the history score data of the user, the to-be-scored item, and the text content of the to-be-scored item, training the preset stack noise reduction self-encoder and the preset probability matrix decomposition model to obtain the user characteristic matrix, the item characteristic matrix, the corresponding item latent characteristics and user latent characteristics; according to the user characteristic matrix, the item characteristic matrix, the corresponding item latent characteristics and the user latent characteristics, calculating the predicted score of the to-be-scored item; and according to the predicted score, generating a recommendation list, and outputting the recommendation list to the user, so that when recommending the item to the user, the item characteristics are combined with the user characteristics, the recommended accuracy is effectively improved, and the recommended efficiency of the item is further improved.

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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IPC IPC(8): G06F17/30G06Q30/02G06Q30/06
CPCG06Q30/0256G06Q30/0631G06F16/951
Inventor 傅向华余冲李坚强
Owner SHENZHEN UNIV
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