Keyword recommending method and system based on latent Dirichlet allocation (LDA) model

A recommendation method and recommendation system technology, applied in the field of keyword recommendation based on the latent Dirichlet allocation model, which can solve problems such as difficult keyword recommendation

Active Publication Date: 2016-06-15
ALIBABA (CHINA) CO LTD
View PDF6 Cites 47 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, it is impossible to provide more targeted keyword recommendations for more subdivided texts in a certain industry. For example, it is possible to get more accurate keyword recommendations for sports-related topics, but it is difficult to get the second-level classification of sports cars. keyword recommendation

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Keyword recommending method and system based on latent Dirichlet allocation (LDA) model
  • Keyword recommending method and system based on latent Dirichlet allocation (LDA) model
  • Keyword recommending method and system based on latent Dirichlet allocation (LDA) model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0029] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way taken as limiting the invention, its application or uses.

[0030]Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered part of the description.

[0031] In all examples shown and discussed herein, any specific values ​​should be construed as exemplary only, and not as limitations. Therefore, other instances of the exemplary embodiment may have diff...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a keyword recommending method and system based on a latent Dirichlet allocation (LDA) model. The keyword recommending method comprises basic LDA training and incremental LDA training. The basic LDA training aims at training texts, probability distribution of basic words to the theme and probability distribution of basic texts to the theme are obtained; the incremental LDA training aims at specific incremental seed words, an incremental LDA model of a training text matched with the incremental seed words is calculated, probability distribution of incremental words to the theme and probability distribution of incremental texts to the theme are obtained, and finally probability distribution of full words to the theme and probability distribution of full texts to the theme are formed. The correlation weight and the final correlation score of any two words in the full model are calculated, and one or more keywords the correlation score of which is highest is recommended. By employing the incremental training model, the theme clustering precision is greatly improved, the theme diversity is increased, and the quality of keywords in the theme is substantially improved.

Description

technical field [0001] The present invention relates to artificial intelligence technology, more specifically, to a keyword recommendation method and system based on a Latent Dirichlet Allocation (LDA) model. Background technique [0002] In search engines, the application of recommendations is a trend in the development of search engines, especially in the context of wireless search, the demand for recommendations is particularly important. Because in the case of wireless search, the cost of obtaining information is higher when the user has a smaller screen, and the user hopes that the machine can better understand the user's needs and provide similar recommendations while satisfying the current search query. query information, so it is especially important to cut into recommendations in search. [0003] In current search engines, there are roughly two types of usage scenarios for recommendations. One is to provide users with some personalized recommendations based on the...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N20/00
CPCG06F16/335G06F16/951G06N20/00G06F16/00G06F16/3346G06F16/3347G06F40/242G06F40/30G06N7/01G06N5/04
Inventor 吴敬桐李天宁
Owner ALIBABA (CHINA) CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap