CN112052389A - Knowledge recommendation method based on regional chain - Google Patents
Knowledge recommendation method based on regional chain Download PDFInfo
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- CN112052389A CN112052389A CN202010881242.1A CN202010881242A CN112052389A CN 112052389 A CN112052389 A CN 112052389A CN 202010881242 A CN202010881242 A CN 202010881242A CN 112052389 A CN112052389 A CN 112052389A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
Abstract
The invention discloses a knowledge recommendation method based on a regional chain, which comprises the following algorithm steps: extracting the information of the user in the area chain; analyzing and processing the information of the user through an internal program; the block chain system feeds the knowledge type recommended by the model back to the user for judgment; the user can continuously provide information to optimize the model, and the model can be more similar to be established according to more specific information, so that the production recommendation can be more accurately produced for the user; the model outputs new knowledge recommendations and provides output reasons; and providing a corresponding learning scheme according to the knowledge type. The method analyzes the use habits of the user by matching with the database, outputs new knowledge recommendation through the model and provides output reasons, returns to the step three to continue to judge the user, carries out closed-loop circular recommendation until the user is satisfied, can continuously satisfy the user, and improves the market competitiveness.
Description
Technical Field
The invention relates to the field of regional chains, in particular to a knowledge recommendation method based on a regional chain.
Background
The regional chain is also called a blockchain, which is a term used in the information technology field. In essence, the system is a shared database, and the data or information stored in the shared database has the characteristics of 'unforgeability', 'whole-course trace', 'traceability', 'public transparency', 'collective maintenance', and the like. Based on the characteristics, the block chain technology lays a solid 'trust' foundation, creates a reliable 'cooperation' mechanism and has wide application prospect.
What is the blockchain? From a technological level, the blockchain involves many scientific and technical problems such as mathematics, cryptography, internet and computer programming. From the application perspective, the blockchain is simply a distributed shared account book and database, and has the characteristics of decentralization, no tampering, trace remaining in the whole process, traceability, collective maintenance, public transparency and the like. The characteristics ensure the honesty and the transparency of the block chain and lay a foundation for creating trust for the block chain. And the rich application scenes of the block chains basically solve the problem of information asymmetry based on the block chains, and realize the cooperative trust and consistent action among a plurality of main bodies.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. Block chain (Blockchain), an important concept of bitcoin, is essentially a decentralized database, and as the underlying technology of bitcoin, is a series of data blocks produced by using cryptographic method to correlate, each data block contains information of a batch of bitcoin network transactions, and is used for verifying the validity (anti-counterfeiting) of the information and generating the next block
However, the existing block chain is too superficial to the information analysis of the user, no interaction exists, a learning method which is preferred by the user is difficult to recommend, the customer satisfaction is poor, and in addition, the safety of the area chain information needs to be enhanced.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a knowledge recommendation method based on a regional chain to solve the problems in the prior art.
The technical purpose of the invention is realized by the following technical scheme:
a knowledge recommendation method based on a regional chain comprises the following algorithm steps:
the method comprises the following steps: firstly, extracting information of a user in an area chain;
step two: analyzing and processing the information of the user through an internal program, and integrating recommendation of knowledge required by the user according to the information of the user;
step three: the block chain system feeds the knowledge type recommended by the model back to the user for judgment, the user feeds back whether the feedback accords with the expectation, and if the feedback accords with the expectation, a corresponding learning scheme is provided according to the knowledge type;
step four: if the model is not in accordance with the expectation, the user can continue to provide information to optimize the model, and the model can be more similar to the establishment according to more specific information, so that the production recommendation can be more accurately produced and recommended for the user;
step five: the model outputs new knowledge recommendations and provides output reasons;
step six: and providing a corresponding learning scheme according to the knowledge type.
Further, the processing of the user information in the second step specifically includes extraction of the user information, information analysis processing, information type modeling and model output recommendation.
Further, in the fifth step, the new recommendation is returned to the third step to continue to be judged for the user, and closed-loop circular recommendation is performed until the user is satisfied.
Further, the step six is to recommend a comprehensive learning scheme more conforming to the learning habits of the user according to the learning habits of the user, wherein the learning scheme comprises various modes such as network learning, book learning, off-line course learning and self-learning.
Further, the extraction of the user information is realized by collecting and quantifying behavior information generated by browsing a knowledge page by a user in a learning process through a monitoring technology of the mobile terminal, wherein the behavior information comprises information of browsing speed, page access times, screen click times, collection behavior, copying behavior, sharing behavior, comment behavior and praise behavior.
Further, the information analysis process obtains the interest characteristic behavior indexes including the staying power, the attention degree and the recognition degree from the extracted information, and weights and calculates the quantitative values of the indexes to obtain the interest value of the current user for the concerned interest point.
Further, the information type modeling inputs the information into a modeling program after analyzing and processing the information, and the model compares the big data of the user according to the information and then makes a prediction according with the user requirement of the big data.
Furthermore, the recommendation process of the knowledge can carry out whole-course information safety protection, the user information of the information base is protected through a plurality of layers of protection walls, the user information is prevented from being infringed, in addition, messy code storage is carried out during the storage of the user information, the information of the user can be extracted only by a specific decoding program, the safety of the user information is further improved, the safety of the privacy information of the user is ensured, in addition, the extraction record of the user can be recorded in real time by the method, public supervision is accepted, and the safety of the user information is monitored in multiple directions.
In conclusion, the invention has the following beneficial effects:
1. in conclusion, the individual behaviors of the users have great relationship with the interests of the users, the interest degree of the users on the webpage is closely related to the operation behaviors of the users on the regional chain, and big data can hide the interest information of the users on the daily behaviors of the users, the analysis of the use habits of the users is matched with the database for analysis, in addition, new knowledge recommendation is output through the model and output reasons are provided, the judgment is continued to the users in the third step, closed-loop circulation recommendation is carried out until the users are satisfied, and the method can be used for continuously satisfying the users and improving the market competitiveness;
2. the method comprises the steps that firstly, user information of an information base is protected through a plurality of layers of protective walls, the user information is prevented from being invaded, in addition, messy code storage is carried out during the user information storage, the user information can be extracted only through a specific decoding program, the safety of the user information is further improved, the safety of the user privacy information is guaranteed, in addition, the extraction record of the user can be recorded in real time, public supervision is accepted, and the user information safety is monitored in multiple directions.
Drawings
Fig. 1 is an overall structural diagram of a knowledge recommendation method based on a regional chain according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In which like parts are designated by like reference numerals. It should be noted that the terms "front," "back," "left," "right," "upper" and "lower" used in the following description refer to directions in the drawings, and the terms "bottom" and "top," "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
Referring to fig. 1, a knowledge recommendation method based on a regional chain in a preferred embodiment of the present invention includes the following algorithm steps:
the method comprises the following steps: firstly, extracting the information of a user in an area chain;
step two: analyzing and processing the information of the user through an internal program, integrating recommendation of knowledge required by the user according to the information of the user, and specifically extracting the information of the user, analyzing and processing the information, modeling the information type and outputting the recommendation by a model;
step three: the block chain system feeds the knowledge type recommended by the model back to the user for judgment, the user feeds back whether the feedback accords with the expectation, and if the feedback accords with the expectation, a corresponding learning scheme is provided according to the knowledge type;
step four: if the model is not in accordance with the expectation, the user can continue to provide information to optimize the model, and the model can be more similar to the establishment according to more specific information, so that the production recommendation can be more accurately produced and recommended for the user;
step five: the model outputs new knowledge recommendation and provides an output reason, and returns to the step three to continue judging for the user, and performs closed-loop circular recommendation until the client is satisfied, so that the method can be a method which is continuously satisfied for the user, and the market competitiveness is improved;
step six: providing a corresponding learning scheme according to the knowledge type, and recommending a comprehensive learning scheme more conforming to the learning habit of the user according to the learning habit of the user, wherein the learning scheme comprises various modes such as network learning, book learning, off-line course learning and self-learning.
The extraction of the user information collects and quantifies the behavior information generated by browsing the knowledge page by the user in one learning process through the monitoring technology of the mobile terminal, wherein the behavior information comprises the information of browsing speed, page access times, screen click times, collection behavior, copy behavior, sharing behavior, comment behavior and praise behavior,
the information analysis processing obtains the interest characteristic behavior indexes including the staying degree, the attention degree and the recognition degree from the extracted information, weights and calculates the quantitative values of the indexes to obtain the interest value of the current user to the concerned interest point,
the information type modeling inputs the information into a modeling program after analyzing and processing the information, the model compares the big data of the user according to the database according to the information and then makes the prediction of the user requirement according with the big data,
the recommendation process of the knowledge can carry out whole-course information safety protection, the user information of the information base is protected through the multilayer protection wall at first, the user information is prevented from being invaded, messy code storage is carried out during storage of the user information, the information of the user can be extracted only by a specific decoding program, the safety of the user information is further improved, the safety of the user privacy information is guaranteed, in addition, the extraction record of the user can be recorded in real time by the method, public supervision is accepted, and the user information safety is monitored in multiple directions.
In conclusion, the individual behaviors of the users have great relationship with the interests of the users, the interest degree of the users on the webpage is closely related to the operation behaviors of the users on the regional chain, and the big data can hide the interest information of the users on the daily behaviors of the users.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A knowledge recommendation method based on a regional chain is characterized in that: the algorithm comprises the following steps:
the method comprises the following steps: firstly, extracting information of a user in an area chain;
step two: analyzing and processing the information of the user through an internal program, and integrating recommendation of knowledge required by the user according to the information of the user;
step three: the block chain system feeds the knowledge type recommended by the model back to the user for judgment, the user feeds back whether the feedback accords with the expectation, and if the feedback accords with the expectation, a corresponding learning scheme is provided according to the knowledge type;
step four: if the model is not expected to be optimized, the user can continue to provide information to optimize the model, and the model can be more similar to be established according to more specific information;
step five: the model outputs new knowledge recommendations and provides output reasons;
step six: and providing a corresponding learning scheme according to the knowledge type.
2. The knowledge recommendation method based on the regional chain as claimed in claim 1, wherein: and step two, the processing of the user information specifically comprises the extraction of the user information, information analysis processing, information type modeling and model output recommendation.
3. The knowledge recommendation method based on the regional chain as claimed in claim 1, wherein: and step five, returning the new recommendation to the step three to continue judging for the user, and performing closed-loop cyclic recommendation.
4. The knowledge recommendation method based on the regional chain as claimed in claim 2, wherein: and step six, recommending a comprehensive learning scheme more conforming to the learning habits of the user according to the learning habits of the user, wherein the learning scheme comprises various modes such as network learning, book learning, off-line course learning and self-learning.
5. The knowledge recommendation method based on the regional chain as claimed in claim 4, wherein: the user information is extracted, and behavior information generated when a user browses a knowledge page in a learning process is collected and quantified through a monitoring technology of the mobile terminal, wherein the behavior information comprises information of browsing speed, page access times, screen click times, collection behaviors, copying behaviors, sharing behaviors, comment behaviors and praise behaviors.
6. The knowledge recommendation method based on the regional chain as claimed in claim 5, wherein: and the information analysis processing obtains interest characteristic behavior indexes including the staying degree, the attention degree and the recognition degree from the extracted information, and weights and calculates the quantitative values of the indexes to obtain the interest value of the current user to the concerned interest point.
7. The knowledge recommendation method based on the regional chain as claimed in claim 6, wherein: the information type modeling inputs the information into a modeling program after analyzing and processing the information, and the model compares the big data of the user according to the database according to the information and then makes a prediction according with the user requirement of the big data.
8. The knowledge recommendation method based on the regional chain as claimed in claim 4, wherein: the information safety protection in the whole process can be carried out in the knowledge recommendation process, the user information of the information base is protected through a plurality of layers of protection walls, in addition, the messy code storage is carried out during the user information storage, the user information can be extracted only by a specific decoding program, and in addition, the extraction record of the method for the user can be recorded in real time.
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