Content recommendation method and device

A content recommendation and content technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of extreme thinking, narrowness, not one, etc., and achieve the effect of expanding horizons and preventing addiction

Inactive Publication Date: 2018-08-21
STATE GRID OF CHINA TECH COLLEGE +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing content recommendation method still has the following defects: First, the existing algorithm is still based on the content recommendation method or collaborative filtering method. The analysis carried out, because many searches by users do not have statistical significance, and they don’t want to read it again after viewing it once. After counting these contents, it will affect the judgment of the content that the user really cares about, resulting in inaccurate push content; second

Method used

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  • Content recommendation method and device
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Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0030] Example one:

[0031] The method includes the following steps: S1, record user personal information through the user login module of the user terminal, and establish a user personal information database; S2 establish a user search information record database and associate it with the user personal information database; S3, record user search keys Words and the browsing time of the corresponding search results, create a vector table, upload it to the server, record the browsing time of each user for different content within a certain period of time, and arrange them from long to short according to the proportion of browsing time , And push the top ranked M content to the personalized content display module on the user side; S4. Crawl network resources through web crawlers and push them to the popular content display module on the user side; S5. The user extracts the associated user differentiated content and pushes it to the associated user content display module on the use...

Example Embodiment

[0032] Embodiment two:

[0033] In order to further enhance the accuracy of content recommendation, this method updates and learns user search keywords in real time. After each search, the user search keyword database can be updated. The specific implementation is as follows: The method includes the following steps, S1 , Record the user's personal information through the user login module of the user terminal, and establish a user personal information database; S2 establish a user search information record database and associate it with the user personal information database; S3, record user search keywords and browse corresponding search results Time, create a vector table, upload it to the server, and record the browsing time of each user for different content within a certain period of time, and arrange them from long to short according to the proportion of browsing time, and sort the top M A content is pushed to the personalized content display module on the user side; S4, ne...

Example Embodiment

[0034] Example three:

[0035] With the explosive growth of the amount of information on the Internet, information with similar content is spread on the Internet, and the searched content is often the same, but divided into different pieces of content and pushed to users, resulting in a poor user experience of viewing content and affecting reading Experience, as a further improvement, the method includes the following steps: S1, record user personal information through the user login module of the user terminal, and establish a user personal information database; S2 establish a user search information record database and associate it with the user personal information database; S3. Record the user's search keywords and the browsing time of the corresponding search results, create a vector table, upload it to the server, and record the browsing time of each user for different content within a certain period of time, and proceed according to the proportion of the browsing time Arra...

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Abstract

The invention relates to a content recommendation device which comprises a client and a server, wherein the client comprises a user login module, a user search information recording module and a display module; the server comprises a content push module and a user information data recording module. When individual contents are pushed, a simple recommendation method based on contents is improved, according to corresponding relationships between counted contents and browsing times, preference degrees of users about different contents can be calculated in real time, then contents can be pushed according to the preference degrees, and relatively precise pushing can be achieved; meanwhile, to develop potential content users, contents with similar habit preference are simultaneously pushed; a hot information display module is additionally set up ultimately, users can be assisted to pay attention to hot information, the view of users can be widened, and the users can be prevented from being addicted to the individual contents. The invention further provides a content recommendation method.

Description

technical field [0001] The present invention relates to a method and device for content recommendation. Background technique [0002] With the increase of Internet information, users generally need to retrieve the information they need. Due to differences in retrieval levels, it is often difficult for some users to obtain useful information quickly and effectively. At present, many user terminals push content based on user interests through content recommendation. For example, Chinese invention patent CN106202131A discloses a news recommendation method based on user interest; The recommendation method and system mainly weight the feature items of the user rating matrix according to the established query-multimedia classification matrix to obtain an improved user rating matrix, and then combine the user similarity based on the user query vector and the user rating matrix based The user similarity is calculated to obtain the overall similarity of users. However, the existing...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 谢清玉王乃玉王文明秦衡李荣凯赵衍恒
Owner STATE GRID OF CHINA TECH COLLEGE
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