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Collaborative filtering method and system based on similarity propagation

A similarity transfer and collaborative filtering technology, applied in the field of collaborative filtering based on similarity transfer, can solve the problems of low similarity accuracy and recognition, and unsatisfactory recommendation success rate, so as to improve the recognition and recommendation coverage. High, avoids the effect of similarity measures

Active Publication Date: 2013-09-18
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a collaborative filtering method based on similarity transfer, which is used to solve the problems of low accuracy and recognition of traditional similarity and unsatisfactory recommendation success rate

Method used

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  • Collaborative filtering method and system based on similarity propagation
  • Collaborative filtering method and system based on similarity propagation
  • Collaborative filtering method and system based on similarity propagation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] In an example of the present invention, "MovieLens100K" is used as the data set, and when the number of recommended items N is 10, such as image 3 As shown, the comparison with the traditional collaborative filtering method in the recommendation accuracy, recommendation recall rate and recommended item coverage index. In the selected data set, randomly select 80% as the training set and the remaining 20% ​​as the test set. The data set already contains the ratings of the data. Select the training set and test set with a score greater than "2" to indicate liking, and replace it with "1", otherwise use "0" instead. The method proposed in the present invention and the recommendation accuracy rate of the traditional collaborative filtering method, the recommendation recall rate and the comparison result of the coverage index of the recommended item are adopted respectively, wherein the k other users are taken as 942, that is, all other users in the system are selected, and...

Embodiment 2

[0048] In an example of the present invention, "MovieLens100K" is used as a data set, and when the number of recommended items N is 20, such as Figure 4 As shown, the comparison with the traditional collaborative filtering method in the recommendation accuracy, recommendation recall rate and recommended item coverage index. In the selected data set, randomly select 80% as the training set and the remaining 20% ​​as the test set. The data set already contains the ratings of the data. Select the training set and test set with a score greater than "2" to indicate liking, and replace it with "1", otherwise use "0" instead. The method proposed in the present invention and the recommendation accuracy rate of the traditional collaborative filtering method, the recommendation recall rate and the comparison result of the coverage index of the recommended item are adopted respectively, wherein the k other users are taken as 942, that is, all other users in the system are selected, and ...

Embodiment 3

[0050] In an example of the present invention, "MovieLens100K" is used as the data set, and when the number of recommended items is from 10 to 100, such as Figure 5 As shown, the comparison with traditional methods in recommending low popularity items to users. In actual recommendation, it is difficult to recommend items with low popularity to customers, and recommending items with low popularity is the direction of development. In the selected data set, 80% are randomly selected as the training set, and the remaining 20% ​​are used as the test set. The scores in the training set and the test set are greater than "2" to indicate liking, replaced by "1", otherwise replaced by "0". The comparison results of the item popularity index changing with the number of the N recommended items using the method proposed in the present invention and the traditional collaborative filtering method respectively, wherein the k other users take 942, that is, select all other users in the system...

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Abstract

The invention relates to the technical field of recommendation, in particular to a collaborative filtering method and system based on similarity propagation. The method comprises the steps as follows: traversing historical behavior data of all users to obtain a relationship vector describing the preferences of all the users on articles; setting a threshold value by the relationship vector and calculating the similarities among the users; calculating the similarity between a target user and the other user, with a similarity value of zero in the matrix, by a similarity propagation calculation principle; obtaining an estimation value of the preference degree of a current user on an unselected article according to the preference degree of the other user most similar to the target user on an article unselected by the target user; and screening the prediction results of all the users to generate recommended articles for all the users. The system comprises a data relationship vector module, a threshold value judgment module, a similarity propagation calculation module, a preference degree estimation module and a prediction screening module.

Description

technical field [0001] The invention relates to the technical field of network recommendation, in particular to a collaborative filtering method based on similarity transfer. Background technique [0002] The development of information technology and the Internet has brought people from the era of information scarcity to the era of information overload. In this era, it is difficult for information consumers to find what they are really interested in from a large amount of information, and it is also difficult for information producers to make the information they produce stand out and match suitable users. As one of the very potential information filtering technologies in the 21st century, the recommendation system can better solve this contradiction. It analyzes the user's historical behavior, establishes the user's interest model, and finally connects the user with the information. On the one hand, it helps the user to discover the information that is valuable to him, and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 谢峰陈震许宏峰曹军威
Owner TSINGHUA UNIV
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