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Intelligent recommendation method and device, computer equipment and readable storage medium

A recommendation method and intelligent technology, applied in computer parts, computing, and other database retrieval, etc., can solve problems such as difficulty in quickly and effectively obtaining recommended information, increased server pressure, and huge data volume, so as to reduce the amount of data calculation and storage The effect of increasing the amount, improving the calculation speed, and improving the operation efficiency

Active Publication Date: 2020-12-22
CHINA PING AN LIFE INSURANCE CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide an intelligent recommendation method, device, computer equipment and readable storage medium, which is used to solve the problems existing in the prior art when faced with massive user data, the current recommendation algorithm will be complex due to calculation and data volume Large size and other reasons lead to slow operation and increased pressure on the server, which in turn makes it difficult to obtain recommended information quickly and effectively

Method used

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  • Intelligent recommendation method and device, computer equipment and readable storage medium
  • Intelligent recommendation method and device, computer equipment and readable storage medium
  • Intelligent recommendation method and device, computer equipment and readable storage medium

Examples

Experimental program
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Embodiment 1

[0059] see figure 1 , an intelligent recommendation method in this embodiment, comprising:

[0060] S102: Obtain user information, and perform characterization processing on the user information to obtain a user vector.

[0061] S103: Invoke the product quantization process to segment the user vector to obtain several sub-vectors, identify the category of each sub-vector, and summarize the categories to obtain a user category set;

[0062] S104: calling the minimum hash process, comparing the similarity between the user category set and each reference category set in the preset index library, and setting the reference category set whose similarity exceeds the preset similarity threshold as the target category set; Wherein, the reference category set is obtained by pre-characterizing the preset reference information and calling the product quantization process, and is used to reflect the data set of the category of the sub-vector corresponding to the reference information. The...

Embodiment 2

[0069] This embodiment is a specific application scenario of the first embodiment above. Through this embodiment, the method provided by the present invention can be described more clearly and specifically.

[0070] Next, in the server running the intelligent recommendation method, the user vector is segmented to obtain several sub-vectors, and the category of each sub-vector is identified and summarized to obtain a user category set, and the user category set is combined with The reference category sets in the preset index library are compared to obtain the similarity, and the target category set is obtained according to the similarity. Finally, the related information corresponding to the target category set is used as the recommendation information as an example to describe the method provided in this embodiment. It should be noted that this embodiment is only exemplary, and does not limit the protection scope of the embodiment of the present invention.

[0071] figure 2 ...

Embodiment 3

[0154] see Figure 8 , an intelligent recommendation device 1 of this embodiment, comprising:

[0155] An information processing module 12, configured to obtain user information, and perform characterization processing on the user information to obtain a user vector;

[0156] The product quantization module 13 is used to call the product quantization process to segment the user vector to obtain several sub-vectors, identify the category to which each sub-vector belongs, and summarize the categories to obtain a user category set;

[0157] The minimum hash module 14 is used to call the minimum hash process to compare the similarity between the user category set and each reference category set in the preset index library, and compare the similarity of the reference categories whose similarity exceeds the preset similarity threshold. The category set is set as the target category set; wherein, the reference category set is obtained by pre-characterizing the preset reference infor...

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Abstract

The invention relates to the technical field of big data, and discloses an intelligent recommendation method and device, computer equipment and a readable storage medium, and the method comprises thesteps: obtaining user information, and carrying out the characterization of the user information to obtain a user vector; calling a product quantization process to segment the user vector to obtain aplurality of sub-vectors, identifying the category to which each sub-vector belongs, and summarizing the categories to obtain a user category set; calling a minimum hash process to perform similaritycomparison on the user category set and each reference category set in a preset index library, and setting the reference category set of which the similarity exceeds a preset similarity threshold as atarget category set; and taking the associated information corresponding to the target category set as recommendation information. According to the method, the fineness and the accuracy of user vector category identification are improved, the operation efficiency of the server is improved, the matching speed between the user information and the reference information in the index database is increased, and the data calculation amount and the data storage amount are reduced.

Description

technical field [0001] The present invention relates to the technical field of big data data analysis, in particular to an intelligent recommendation method, device, computer equipment and readable storage medium. Background technique [0002] The recommendation algorithm is an algorithm in computer science. Through some mathematical algorithms, it can be used to infer what the user may like. At present, the best place to apply the recommendation algorithm is mainly the Internet. The so-called recommendation algorithm is to use some behaviors of users, through some mathematical algorithms, to infer what the user may like. [0003] The current recommendation algorithm either adopts a label-based mapping method to recommend products to the user end, or adopts a Bayesian method to recommend products to the user end. [0004] However, the inventor realized that the current recommendation algorithm can run without pressure when faced with a small amount of data, but once faced w...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06Q30/02G06F16/9535G06F16/901G06K9/62
CPCG06Q30/0631G06Q30/0201G06F16/9535G06F16/9014G06F18/23213G06F18/22Y02D10/00
Inventor 王浩刘丹
Owner CHINA PING AN LIFE INSURANCE CO LTD
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