WO2023046045A1

WO2023046045A1 - Fair evaluation system and method

Info

Publication number: WO2023046045A1
Application number: PCT/CN2022/120724
Authority: WO
Inventors: 马山河
Original assignee: 马山河
Priority date: 2021-09-23
Filing date: 2022-09-23
Publication date: 2023-03-30
Also published as: CN113704330A; CN113704330B

Abstract

The present disclosure relates to a fair evaluation system and method. The system comprises: a target recommendation component, which is used for recommending a plurality of targets to be evaluated that belong to the same evaluation item, and for appending, to each of said targets, an initial presentation serial number which is publicly presented to users; an evaluation user access component, which acquires unique identity information of each user, and gives the user an evaluation restriction on said targets, wherein the evaluation restriction is used for indicating at least two or more than two predetermined quantities of said targets in the same evaluation among user evaluations, and limiting the user evaluation to only once; a valid evaluation determination component, which acquires evaluation data that is input by each user, and determines an evaluation under the evaluation restriction to be valid evaluation data, so as to eliminate a non-valid evaluation result; and an evaluation result statistics compilation component, which performs statistics compilation, on time, on a publicly presented user evaluation score of each of said targets that belong to the same evaluation item, and modifies a presentation serial number according to the descending order of accumulative evaluation scores.

Description

Fair Evaluation System and Its Method

technical field

The present disclosure relates to a fair review system and method thereof.

Background technique

At present, the Internet business is developing rapidly, and many industries are conducting various evaluation projects, and invite the public to vote for evaluation through various application software.

However, due to the fact that the reviewers in the existing review system randomly review, artificially participate in order swiping, canvassing, etc., the review does not actually reflect people's actual attitudes towards each target to be reviewed in the reviewed project, resulting in the final review result. Sometimes it will completely deviate from people's intuitive feelings, causing people to increasingly distrust the evaluation results, forming a sense of distrust in social networks, resulting in extremely negative social effects. How to eliminate the problem of repeated comments by the same user and the problem of unanimous comments by many users for the same target to be commented based on canvassing become the real needs that need to be solved now.

technical problem

Therefore, people expect to obtain a review system and review method that can achieve fair review results when social networking is becoming more and more convenient. It can eliminate the injustice caused by canvassing, thereby rebuilding the trust of Internet review.

technical solution

Therefore, in order to solve the above-mentioned technical problems, this disclosure proposes a fair evaluation system, including: a target recommendation component, which is used to recommend multiple targets to be evaluated in the same evaluation project, and for each target to be evaluated, an additional The initial presentation sequence number presented; the commenting user accesses the component, obtains the unique identity information of each user, and gives the user the commenting limit for the target to be commented, and the commenting limit indicates that the user comments at least two or two of the target to be commented in the same comment. More than a predetermined number, and limit the user to only comment on the same comment item once; the effective comment determination component obtains the comment data input by each user, and determines the comment that contains a comment result for a predetermined number of targets as valid comment data , thus eliminating non-valid review results: and the review result statistics component, which regularly counts the publicly presented user review scores of each object to be reviewed belonging to the same review project, and modifies the presentation sequence number according to the order of the accumulated review scores.

According to the fair review system of the present disclosure, it also includes: an expert review access component, which is used to send multiple objects to be reviewed in the same review item that is not publicly presented to the expert user, so that the expert user can conduct expert review based on his expert review authority, Therefore, the target recommendation component performs target recommendation based on the expert review results, and recommends a predetermined number of top-ranked multiple targets to be reviewed for public presentation.

According to the fair review system of the present disclosure, when the scores of the expert review results of the plurality of targets to be reviewed reach a predetermined score, they are recommended to be presented publicly.

According to the fair review system of the present disclosure, it also includes: a review target collection component, which is used to openly solicit multiple targets for review belonging to the same review project from the public, and after a predetermined period of time or when the multiple targets for review exceed the predetermined After the number is reached, a review invitation is sent to the expert review access component, so that the experts who receive the invitation conduct expert review on the multiple collected targets to be reviewed through the expert review access component.

According to the fair review system of the present disclosure, the review user access component obtains the unique identity information of each user including user information of multiple application software associated with each other deployed on one or more terminal devices.

According to the fair review system of the present disclosure, the unique identity information of the user includes but is not limited to: one of various combinations of Weibo, QQ, WeChat, Twitter, SMS, and Facebook user information that point to the same user .

According to the fair review system of the present disclosure, the review result statistics component excludes the review results submitted by different applications associated with the user unique identity information based on the association relationship of the user information included in the user unique identity information.

According to the fair review system disclosed in this disclosure, it also includes: a review reward component, which compares the review results provided by any user through the review user access component with the review results at the end of the review, and when the similarity of the user review results reaches a predetermined Send the user a predetermined reward when the value is reached.

According to the fair review system of the present disclosure, it also includes: a transaction request component, based on the user's request, sending a transaction request for the target to be reviewed to the specific public who provided the collected target to be reviewed, and based on the confirmation of the specific public, Feedback the confirmation result to the requesting user.

According to the fair review system disclosed in the present disclosure, it also includes: a review project initiation component, which is used to create a new review project, and based on the created new review project, send a review project solicitation request to the review target collection component, and the request includes the target category , Evaluation method, collection time period, evaluation time period and evaluation reward method.

According to another aspect of the present disclosure, a fair review method is provided, including: using a target recommendation component to recommend multiple targets for review belonging to the same review project, and attaching an initial presentation for each target to be publicly presented to users Serial number; when a user accesses a review item through the comment user access component, obtain the unique identity information of each user, and give the user the comment restriction on the target of the review; obtain the user through the comment user access component based on the comment restriction only for the same comment A review result input by at least two or more targets to be reviewed in the project; the review data input by each user is obtained through the valid review determination component, and the review containing the review results for predetermined multiple targets is judged as valid Review data: through the review result statistics component, the publicly presented user review scores of each object to be reviewed belonging to the same review project are counted on time, and the presentation sequence number is modified according to the order of the accumulated review scores.

According to the fair review method of the present disclosure, it also includes: transmitting to the expert user through the expert review access component a plurality of objects to be reviewed belonging to the same review item that is not publicly presented, so that the expert user can conduct expert review based on his expert review authority, so that The target recommendation component executes target recommendation based on expert review results, and recommends a predetermined number of top-ranked multiple targets to be reviewed for public presentation.

According to the fair review method of the present disclosure, it further includes: when the scores of the expert review results of the plurality of targets to be reviewed reach a predetermined score, recommending to publicly present the target to be reviewed.

According to the fair review method of the present disclosure, it also includes: openly soliciting multiple targets for review belonging to the same review project from the public through the review target collection component; after a predetermined period of time or after the multiple targets for review exceed a predetermined number, A review invitation is sent to the expert review access component; the experts who receive the invitation conduct expert review of the multiple collected targets to be reviewed through the expert review access component.

According to the fair review method of the present disclosure, the review user access component obtains the unique identity information of each user including user information of multiple application software associated with each other deployed on one or more terminal devices.

According to the fair review method of the present disclosure, the unique identity information of the user includes but is not limited to: one of various combinations of Weibo, QQ, WeChat, Twitter, SMS, and Facebook user information that point to the same user .

According to the fair review method of the present disclosure, it further includes: using the review result statistics component to exclude the review results submitted by different applications associated with the user's unique identity information based on the association relationship of the user information contained in the user's unique identity information .

According to the fair review method of the present disclosure, it also includes: comparing the review result provided by any user through the review user access component with the review result similarity at the end of the review, and submitting a report to the user when the similarity of the user's review result reaches a predetermined value The user sends a predetermined reward.

According to the fair review method of the present disclosure, it also includes: sending a transaction request for the target to be reviewed based on the user's request to the specific public who provided the target to be reviewed, and based on the confirmation of the specific public, sending the request to the user who sent the request Feedback confirms the result.

According to the fair review method of the present disclosure, it also includes: creating a new review project through the review project initiation component, and sending a review project solicitation request to the review target collection component based on the created new review project, the request includes target category, review method, collection time period, review time period and review reward method.

Beneficial effect

Through the fair evaluation system and method according to the present disclosure, since each user must evaluate one or more other objects to be evaluated in addition to evaluating the target to be evaluated, in order to form an effective evaluation result, Therefore, when people subjectively evaluate an object to be evaluated that is related to themselves, they will evaluate and select more substantively when evaluating other non-relational objects to be evaluated. Even if users make random comments when they comment on other non-relational objects to be evaluated, due to the randomness caused by their arbitrariness, they will not cause extremely unfair evaluation results to other objects to be evaluated. In addition, since the identity provided by this review system for users is unique identity information, it associates various participating application platforms and can obtain user identity information of different social software platforms on different terminals, so it will The user identity information of different platforms pointing to the same actual user is associated to form unique identity information. Therefore, the review system of the present disclosure can be associated with the unique identity information based on the user identity information of the software platform used by the actual user, thereby preventing the same actual user from Users make repeated comments on the same comment item, further eliminating the unfairness of Internet comments. Moreover, through the final data analysis, the data of swiping votes was identified and eliminated, which further ensured the fairness and justice of the review.

Other advantages, objectives and features of the present invention will partly be embodied through the following descriptions, and partly will be understood by those skilled in the art through the study and practice of the present invention.

Description of drawings

FIG. 1 is a schematic diagram of a fair review system according to the present disclosure.

FIG. 2 is a schematic flow chart of the fairness review method according to the present disclosure.

Embodiments of the present invention

The present invention will be described in further detail below in conjunction with the embodiments and accompanying drawings, so that those skilled in the art can implement it with reference to the description.

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.

The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to limit the present disclosure. As used in this disclosure and the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, in the following Herein, one of the two possible graphics cards may be referred to as the first deliberation target or as the second deliberation target. Depending on the context, the word "if" as used herein may be interpreted as "at" or "when" or "in response to a determination."

In order to enable those skilled in the art to better understand the present disclosure, the present disclosure will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

FIG. 1 is a schematic diagram of a fair review system according to the present disclosure. As shown in FIG. 1 , the fair review system 100 includes: a target recommendation component 105 , a review user access component 110 , an effective review determination component 115 and a review result statistics component 120 . Optionally, the fair review system 100 further includes one of an expert user access component 130, a review target collection component 135, a review item initiation component 140, a review reward component 145, or a target transaction request component 150 or a combination thereof.

Specifically, the target recommendation component 105 is used to recommend multiple targets to be reviewed belonging to the same review item, and attach an initial presentation number for each target to be reviewed to be publicly presented to the user. The initial serial number can be marked arbitrarily according to the target to be evaluated or marked according to the order of solicitation and acquisition. During the solicitation period, the numbers will continue to increase as the solicited targets increase until the solicitation task ends. Optionally, the target recommendation component 105 may also assign an initial presentation sequence number based on the expert review results output by the expert review access component 130 (to be described later).

When the public participates in target evaluation, they will access the evaluation system 100 through the evaluation user access component 110 . The review system 100 will obtain the unique identity information of each visiting user based on the access message, and give the user the review restrictions on the review target. The review user access component obtains the unique identity information of each user, including user information of multiple application software associated with each other deployed on one or more terminal devices. These applications include but are not limited to Weibo, QQ, WeChat, Twitter, SMS, and Facebook. These application software also contain user information dedicated to the software of the accessing user. These user information or user identifications are different from each other, but all point to the same actual user. To this end, when the review user access component 110 receives the user access message, through the user's authorization confirmation, obtains the exclusive user identity information of multiple application software deployed by the user on the current terminal device, and uses these exclusive user identities Information is associated with unique identity information for the fair review system 100 . In this way, the possibility of repeated evaluations by an actual user with different applications is eliminated. The specific user identity information of which application software needs to be obtained, based on which application software the item to be reviewed will be deployed on.

The commenting user access component 110 will give a specific commenting user a commenting restriction range based on the commenting restriction requirement set by a specific commenting item. For example, when evaluating the same evaluation item, each user needs to evaluate at least two or more objects to be evaluated, and no matter what application software is used, they can only evaluate once. In this way, each user who actually participates in the evaluation must evaluate one or more other objects to be evaluated in addition to the object to be evaluated in order to form an effective evaluation result. When evaluating an object to be evaluated that is related to oneself, when evaluating other non-relational objects to be evaluated, it will conduct more substantial evaluation and selection. Even if users make random comments when they comment on other non-relational objects to be evaluated, due to the randomness caused by their arbitrariness, they will not cause extremely unfair evaluation results to other objects to be evaluated.

The review user access component 110 includes favorites, which are used to store the review targets that one wants to review in the favorites. The number of favorites is limited to a predetermined number. Users can directly submit the review results that have been reviewed in the favorites. Each user can obtain different review score ranges based on the number of times of participating in the review and review history data. For users whose review history shows that their review results are highly similar to the final review results, they will be given a higher-score review authority, that is, the user's review of the target can be given a higher weight or a higher score range.

Optionally, the review user access component 110 includes a review target number selection unit and a review score selection unit, so as to facilitate the selection of the number of reviews and review scores to be given directly through the drop-down menu when conducting target reviews. In addition, the review user access component 110 may also include a warning unit, which issues a warning when the user makes a wrong operation, for example, the number of reviews exceeds the specified number, and the review score exceeds the weight or score assigned to the user.

After each user makes a review, the effective review determining component 115 will obtain the review data input by each user, and determine the review containing the review results for predetermined multiple targets as valid review data. Specifically, there may be situations where the same user uses different application software to conduct multiple evaluations. To this end, the effective review determination component 115 will conduct a correlation query based on the unique identity information of the application software that the user actually uses to access, to determine whether the user has already used other application software for review, so as to determine the validity of the review , if valid, the review result is transferred to the valid review result database, otherwise, the review result is discarded. Optionally, when the review user access component 110 uses different application software to participate in the target review again, it can delete its previous review results, so as to achieve the purpose of correcting its own review results.

Finally, the review result statistics component 120 counts the publicly presented user review scores of each object to be reviewed belonging to the same review project on time, and modifies the presentation sequence number according to the order of accumulated review scores. Therefore, the review result presenting means 125 always presents the current actual results on time, and the numbers and display order of all the targets to be reviewed in it are also publicly presented to the public in a timely manner. The evaluation result statistics component 120 excludes the evaluation results submitted by different applications associated with the user's unique identity information based on the association relationship of the user information contained in the user's unique identity information. By analyzing the user evaluation results in this way, the illegal (swipe ticket) data is found, analyzed and judged, and the illegal user evaluation data is finally eliminated to obtain a fair evaluation result.

Optionally, the expert review access component 130 of the fair review system according to the present disclosure transmits to the expert user a plurality of objects to be reviewed belonging to the same review item that is not publicly presented, so that the expert user can conduct expert review based on his expert review authority, thereby The target recommendation component 105 is made to perform target recommendation based on the expert review results, and recommend a predetermined number of top-ranked multiple targets to be reviewed for public presentation. In addition, optionally, when the scores of the expert review results of the plurality of targets to be reviewed reach a predetermined score, they are recommended to be publicly presented. The use of experts to pre-evaluate and recommend the evaluation targets can guide the general public to participate in the evaluation, see excellent targets faster, and improve the efficiency of public evaluation. Optionally, even after all objects to be evaluated are publicly presented, experts can conduct expert evaluation through the expert evaluation access component 130, so that newly added excellent objects can be recommended to the forefront of the ranking.

The fair review system 100 according to the present disclosure is a review system facing the public. The public can create their own accounts on the review system and initiate project reviews through the review project initiation component 140 . The evaluation item initiating component 140 is used to create a new evaluation item, and based on the created new evaluation item, send an evaluation item solicitation request to the evaluation target collection component, and the request includes the target category, evaluation method, collection time period, evaluation time period and Appraisal reward method.

Regardless of whether it is the owner of the fair review system 100 or a public user with an account, after initiating project review through the review project initiation component 140, the review target collection component 135 can be used to openly solicit multiple targets for review belonging to the same review project from the public , and after a predetermined period of time or after the plurality of targets to be reviewed exceeds a predetermined number, an invitation to review is sent to the expert review access component 130, so that the experts who receive the invitation can review the collected multiple targets to be review through the expert review access component Conduct an expert review.

In order to enhance the enthusiasm of users to participate in the review and eliminate the situation that users only make positive reviews on the targets they care about and make negative coping reviews on other additional targets, according to the review reward component 145 of the fair review system disclosed in the present The user proposes a reward. Specifically, since the non-utilitarian evaluation of additional targets more reflects the real value of the target, the final result of the non-utilitarian evaluation of additional targets will be more similar to the statistical results of the reviews. For this reason, the review reward component 145 will compare the review result provided by any user through the review user access component with the review result similarity at the review deadline after the review deadline, and send a report to the user when the similarity of the review result of the user reaches a predetermined value. The user sends a predetermined reward. In order to make the initial ranking of recommended targets more fair, the review reward component 145 also compares the target recommendation result provided by any expert through the expert review access component with the review result similarity at the end of the review, and compares the target recommendation result similarity A predetermined reward is sent to the expert when a predetermined value is reached. Optionally, for the rewarded experts, their evaluation weights can be increased, so that their evaluation results will be given higher weights in subsequent expert evaluations, and their weight in the initial target evaluations can be increased.

In order to facilitate public users to obtain their favorite targets to be reviewed, the fair review system according to the present disclosure further includes a transaction request component 150 through which users can send transaction requests. Such a transaction request could be for a purchase, contact for a production license, and the like. The user can send a transaction request for the target to be reviewed to the specific public who provided the collected target to be reviewed through the transaction request component 150 . After receiving the request, the specific public can send out whether to confirm the information, so as to feedback the confirmation result to the requesting user.

FIG. 2 is a schematic flow chart of the fairness review method according to the present disclosure. As shown in Figure 2, first at step S205, multiple targets to be reviewed belonging to the same review item are recommended by the target recommendation component, and an initial presentation number for each target to be reviewed is attached to the user for public presentation. At step S210, when a user accesses a review item through the review user access component, the unique identity information of each user is obtained, and the user is given a review limit for the review target. At step S215, the user obtains a review result input by the user through the review user access component based on the review restriction to at least two or more objects to be reviewed in the same review item. The review user access component 110 obtains the unique identity information of each user, including user information of multiple application software associated with each other deployed on one or more terminal devices. These applications include but are not limited to Weibo, QQ, WeChat, Twitter, SMS, and Facebook. These application software also contain user information dedicated to the software of the accessing user. These user information or user identifications are different from each other, but all point to the same actual user. To this end, when the review user access component 110 receives the user access message, through the user's authorization confirmation, obtains the exclusive user identity information of multiple application software deployed by the user on the current terminal device, and uses these exclusive user identities Information is associated with unique identity information for the fair review system 100 . In this way, the possibility of repeated evaluations by an actual user with different applications is eliminated. The specific user identity information of which application software needs to be obtained, based on which application software the item to be reviewed will be deployed on.

Thereafter, at step S220, the valid comment determining component acquires the comment data input by each user, and determines the comment including the comment results for a plurality of predetermined targets as valid comment data. Specifically, there may be situations where the same user uses different application software to conduct multiple evaluations. To this end, the effective review determination component 115 will conduct a correlation query based on the unique identity information of the application software that the user actually uses to access, to determine whether the user has already used other application software for review, so as to determine the validity of the review , if valid, the review result is transferred to the valid review result database, otherwise, the review result is discarded. Optionally, the review user access component 110 can delete its previous review results when using different application software to participate in the target review again, so as to achieve the purpose of modifying its own review results.

At step S225, the evaluation result statistics component is used to count the publicly presented user evaluation scores of each object to be evaluated belonging to the same evaluation item on time, and modify the presentation sequence number according to the order of the accumulated evaluation scores. Therefore, the review result presenting means 125 always presents the current actual results on time, and the numbers and display order of all the targets to be reviewed in it are also publicly presented to the public in a timely manner. The evaluation result statistics component 120 excludes the evaluation results submitted by different applications associated with the user's unique identity information based on the association relationship of the user information contained in the user's unique identity information.

In addition, according to the fair review method of the present disclosure, it also includes, at step S230, transmitting to the expert user through the expert review access component 130 a plurality of objects to be reviewed belonging to the same review item that is not publicly presented, so that the expert user can base on his expert The review authority conducts expert review, so that the target recommendation component 105 performs target recommendation based on the expert review results, and recommends a predetermined number of top-ranked multiple targets to be reviewed for public presentation. Optionally, when the expert review result scores of the plurality of targets to be reviewed reach a predetermined score, it is recommended to publicly present the target to be reviewed.

In addition, according to the fair review method of the present disclosure, it also includes, at step S235, openly soliciting multiple targets for review belonging to the same review project from the public through the review target collection component 135; After the objects to be evaluated exceed the predetermined number, an invitation for evaluation is sent to the expert evaluation access component 130; the experts who receive the invitation conduct expert evaluation on the multiple collected objects to be evaluated through the expert evaluation access component.

In addition, according to the fair review method of the present disclosure, it also includes, at step S220, using the review result statistics component based on the association relationship of the user information contained in the user unique identity information to exclude the difference associated with the user unique identity information. Review results for application submissions.

In addition, according to the fair review method of the present disclosure, it also includes, at step S245, comparing the review result provided by any user through the review user access component with the review result similarity at the time of the review deadline, and comparing the similarity of the user review result A predetermined reward is sent to the user when a predetermined value is reached.

In addition, according to the fair review method of the present disclosure, it also includes, at step S250, sending a transaction request for the target to be reviewed based on the user's request to the specific public who provided the collected target to be reviewed, and based on the confirmation of the specific public , feedback the confirmation result to the requesting user.

In addition, according to the fair review method of the present disclosure, it also includes, at step S240, creating a new review project through the review project initiation component, and sending a review project solicitation request to the review target collection component based on the created new review project, said The request includes the target category, review method, collection time period, review time period, and review reward method.

To sum up, through the fair evaluation system and method according to the present disclosure, since each user must evaluate one or more other objects to be evaluated in addition to evaluating the relationship object to be evaluated by himself when evaluating, the An effective evaluation result is formed. Therefore, when people subjectively evaluate an object to be evaluated that is related to themselves, when evaluating other non-relational objects to be evaluated, they will conduct more substantial evaluation and selection. Even if users make random comments when they comment on other non-relational objects to be evaluated, due to the randomness caused by their arbitrariness, they will not cause extremely unfair evaluation results to other objects to be evaluated. In addition, since the identity provided by this review system for users is unique identity information, it associates various participating application platforms and can obtain user identity information of different social software platforms on different terminals, so it will The user identity information of different platforms pointing to the same actual user is associated to form unique identity information. Therefore, the review system of the present disclosure can be associated with the unique identity information based on the user identity information of the software platform used by the actual user, thereby preventing the same actual user from Users make repeated comments on the same comment item, further eliminating the unfairness of Internet comments. Moreover, through the final data analysis, the data of swiping votes was identified and eliminated, which further ensured the fairness and justice of the review.

The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be pointed out that those of ordinary skill in the art can understand that all or any steps or components of the methods and devices of the present disclosure can be implemented on any computing device (including processors, storage media, etc.) or a network of computing devices, implemented in hardware, firmware, software or a combination of them, this is the basic knowledge that those skilled in the art use after reading the description of the present disclosure programming skills will do.

Therefore, the object of the present disclosure can also be achieved by running a program or a group of programs on any computing device. The computing device may be a known general-purpose device. Therefore, the object of the present disclosure can also be achieved only by providing a program product including program codes for realizing the method or device. That is, such a program product also constitutes the present disclosure, and a storage medium storing such a program product also constitutes the present disclosure. Obviously, the storage medium may be any known storage medium or any storage medium developed in the future.

It should also be pointed out that, in the apparatus and method of the present disclosure, obviously, each component or each step can be decomposed and/or reassembled. These decompositions and/or recombinations should be considered equivalents of the present disclosure. Also, the steps for performing the above series of processes may naturally be performed in chronological order in the order described, but need not necessarily be performed in chronological order. Certain steps may be performed in parallel or independently of each other.

The specific implementation manners described above do not limit the protection scope of the present disclosure. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

A fair review system comprising:

The target recommendation component is used to recommend multiple targets for review belonging to the same review project, and attach an initial presentation number for each target to be reviewed for public presentation to users;

The reviewing user accesses the component, obtains the unique identity information of each user, and gives the user a review limit for the review target, the review limit indicates that the user reviews at least two or more predetermined numbers of the target to be review in the same review, And restrict users to comment on the same comment item only once;

The valid review determination component obtains the review data input by each user, and determines the review containing one review result for a predetermined number of targets as valid review data, thereby eliminating ineffective review results: and

The evaluation result statistics component is used to count the publicly presented user evaluation scores of each object to be evaluated in the same evaluation project on a timely basis, and modify the presentation sequence number according to the order of the accumulated evaluation scores.
The fair review system as claimed in claim 1, further comprising:

The expert review access component is used to transmit multiple targets to be reviewed in the same review project that is not publicly presented to the expert user, so that the expert user can conduct expert review based on his expert review authority, so that the target recommendation component executes the target based on the expert review results recommended, and a predetermined number of top-ranked multiple targets to be reviewed are recommended for public presentation.
The fair review system according to claim 2, wherein when the scores of the expert review results of the plurality of targets to be reviewed reach a predetermined score, they are recommended to be publicly presented.
The fair review system according to any one of claims 1-3, further comprising:

The review target collection component is used to openly solicit multiple targets for review belonging to the same review project from the public, and send a review invitation to the expert review access component after a predetermined period of time or after the multiple targets to be reviewed exceed a predetermined number , so that the experts who receive the invitation conduct expert reviews on the multiple solicited targets to be reviewed through the expert review access component.
The fair review system according to claim 1, wherein said review user access component acquires unique identity information of each user including user information of multiple application software associated with each other deployed on one or more terminal devices.
The fair review system according to claim 5, wherein the unique identity information of the user includes but is not limited to: all kinds of related Weibo, QQ, WeChat, Twitter, SMS and Facebook user information pointing to the same user. One of the combinations.
The fair review system according to claim 6, wherein the review result statistics component excludes review results submitted by different applications associated with the user unique identity information based on the association relationship of the user information included in the user unique identity information.
The fair review system according to claim 1, further comprising: a review reward component, which compares the review result provided by any user through the review user access component with the review result similarity at the time of the review deadline, and compares the similarity of the review result of the user Send a predetermined reward to the user when the degree reaches a predetermined value.
The fair review system according to claim 4, further comprising: a transaction request component, based on the user's request, sending a transaction request for the target to be reviewed to the specific public who provided the collected target to be reviewed, and based on the specific public Confirmation, feedback the confirmation result to the requesting user.
The fair review system according to claim 4, further comprising: a review project initiation component, configured to create a new review project, and based on the created new review project, send a review project solicitation request to the review target collection component, the request Including target category, review method, collection time period, review time period and review reward method.
A fair review method, including:

Recommend multiple targets for review belonging to the same review project through the target recommendation component, and attach an initial presentation number for each target to be reviewed for public presentation to users;

When a user accesses a comment item through the comment user access component, obtain the unique identity information of each user, and give the user comment restrictions on the comment target;

Obtaining a review result entered by the user through the review user access component based on the review restriction to at least two or more targets to be reviewed in the same review item;

Obtain the comment data input by each user through the valid comment determination component, and determine the comments containing the comment results for predetermined multiple targets as valid comment data: and

Through the review result statistics component, the publicly presented user review scores of each object to be reviewed belonging to the same review project are counted on time, and the presentation sequence number is modified according to the order of the cumulative review scores.
The fair evaluation method as claimed in claim 11, further comprising:

Through the expert review access component, multiple targets to be reviewed belonging to the same review item that are not publicly presented are transmitted to the expert user, so that the expert user can conduct expert review based on his expert review authority, so that the target recommendation component executes target recommendation based on the expert review results, A predetermined number of top-ranked targets to be reviewed are recommended for public presentation.
The fair evaluation method as claimed in claim 12, further comprising:

When the scores of the expert review results of the plurality of targets to be reviewed reach a predetermined score, it is recommended to publicly present the targets to be reviewed.
The fair review method according to any one of claims 11-13, further comprising:

Publicly solicit multiple targets for review belonging to the same review project through the review target collection component;

sending a review invitation to the expert review access component after a predetermined time period or after the plurality of objects to be reviewed exceeds a predetermined number;

Experts who receive the invitation conduct expert reviews on the multiple solicited targets to be reviewed through the expert review access component.
The fair review method according to claim 11, wherein said review user access component obtains the unique identity information of each user including user information of multiple application software associated with each other deployed on one or more terminal devices.
The fair review method as claimed in claim 15, wherein the unique identity information of the user includes but is not limited to: all kinds of related Weibo, QQ, WeChat, Twitter, SMS and Facebook user information pointing to the same user. One of the combinations.
The fair review method as claimed in claim 16, further comprising:

Based on the association relationship of the user information included in the user unique identity information, the evaluation result statistics component excludes the evaluation results submitted by different applications associated with the user unique identity information.
The fair evaluation method as claimed in claim 11, which also includes:

Compare the similarity between the review results provided by any user through the review user access component and the review results at the end of the review, and send a predetermined reward to the user when the similarity of the user's review results reaches a predetermined value.
The fair evaluation method as claimed in claim 14, which also includes:

Based on the user's request, a transaction request for the target to be reviewed is sent to the specific public who provided the collected target to be reviewed, and based on the confirmation of the specific public, the confirmation result is fed back to the requesting user.
The fair evaluation method as claimed in claim 14, which also includes:

Create a new review project through the review project initiation component, and send a review project solicitation request to the review target collection component based on the created new review project. The request includes the target category, review method, collection time period, review time period and review rewards Way.

the