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Method and device for performing personalized recommendation on users

A technology of users and computing methods, applied in the direction of special data processing applications, instruments, electrical digital data processing, etc.

Active Publication Date: 2013-08-14
UNIV OF SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the prior art, there is no comprehensive use of user-product rating data and user label data to make personalized recommendations for users

Method used

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  • Method and device for performing personalized recommendation on users
  • Method and device for performing personalized recommendation on users
  • Method and device for performing personalized recommendation on users

Examples

Experimental program
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Embodiment 1

[0021] In the collaborative filtering algorithm, there are two most basic algorithms: the nearest neighbor algorithm and the matrix decomposition algorithm. The nearest neighbor algorithm includes the user-based nearest neighbor algorithm and the product-based nearest neighbor algorithm. The common characteristics of the two algorithms are Find the K nearest neighbors of each user or each product through the scoring matrix, and predict the user's preference for the product through the scores of the K nearest neighbors. The user-based nearest neighbor algorithm assumes that if two users have similar ratings for the same product, then they are likely to have similar ratings for other products. The product-based nearest neighbor algorithm assumes that if many users have similar ratings for two different products, then other users should also have similar ratings for these two products.

[0022] Matrix factorization algorithms, such as singular value decomposition, represent users...

Embodiment 2

[0093] This embodiment provides a device for making personalized recommendations to users, and its specific structure is as follows: image 3 As shown, the following modules are included:

[0094] Neighbor set acquisition module 31 is used to calculate the similarity between users by using the label data of users, calculate the similarity between products by using the label data of products, and obtain the neighbor set of each user and each product according to the similarity information;

[0095] The eigenvector acquisition module 32 is used to calculate the eigenvectors of the user and the product by adopting the nearest neighbor matrix decomposition algorithm based on the neighbor set information of the user and the product;

[0096] The personalized recommendation processing module 33 is used to predict the user's rating data for unrated products according to the feature vectors of the user and the product, and recommend personalized products to the user according to the r...

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Abstract

The invention relates to a method and a device for performing personalized recommendation on users. The method mainly comprises: calculating the similarity of users through label data of the users, calculating the similarity of products through label data of the products, and obtaining a neighboring set of each user and each product according to the similarity information; calculating characteristic vectors of the users and the products through a nearest neighbor matrix decomposition algorithm on the basis of the neighbor set information of the users and the products; and forecasting grading data of products not judged by the users according to the characteristic vectors of the users and the products, and performing personalized product recommendation according to the grading data. Labeling information and the neighbor set information can be guided into a matrix decomposing model by means of an embodiment of the method and the device, so that similar users / products can have the similar characteristic vectors, and the device for performing personalized recommendation on users can have the advantages of the matrix decomposing model and can capture the strong ties among neighbors in an overall situation.

Description

technical field [0001] The invention relates to the field of computer applications, in particular to a method and device for personalized recommendation to users. Background technique [0002] With the vigorous development of information technology and the Internet, the resources on the network are growing explosively. For example: there are tens of thousands of movies on Netflix, millions of books on Amazon, and hundreds of millions of products on Taobao. Faced with so much information, if you use traditional information search engines to search for information, you will get the same sorting results of information, and it is impossible to provide corresponding services according to the preferences of different users. Therefore, while the massive information space brings users diversified choices, it makes users get lost in the ocean of information, and users have to spend a lot of time and cost to find the information they need, which is the so-called "information overload...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 陈恩红吴乐宝腾飞向彪徐林莉
Owner UNIV OF SCI & TECH OF CHINA
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