WO2023093783A1

WO2023093783A1 - Distributed recommendation method for mass digital information

Info

Publication number: WO2023093783A1
Application number: PCT/CN2022/133898
Authority: WO
Inventors: 唐英
Original assignee: 苏州凉白开网络科技有限公司
Priority date: 2021-11-25
Filing date: 2022-11-24
Publication date: 2023-06-01
Also published as: CN114117218A

Abstract

The present invention belongs to the technical field of mass information processing. Particularly disclosed is a distributed recommendation method for mass digital information. The specific step flow of the distributed recommendation method for mass digital information is as follows: step 1: acquiring content of a large amount of digital information from the Internet, storing the content of the digital information, and classifying and processing the digital information; step 2: studying the recorded digital information, and recording the categories to which the digital information belongs after performing classification and processing, and finding the relationship between the digital information and corresponding link addresses of the digital content; and step 3: monitoring, in real time, a digital information link, which is opened and operated by a user over the Internet, comparing digital content that is queried by the user with the originally recorded digital information, removing the digital information that is the same as that found by the user, and pushing the remaining digital information in the form of links. By means of the method, associated digital information can be learned while a query for digital information by a user is effectively fulfilled.

Description

A Distributed Recommendation Method for Massive Digital Information

technical field

The invention relates to the technical field of mass information processing, in particular to a distributed recommendation method for mass digital information.

Background technique

The rapid development of science and technology and information technology in the 21st century, especially with the development and popularization of Internet technology, the rapid growth of network information resources, has now entered an era of digital information explosion. The so-called digital information refers to information content such as articles, pictures, sounds, and images published on the Internet. As Web2.0 replaces Web1.0, Web2.0 has become a platform for digital information sharing. Since Web2.0 pays more attention to the interaction of users, users are not only the viewers of website content, but also the creators of website content. Therefore, digital information is increasing rapidly. Among the massive digital information, it will become difficult for people to find the exact information they need. It's getting harder and harder.

There are three most common ways to obtain digital information: the first is regular web page information links, such as popular post recommendations on portal websites, news links, etc.; the second is that users search for the desired information through search engines, commonly used search engines Generally include Google, Bing, Baidu, etc.; the third is to recommend information to users through the introduction of friends, sending links or information keywords. Among the above three methods, the search engine is the best way to quickly find the target information. When users are relatively clear about the information they need, they can easily find the information they need by using search engines through keyword searches. However, search engines cannot fully meet the needs of users for information discovery, because in many cases, users do not know their needs clearly, or their needs are difficult to express with simple keywords, or they need to be more in line with them. Information about personal tastes and preferences. Therefore, the distributed recommendation method is very important in meeting people's query needs, but the existing digital information distributed recommendation method cannot remove the digital information with the same query content when recommending information, thus pushing out a large number of The content of the digital information is exactly the same as that of the original query digital information, which makes it difficult to meet the needs of digital information query.

Contents of the invention

The purpose of the present invention is to provide a distributed recommendation method for massive digital information, so as to solve the problem that the existing digital information distributed recommendation method proposed in the above-mentioned background technology cannot perform information recommendation on digital information with the same query content. The content of a large amount of digital information pushed out is exactly the same as that of the original query digital information, which leads to the problem that it is difficult to meet the needs of digital information query.

In order to achieve the above object, the present invention provides the following technical solution: a distributed recommendation method for massive digital information, the specific steps of the distributed recommendation method for massive digital information are as follows:

Step 1: Obtain a large amount of Internet digital information content, store the digital information content, and classify the digital information;

Step 2: Research the recorded digital information, record the category after classification and processing, and find the relationship between these digital information and the connection address of the corresponding digital content;

Step 3: Real-time monitoring of digital information links opened and operated by users on the Internet, and saving the opened digital information links;

Step 4: Analyze the digital information link opened by the user to determine the category it belongs to;

Step 5: According to the category of the digital information inquired by the user, perform a quick query in the original stored digital information content, and find a category similar or identical to it;

Step 6: Push the same or similar digital information link and display it on the interface opened by the user.

Preferably, in the step five, when the user opens the digital information content on the computer interface, firstly, it is necessary to search for the category it belongs to according to the digital information content, and search according to this category in the digital information saved in the original record. When the category is similar or the same as the category queried by the user, the queried digital information needs to be removed from the recorded category, and the remaining digital information is sent to the window opened by the user in the form of a link, and pops up continuously.

Preferably, in said step 1, when storing and classifying these digital information, firstly, according to the technical fields described in the information, including multiple technical fields such as industry, medical treatment, life, etc., in these various technical fields, it can be It is subdivided into computer hardware, software, peripheral equipment and communication network equipment.

Preferably, the distributed recommendation method for massive digital information requires the use of a digital information monitoring module, a first digital information storage module, a classification module, a second digital information storage module, a digital information category judgment module, a digital information Push modules.

Preferably, the digital information monitoring module is used to monitor the content of digital information inquired by users in the network window at any time, and transmit the information in time, and the first digital information storage module is used to collect a large amount of digital information on the network , and record and save these digital information, the classification module is used to classify the digital information stored in the first digital information storage module, and find the association relationship with similar or related digital content connection addresses, the second digital information The information storage module stores and records the digital information queried by the user in the network window, and the category judging module of the digital information judges the digital information stored in the second digital information storage module to identify the category it belongs to. Find similar or identical digital information links in the first digital information storage module, eliminate the identical digital links, and pick out the remaining digital links. The digital information push module is used to pick out those digital information The link is pushed to the window where the user queries digital information, and pops up directly.

Compared with prior art, the beneficial effect of the present invention is:

When the distributed recommendation method for massive digital information pushes digital information, it can compare the digital content queried by the user with the previously recorded digital information, and sort out the digital information searched for, and the rest of the digital information Pushing in the form of links can effectively satisfy the user's digital information query and at the same time understand the associated digital information.

Description of drawings

Figure 1 is a flow chart of the steps of the distributed recommendation method.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

In describing the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", " The orientation or positional relationship indicated by "outside", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, so as to Specific orientation configurations and operations, therefore, are not to be construed as limitations on the invention.

Please refer to Figure 1, the present invention provides a technical solution: a distributed recommendation method for massive digital information, the specific steps of the distributed recommendation method for massive digital information are as follows:

In said step 5, when the user opens the digital information content on the computer interface, it is first necessary to search for the category it belongs to according to the digital information content, and search according to the category in the digital information saved in the original record. When this category is similar or the same category, it is necessary to remove the queried digital information from the recorded category, and send the remaining digital information to the window opened by the user in the form of a link, and keep popping up.

In the first step, when the digital information is stored and classified, it can first be classified according to the technical fields described in the information, including multiple technical fields such as industry, medical treatment, and life, and can be subdivided in these various technical fields For computer hardware, software, peripheral equipment and communication network equipment.

The distributed recommendation method for massive digital information requires the use of a digital information monitoring module, a first digital information storage module, a classification module, a second digital information storage module, a digital information category judgment module, and a digital information push module.

The digital information monitoring module is used to monitor the content of the digital information inquired by the user in the network window at any time, and transmit the information in time; the first digital information storage module is also used to collect a large amount of digital information on the network, and These digital information are recorded and saved, and the classification module is used to classify the digital information stored in the first digital information storage module, and find the association relationship with the connection addresses of digital content similar or related to it, and the second digital information storage module Same as storing and recording the digital information queried by the user in the network window, the category judging module of the digital information judges the digital information stored in the second digital information storage module and identifies the category it belongs to. Find similar or identical digital information links in the information storage module, get rid of the digital links that are exactly the same as it, and pick out the remaining digital links, and the digital information push module is used to push the picked out digital information links to A window for users to query digital information, and it pops up directly.

The basic principles and main features of the present invention and the advantages of the present invention have been shown and described above. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic principles of the present invention. The present invention can be realized in other specific forms under the condition of certain characteristics; therefore, the embodiment should be regarded as exemplary and non-restrictive in every respect, and the scope of the present invention is determined by the appended claims. Requirements rather than the above description, therefore, it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference signs in the claims should not be regarded as limiting the rights involved. Require.

Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications and substitutions can be made to these embodiments without departing from the principle and spirit of the present invention. and modifications, the scope of the invention is defined by the appended claims and their equivalents.

Claims

A distributed recommendation method for massive digital information, characterized in that: the specific steps of the distributed recommendation method for massive digital information are as follows:

Step 1: Obtain a large amount of Internet digital information content, store the digital information content, and classify the digital information;

Step 2: Research the recorded digital information, record the category after classification and processing, and find the relationship between these digital information and the connection address of the corresponding digital content;

Step 3: Real-time monitoring of digital information links opened and operated by users on the Internet, and saving the opened digital information links;

Step 4: Analyze the digital information link opened by the user to determine the category it belongs to;

Step 5: According to the category of the digital information inquired by the user, perform a quick query in the original stored digital information content, and find a category similar or identical to it;

Step 6: Push the same or similar digital information link and display it on the interface opened by the user.
A distributed recommendation method for massive digital information according to claim 1, characterized in that: in step 5, when the user opens the digital information content on the computer interface, it is first necessary to search for the content of the digital information according to the content of the digital information. Category, according to this category, search in the digital information saved in the original record. When you find a category similar or identical to the category queried by the user, you need to remove the queried digital information from the category in the record, and send The remaining digital information is sent to the window opened by the user in the form of a link, which continues to pop up.
A distributed recommendation method for massive digital information according to claim 1, characterized in that: in the first step, when storing and classifying these digital information, firstly, according to the technical fields described in the information, including industrial , medical, life and other technical fields, in these various technical fields can be subdivided into computer hardware, software, peripheral equipment and communication network equipment.
A distributed recommendation method for massive digital information according to claim 1, characterized in that: the distributed recommendation method for massive digital information needs to use a digital information monitoring module, a first digital information storage module, A classification module, a second digital information storage module, a category judging module to which the digital information belongs, and a digital information push module.
A distributed recommendation method for massive digital information according to claim 4, characterized in that: the digital information monitoring module is used to monitor the content of digital information inquired by users in the network window at any time, and to monitor these information in a timely manner transmission, the first digital information storage module collects a large amount of digital information on the network, and records and saves these digital information, and the classification module is used to classify the digital information stored by the first digital information storage module, and Searching for the association relationship of the digital content connection address similar or related to it, the second digital information storage module stores and records the digital information queried by the user in the network window, and the digital information belongs to the category judgment module. The digital information stored in the second digital information storage module is judged, the category it belongs to is identified, the digital information link similar or identical to it is found in the first digital information storage module, the digital link identical to it is excluded, and the remaining digital links are For picking out, the digital information push module is used to push the selected digital information links to the window where the user queries digital information, and directly pops up.