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Method of improving accuracy of recommendation system

A recommendation system and accuracy technology, applied in the field of Internet services, to achieve the effects of resisting scoring noise, improving recommendation accuracy, and good robustness

Inactive Publication Date: 2015-12-30
GUANGDONG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to overcome the defects and technical deficiencies in the Internet recommendation system in the prior art, and provide a method for improving the accuracy of the recommendation system, through which the scoring noise can be effectively resisted, and the recommendation accuracy can be improved

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  • Method of improving accuracy of recommendation system
  • Method of improving accuracy of recommendation system

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Embodiment 1

[0038] Such as figure 1 , figure 2 As shown, the present invention provides a method for improving the accuracy of the recommendation system, comprising the following steps:

[0039] S1. Build data subsets

[0040] S11. Input a scored sample training set L and an ungraded sample set Un;

[0041] S12. Initialize the three data subsets as an empty set;

[0042] S13. According to the confidence, use the roulette algorithm to select labeled data into the data subset;

[0043] S14. Use the Bagging method to randomly select samples from the unlabeled data, and complete the grouping of the three data subsets. Such as figure 1 As shown, after the data set is divided into three data subsets, different subsets contain differentiated data samples. The differentiated subset includes scored (labeled) and unscored (unlabeled) data.

[0044] The construction of different data subsets is very important, and the differences between data subsets determine the effectiveness of semi-super...

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Abstract

The invention discloses a method of improving accuracy of a recommendation system. The method of improving the accuracy of the recommendation system comprises the steps of firstly establishing three data subsets, then marking label-free data in each data subset by respectively applying a scoring model based on Gaussian mixture distribution established by the invention onto the established data subsets to obtain each training set, adding the original label-free data marked in the obtained training sets into labeled data in other subsets, iteratively updating labels of the original label-free data in the other subsets, and finally outputting a final recommendation result. The method enables the recommendation system to be capable of effectively resisting scoring noises, and good robustness is obtained and the recommendation accuracy is improved. Besides, the problem of cold start can be effectively relieved.

Description

technical field [0001] The invention relates to the field of Internet services, and more specifically, to a method for improving the accuracy of a recommendation system. Background technique [0002] The Internet has been closely integrated with people's daily life. In recent years, recommender systems have been widely used in various Internet applications represented by e-commerce to solve the problem of information overload. Most recommendation systems use collaborative filtering technology, which learns the user's online behavior patterns and builds a model by analyzing a large number of users' past online history records to help users filter the information they need from massive information and recommend it to users. Most of the recommendation services of domestic and foreign well-known websites such as Amazon and Taobao are based on collaborative filtering. [0003] In a recommender system, the user’s historical behavior data is organized into a scoring matrix R U×I...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q30/02
CPCG06F16/9535G06Q30/0251G06Q30/0277
Inventor 郝志峰成英超蔡瑞初温雯
Owner GUANGDONG UNIV OF TECH
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