Article recommendation method and device

A recommendation method and item technology, applied in the computer field, can solve the problems of cold start of new items, poor recommendation effect, and low recommendation accuracy, and achieve the effect of improving the accuracy.

Active Publication Date: 2017-10-17
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 an item recommendation method and device, which aims to solve the problem of cold start of the existing recommendation system for new items, and the recommendation accuracy is not high, resulting in poor recommendation effect

Method used

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  • Article recommendation method and device
  • Article recommendation method and device
  • Article recommendation method and device

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Embodiment 1

[0020] figure 1 The implementation process of the item 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:

[0021] In step S101 , according to the similarity between the rated item and the item to be rated in the historical scoring data, the influence factor of the predicted rating of the item to be rated in the historical scoring data is calculated.

[0022] In the embodiment of the present invention, according to the pre-obtained similarity between the user's rated items and the items to be rated, the influence factor of the predicted rating of the rated items in the historical rating data to the items to be rated is calculated.

[0023] Preferably, before calculating the impact factor of the predicted rating of the rated item in the historical rating data, the item to be rated and the text content of ...

Embodiment 2

[0037] figure 2 The structure of the item recommendation device provided by the second embodiment 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:

[0038] The factor calculation unit 21 is used to calculate the influence factor of the predicted score of the item to be rated based on the historical rating data according to the similarity between the rated item and the item to be rated in the historical rating data.

[0039] In the embodiment of the present invention, the factor calculation unit calculates the influence factor of the predicted rating of the rated item in the historical scoring data according to the similarity between the user's rated item and the item to be rated in advance.

[0040] Preferably, before calculating the impact factor of the predicted rating of the rated item in the historical rating data, the item to be rated and the text content of the ...

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Abstract

The invention is applicable to the technical field of computers, and provides an article recommendation method and a device. The method comprises the following steps of according to the similarity between a scored article and a to-be-scored article in historical scoring data, calculating the influence factor of the historical scoring data on the predicted score of the to-be-scored article; inputting the calculated influence factor into a pre-established restricted boltzmann machine model; calculating the predicted score of a user on each to-be-recommended article through the restricted boltzmann machine model; generating a recommendation list; and outputting recommended articles to the user according to the generated recommendation list. In this way, the cold start problem during the new article recommending process can be solved. The recommendation accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to an item recommendation method and device. 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 most popular technology in the recommendation system. The commonly used collaborative filtering technologies are based on the nearest neighbor method and the model-based method. 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 Applications(China)
IPC IPC(8): G06Q30/02G06Q30/06G06F17/30
CPCG06F16/9535G06Q30/0255G06Q30/0631
Inventor 傅向华余冲李坚强
Owner SHENZHEN UNIV
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