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Training method and system for digital information recommending and forecasting model

A digital information and predictive model technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as difficult user recommendation, user scoring data sparsity, etc., and achieve good recommendation effect

Inactive Publication Date: 2014-05-07
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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AI Technical Summary

Problems solved by technology

As mentioned above, the sparsity of user rating data makes it difficult to make accurate recommendations for users

Method used

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  • Training method and system for digital information recommending and forecasting model
  • Training method and system for digital information recommending and forecasting model
  • Training method and system for digital information recommending and forecasting model

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Embodiment Construction

[0043] In order to make the purpose, technical solution and advantages of the present invention clearer, the method and system for training a digital information recommendation prediction model according to an embodiment of the present invention will be further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0044] The invention models based on the hierarchical structure of the digital information and the association relationship between each hierarchy to improve the recommendation effect. The method and system of the present invention will be described in detail below by taking the music recommendation prediction model as an example.

[0045] Music categories can generally be divided into: songs, albums, singers, and genres, and there is a hierarchical structure among these categories, and different...

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Abstract

The invention discloses a training method and system for a digital information recommending and forecasting model; the training method comprises the following steps of: (1) receiving rating data; (2) determining different categories of digital information, wherein each category comprises multiple items, and correlative relations exist among the categories; and (3) establishing the model based on the correlative relations and training the obtained model, wherein the model comprises one or a plurality of parameters related to an assembly, and the assembly is an assembly of items of another category related to items of a category or a union of the assemblies of the items of other categories related to the items of a category. The trained forecasting module can effectively solve the problem of serious sparsity of available rating data caused by insufficient marking of users in practical recommendation and has very good recommendation effect.

Description

technical field [0001] The present invention relates to the field of digital information processing, in particular, to the field of digital information recommendation. Background technique [0002] With the rapid development of the Internet, users are facing the problem of information overload in various portal websites, e-commerce websites, video or music audio-visual websites, so mining the possible preferences of users and providing personalized services are crucial to improving user satisfaction and loyalty is of great significance. It is in this background that the recommendation system was produced, and it has developed very rapidly in the past two decades. IT giants such as Amazon, Google, Yahoo, etc., domestic companies such as Dangdang, Taobao, etc. have provided personalized recommendation systems in their different applications, which greatly facilitates users and brings huge benefits to businesses. [0003] Recommendation systems can be roughly divided into two...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 鲁凯王斌史亮李文娜李锐徐飞
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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