Comprehensive similarity migration-based collaborative filtering algorithm

A collaborative filtering algorithm and a technology that integrates similarity. It is applied in computing, computing models, instruments, etc. It can solve the problems of ignoring differences in user scoring standards, less model applicable scenarios, and more model training parameters, etc., to alleviate the problem of data sparsity, Improve recommendation accuracy and improve the effect of accuracy

Active Publication Date: 2018-07-10
SICHUAN UNIV
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Problems solved by technology

[0007] Although the above algorithms all use auxiliary domain knowledge to improve the recommendation accuracy, there are still some shortcomings: first, the model based on matrix transformation has more model training parameters; The third is that when calculating the simi

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  • Comprehensive similarity migration-based collaborative filtering algorithm

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Embodiment Construction

[0065] The present invention will be further described below in conjunction with accompanying drawing:

[0066] Recommendation algorithm based on comprehensive similarity migration:

[0067] The present invention proposes a recommendation algorithm based on comprehensive similarity migration, and uses auxiliary domain information to alleviate the data sparsity problem in the target domain.

[0068] The algorithm of the present invention will be described below by taking two movie platforms as examples. Suppose there are two platforms e 1 and e 2 , U 1 Indicates only on platform e 1 For users with historical behavior information in U, U 2 Indicates only on platform e 2 For users with historical behavior information in U, U c Indicated on platform e 1 and e 2 Users who have historical behavior information in both are defined as cross-users. User behavior matrix such as figure 1 shown.

[0069] In practical situations, the number of intersecting users is much smaller ...

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Abstract

The invention discloses a comprehensive similarity migration-based collaborative filtering algorithm. According to the algorithm of the invention, in the aspect of similarity calculation, user ratinginformation and user attribute information are both utilized, satisfaction degree scoring criterion differences between users are considered, a method which adopts the distribution consistency of userratings to measure the similarity of the user ratings is adopted, and therefore the comprehensive similarity migration-based collaborative filtering algorithm can improve the accuracy of similarity calculation and improve the quality of data migration compared with the prior art. The experimental results show that the model can effectively alleviate a data sparsity problem compared with other algorithms. In the future, the data of auxiliary fields can be migrated through combining the similarity of projects or other knowledge such as text information, and therefore, recommendation accuracy can be improved.

Description

technical field [0001] The invention relates to the field of network information calculation, in particular to a collaborative filtering algorithm based on comprehensive similarity migration. Background technique [0002] At present, the amount of network information is increasing exponentially. On the one hand, network users can obtain a wealth of information, but on the other hand, they are faced with the problem of information overload, and it is difficult to mine useful information from the massive information. The recommendation system can filter out the parts that the user is interested in from massive data according to the user's interests. At present, recommender systems have been widely used in e-commerce platforms such as Amazon, eBay, MovieLens, and GroupLens. [0003] Collaborative filtering technology is one of the most widely used technologies in the recommendation system. Its basic idea is to use the user's historical rating data to predict the user's interes...

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Application Information

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IPC IPC(8): G06Q30/06G06N99/00
CPCG06N20/00G06Q30/0631
Inventor 琚生根孙界平陈黎夏欣金玉王婧研
Owner SICHUAN UNIV
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