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A method and system for recommending content and services based on large-scale machine learning

A machine learning and service recommendation technology, applied in the field of data processing, can solve problems such as inability to quickly identify and filter, rigid recommendation rules, and reduced data usage efficiency, to achieve efficient and accurate content and service recommendations, improve resource release efficiency, and high The effect of user satisfaction

Active Publication Date: 2021-03-05
GUANGDONG UCAP INTERNET INFORMATION TECH +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the huge amount of information meets the needs of users in the information age, the growth rate of the amount of information and the quality of data far exceed the speed of user processing, resulting in users being unable to quickly identify and filter out the required information in the face of massive information, making the data Use efficiency is reduced due to information redundancy, and information overload occurs
[0003] At present, the existing technology is to use the method of rule recommendation to provide users with interesting content and services. There are generally problems such as rigid recommendation rules and the need to formulate rules in advance before recommendation.

Method used

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  • A method and system for recommending content and services based on large-scale machine learning
  • A method and system for recommending content and services based on large-scale machine learning
  • A method and system for recommending content and services based on large-scale machine learning

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Embodiment 1

[0029] see figure 1 , a method for recommending content and services based on large-scale machine learning provided in this embodiment, the examples given are only used to explain the present invention, and are not used to limit the scope of the present invention. The method specifically includes the following steps:

[0030] S1. Define the feature tags of users and resources at a coarse grain level, and associate tags by calculating tag weights;

[0031] S2. Mining the relationship between users and resources in a fine-grained manner, using BP neural network learning rules to carry out machine training on data and building models, and scoring and sorting recommended resources by calculating recommendation degrees;

[0032] S3. Recommending the resource with the highest score to the front-end user.

[0033] Wherein, S1 also includes the following steps:

[0034] S1.1. The system analyzes user needs through user attributes and behaviors and search engine retrieval rules;

...

Embodiment 2

[0100] see Figure 4 , a content and service recommendation system based on large-scale machine learning provided in this embodiment, the examples given are only used to explain the present invention, and are not used to limit the scope of the present invention. The system specifically includes the following modules:

[0101] Data preprocessing module: preprocess the gathered resources to remove noise;

[0102] Indexing association module: By analyzing user needs, the system automatically sorts out relevant website content and services such as columns, categories, and interactive content that users are interested in, marks tags and calculates weights to associate users with website resources;

[0103] Intelligent recommendation module: monitor and record user identity, access and search behavior in real time, and actively push the website resources that the user is interested in to the user by analyzing relevant data such as the content visited and searched by the user, the t...

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Abstract

The present invention provides a method and system for recommending content and services based on large-scale machine learning. The method includes: the system adopts semi-supervised learning to realize intelligent recommendation. Tag weights are associated with tags; secondly, the relationship between users and resources is mined in a fine-grained manner, and BP neural network learning rules are used to carry out machine training on the data and build a model. Relevant display of resource content and personalized service recommendation, the present invention combines BP neural network technology with recommendation technology, makes full use of accumulated website big data, realizes intelligent recommendation through large-scale machine training, and has strong nonlinear mapping ability The flexible network structure effectively improves the efficiency of resource publishing, realizes fast, efficient and accurate content and service recommendations, and achieves higher user satisfaction.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a method and system for recommending content and services based on large-scale machine learning. Background technique [0002] With the rapid development of the Internet, the amount of information has exploded. Although the huge amount of information meets the needs of users in the information age, the growth rate of the amount of information and the quality of data far exceed the speed of user processing, resulting in users being unable to quickly identify and filter out the required information in the face of massive information, making the data The efficiency of use is reduced due to the redundancy of information volume, and information overload occurs. [0003] At present, the existing technology is to provide users with interesting content and services in the form of rule recommendation, but there are generally problems such as rigid recommendation rules...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06N3/04G06N3/08
CPCG06F16/9535G06N3/08G06N3/084G06N3/045
Inventor 汪敏严妍贾亦赫刘轩山周键王静
Owner GUANGDONG UCAP INTERNET INFORMATION TECH