Optimized selection method of user closest neighbor set of electronic commerce recommendation system

A nearest neighbor, recommendation system technology, applied in business, data processing applications, instruments, etc., can solve problems such as data sparse

Inactive Publication Date: 2016-10-12
TIANJIN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention aims to overcome the deficiencies in the prior art, and proposes an optimal selection method of the user's nearest neighbor

Method used

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  • Optimized selection method of user closest neighbor set of electronic commerce recommendation system
  • Optimized selection method of user closest neighbor set of electronic commerce recommendation system
  • Optimized selection method of user closest neighbor set of electronic commerce recommendation system

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Embodiment Construction

[0013] Technical scheme of the present invention is:

[0014] (1) Collect the user-item rating data in the historical records, and establish the user-item rating matrix:

[0015]

[0016] (2) Calculate the similarity between the target user and other users. When calculating the similarity between other users and the target user, the common scoring items between the two are considered, and the classic Pearson similarity calculation method is adopted. The specific method is as follows:

[0017] s i m ( a , b ) = Σ i ∈ I a b ( R a i ...

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Abstract

The invention relates to an optimized selection method of a user closest neighbor set of an electronic commerce recommendation system. The optimized selection method comprises the steps that user-project scoring data of a historical record is acquired, and a user-project scoring matrix is established; similarity between a target user and other users is calculated by adopting the Pearson similarity calculating method; for the users in a closest neighbor candidate set, an average error between the score of the user and the scores of the users in the set is calculated according to data of a common scoring item set shared by the user and the users in the above mentioned set, and then confidence level is calculated, when the average error is greater, the confidence level between the target user and the users in the set is smaller; K users having the high confidence levels are used to form the closest neighbor set of the target user, and in addition, 1<K<0. Accuracy of recommendation is improved.

Description

technical field [0001] The invention relates to a method for optimizing and selecting a user's nearest neighbor set in an e-commerce recommendation system. Background technique [0002] With the advent of the "Internet +" era and the rapid development of e-commerce, e-commerce websites use e-commerce recommendation systems to recommend product information to users, provide relevant suggestions, and assist users in making shopping decisions. As one of the traditional recommendation techniques, the collaborative filtering recommendation algorithm is simple and efficient, and has been widely and successfully applied in the recommendation system. [0003] Collaborative filtering recommendation methods are mainly divided into neighbor set-based and model-based recommendation methods. Among them, the recommendation method based on neighbor set is further divided into user-based and item-based methods. The core idea of ​​the user-based recommendation method is to recommend the it...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q30/06
CPCG06Q30/0255G06Q30/0631
Inventor 金志刚张子洋
Owner TIANJIN UNIV
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