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Recommendation method and system based on multi-category joint soft clustering

A recommendation method and recommendation system technology, applied in the field of data analysis, can solve problems such as difficulty in recommendation, lack of historical behavior data for new users, and inability to know user preferences, etc., to improve prediction performance, reduce sparsity, and high prediction accuracy Effect

Inactive Publication Date: 2018-12-11
GUANGZHOU INTELLIGENT CITY DEV INST +2
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AI Technical Summary

Problems solved by technology

New users do not have historical behavior data, so it is impossible to know the user's preferences and make personalized recommendations
Since there is no relevant user rating data for new products, it is also difficult to recommend them through collaborative filtering

Method used

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  • Recommendation method and system based on multi-category joint soft clustering
  • Recommendation method and system based on multi-category joint soft clustering
  • Recommendation method and system based on multi-category joint soft clustering

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Embodiment Construction

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0063] figure 1 is a schematic diagram of the method flow chart of the recommendation method based on multi-category joint soft clustering in the embodiment of the present invention, as shown in figure 1 As shown, the recommended methods include:

[0064] S11: Obtain user-item interaction information, and construct a scoring matrix and a classification matrix according to the user-item interaction information;

[0065] S12: Perform multi-category soft clustering ...

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Abstract

The invention discloses a recommendation method and system based on multi-category joint soft clustering. The recommendation method comprises a step of acquiring user-item item interaction informationand constructing a scoring matrix and a classification matrix according to the user-item interaction information, a step of carrying out multi-category soft clustering processing on the scoring matrix and the classification matrix to obtain a multi-category soft clustering result, a step of carrying out user preference prediction on the multi-category soft clustering result by using weighted non-negative matrix decomposition to obtain a prediction result, and a step of recommending an item with a highest prediction score is to a user based on the prediction result. In the embodiment of the present invention, the score prediction can be performed according to the degree of preference of the user to the item, and the item is recommended to the user according to the score prediction, and theprediction accuracy is high.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a recommendation method and system based on multi-category joint soft clustering. Background technique [0002] Personalized recommendation is currently used in all aspects of our lives. It can filter out the parts that users are interested in from a large number of articles, articles, movies, music, the Internet, and so on. Currently popular recommendation systems include e-commerce platforms such as Amazon, music recommendation systems, and movie systems. A good recommender system can benefit both recommender system owners and users. [0003] According to different recommendation methods, recommendation systems can be roughly divided into the following categories: [0004] ①Content-based recommendation [0005] ② Recommendation based on Collaborative Filtering-Based [0006] ③Hybrid recommendation system. [0007] The content-based recommendation algorithm completes ...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q10/04G06Q30/06
CPCG06Q10/04G06Q30/0631
Inventor 胡建国郑慧琳李仕仁
Owner GUANGZHOU INTELLIGENT CITY DEV INST
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