JP2003006375A - Evaluation distribution method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method by which evaluations of goods and services are distributed more extensively and accurately. SOLUTION: The method of this invention includes a storing step which associates and stores evaluation objects information indicating evaluation objects (Y1,...Yn), evaluators information (X1,...Xm) indicating evaluators and evaluation results information indicating evaluation results (E(Xi, Yj)) of the evaluation objects by the evaluators in a memory unit as computer readable forms, a specifying step which specifies by computer such a resemblant evaluator (Xr) of the second evaluator (Xq) as which each evaluation result ensemble (E(Xp, Y1),...E(Xp, Yn)) to each evaluation object by the first evaluator (Xp) resembles each evaluation results ensemble (E(Xq, Y1),...E(Xq, Yn)) to each evaluation object by the second evaluator (Xq), and a presentation step which presents to the first evaluator (Xp) information obtained from each evaluation results (E(Xr, Y1),...E(Xr, Yn)) by the resemblant evaluator.

Description

Detailed Description of the Invention

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for distributing evaluations of commonly supplied items such as goods and services. In particular, it relates to a method of removing a barrier when a legitimate evaluation is distributed in a conventional social environment by using a computer, a network, or both. Further, the present invention relates to various systems, computer programs, and recording media for this purpose.

[0001]

2. Description of the Related Art Conventionally, products such as electric appliances and sweets and services (that is, services) such as eating out have been evaluated and recommended to users. Typically, a method in which a small number of people and institutions with credibility and authority recommend a product or a menu of a store in a specialized magazine or the like has been adopted. Alternatively, although a little, a method was adopted in which users voted for evaluations and the ones with good evaluations were published. With the spread of the Internet, the latter method has become easier to implement than it used to be. However, the above-mentioned two conventional evaluation and distribution methods have more or less the following problems.

1) Generally, there is no mechanism for rewarding the person who has evaluated and the person who has accumulated and distributed the evaluation. Therefore, the evaluation results are hardly accumulated and distributed in the first place.

2) People who evaluate correctly are hard to get rewarded. For example, the less-known places are less popular, the price is lower, and they are not out of stock or crowded. On the other hand, even if it is not a great product, there is no mechanism for the authoritative evaluator to take responsibility, so irresponsible evaluation is circulated. Especially vulnerable to malicious propaganda and information manipulation.

3) For example, personal preference such as ramen preference is hardly reflected in the evaluation, and only evaluation in the greatest common divisor can be used. In addition, such an expression of a tasteful evaluation is troublesome in the first place, and usually, for a general consumer,
There is not enough motivation to express or distribute evaluations with tastes.

4) Since the conventional evaluation-based recommendation method recommends even products that the person already knows, it is not easy for the consumer to selectively make a new experience.

[0006]

SUMMARY OF THE INVENTION The present invention solves the above problems and provides a method, a system, a computer program, and a storage medium for the program, which further enhances the added value of evaluation distribution. .

[0007]

According to the method of the present invention, evaluation target information indicating an evaluation target (Y1, ..., Yn) and evaluator information (X1, ..., Xm) indicating an evaluator are used. , An accumulation step of accumulating in a storage device in a computer-readable format in association with evaluation result information indicating an evaluation result (E (Xi, Yj)) by an evaluator to be evaluated, and each evaluation of the first evaluator (Xp) The set (E (Xp, Y1), ..., E (Xp, Yn)) of each evaluation result for the target (Y1, ..., Yn) is each evaluation target (Y1) of the second evaluator (Xq). , ..., Y
(E (Xq, Y
1) ,. ． ． , E (Xq, Yn)), and one or more similar evaluators (Xr) that are the second evaluators (Xq), and specify by a computer from the information accumulated in the storage device. A step of presenting information obtained from each evaluation result (E (Xr, Y1), ..., E (Xr, Yn)) of the similar evaluator (Xr) to the first evaluator (Xp). This is the most important feature.

As a result, the evaluator (X who has honestly shown his / her own evaluation of some of the world's products in advance (for example, input from a mobile terminal via the Internet))
p) is by another person (Xr) who also honestly evaluated,
It is possible to obtain an evaluation result that also reflects the preference (for example, through the display of the same mobile terminal). As a result, the above-mentioned problems 1), 2) and 3) can be achieved.

The present invention also provides the first evaluator (Xp).
It is possible to limit the evaluation targets (Y1, ..., Yn) that serve as the similar reference in the specific step according to the selection of the above.

Thus, for example, instead of seeking people with similar tastes based on the tastes of all evaluation objects, for example, evaluation objects related to the situation at that time (for example, when selecting a ramen shop, I would like to refer to the opinions of people with similar tastes regarding food and interior sense. If I want to choose shoes, I would like to refer to the opinions of people with similar tastes regarding fashion and health.) You can efficiently obtain information from people who do it.

The present invention further includes a step of storing information indicating an attribute (or category) of each evaluation target in a storage device in a computer-readable format, and using the information, the evaluation target (Y1 which is the similar reference). , ..., Yn) can be restricted.

[0012] With this, it is possible to efficiently specify the category (attribute) or the like to be evaluated depending on the situation in consideration of the preference.

The present invention also provides the first evaluator (Xp).
The second evaluator (X1, ..., Xm) to be the similar target in the specific step can be limited in accordance with the selection of the above.

Even if the profiles of evaluation are similar (e.g., the distribution of evaluations in the space in which the evaluation target is the horizontal axis and the evaluation result is the vertical axis), for example, the evaluator who does not want to be referred to depending on the age group, occupation, and residential area. Or, there may be a group of evaluators who want to refer to, and in such a case, this feature enables efficient information acquisition.

The present invention further includes a step of storing information indicating attributes of each evaluator in a storage device in a computer-readable format, and using the information, the second evaluator (X1, ..., Xm) can be restricted.

As a result, the category of the "person" to be evaluated at that time can be specified in multiple ways or efficiently.

The present invention also provides the first evaluator (Xp).
The information shown in the presenting step can be limited in accordance with the selection etc.

As a result, the type of information presented can be limited and the information can be browsed efficiently.

The present invention further includes a step of storing information indicating an attribute of each evaluation target in a storage device in a computer-readable format, and using the information, the evaluation target (Y1 , ..., Yn) can be restricted.

As a result, the type of information to be presented can be limited in many ways or efficiently.

The present invention also provides an evaluation target (Y1, ..., Yn) which is a target for which information is displayed in the presenting step.
The attribute of the evaluation target that is used to limit the value can also be used to limit the evaluation target that is a similar reference in the specifying step.

As a result, the preference for the evaluation target related to the evaluation target for which information is requested is automatically taken into consideration,
Alternatively, it can be easily specified.

In the present invention, the evaluation target (Y1, ..., Yn) to which information is to be displayed in the presenting step is also included.
Can be limited to a specific evaluation target (Ys).

As a result, when some products / services to be purchased or used are specifically determined, it is possible to request an evaluation only for the evaluation target. Thereby, the above-mentioned subject 4) can be achieved.

The present invention is also for limiting the attribute of the limited specific evaluation target (Ys), which is a target of information in the presenting step, to the evaluation target which is a similar reference in the specific step. Can be used.

As a result, the preference for the evaluation object related to the evaluation object for which information is requested can be more specifically determined as follows.
Moreover, it can be designated automatically or simply.

The present invention also provides an evaluation target (Y1, ..., Yn) which is a target for which information is displayed in the presenting step.
Can be limited to evaluation targets that have not been evaluated by the first evaluator (Xi).

As a result, the above-mentioned problem 4) can be achieved more sufficiently.

In the present invention, the evaluation target (Y1, ..., Yn), which is a target of information in the presenting step, is also included.
Can be limited to the information about the evaluation target with a high evaluation result by the similar evaluator (Xr).

Thus, when selecting a store, it is possible to efficiently and quickly consider only the information about the highly evaluated objects.

In the present invention, the evaluation target (Y1, ..., Yn), which is the target of information in the presenting step.
Can be limited to the information about the evaluation target whose evaluation result by the similar evaluator (Xr) is low.

As a result, it is possible to avoid shopping at stores that should not be used and to carry out shopping.

According to the present invention, the similarity in the specific step is determined by each evaluation target (Y) of the first evaluator (Xp).
1 ,. ． ． , Yn) of each evaluation result set (E (X
p, Y1) ,. ． ． , E (Xp, Yn)) and each evaluation result set (E (Xq, Y1), ..., E for each evaluation target (Y1, ..., Yn) of the second evaluator (Xq).
(Xq, Yn)) can be judged by the similarity of each evaluation result as a vector configured as each component.

As a result, it is possible to calculate the evaluation similarity at high speed and efficiently perform pruning of the candidate set of similar evaluators.

In the present invention, it is possible not to consider the evaluation result of the evaluation target which has not been evaluated by the first evaluator in the determination of the vector construction or the vector similarity.

As a result, the evaluation result of the first evaluator who has a small evaluation result can be fully utilized.

The present invention can also prevent the evaluation result of the evaluation target which is not evaluated by the second evaluator from being considered in the determination of the construction of the vector or the similarity of the vectors.

As a result, it is possible to highly estimate the degree of similarity between the first evaluator and the second evaluator who has the exact evaluation, and it is possible to accurately evaluate the preference.

In the determination of the configuration of the vector or the similarity of the vectors, the evaluation result of the evaluation target not evaluated by the second evaluator can be regarded as the average of the evaluation results of the second evaluator.

As a result, it is possible to prevent the evaluation target that has not been evaluated by the second evaluator from being judged unreasonably high or low.

The present invention also calculates the degree of vector similarity by averaging the squares of the differences between the evaluation results of the first evaluator and the second evaluator for each evaluation object in the determination of the vector similarity. be able to.

As a result, the degree of similarity between the first evaluator and the second evaluator who has the exact evaluation can be further estimated, and more accurate taste evaluation can be performed.

According to the present invention, the difference between the average values of the evaluation results of the first evaluator's evaluation result set and the second evaluator's evaluation result set is not reflected as a difference in the determination of the similarity of the vectors. The degree of similarity can be calculated.

As a result, it is possible to appropriately take into consideration the difference in evaluation depending on the product of a person who has a poorer evaluation in the first place.

According to the present invention, the difference in the standard deviation of the evaluation results between the evaluation result set of the first evaluator and the evaluation result set of the second evaluator is not reflected as a difference in the determination of the similarity of the vectors. The degree of similarity can be calculated.

As a result, it is possible to properly standardize the evaluation from the person who judges the difference in evaluation more violently than the person.

In the present invention, the similarity with the second evaluator who has the evaluation result for more evaluation objects can be evaluated higher in the specific step.

In the present invention, in the presenting step, the second evaluator who has the evaluation result for more evaluation objects can be preferentially presented.

With these, it is possible to encourage many shops to register evaluations.

The present invention can also show information on the degree of similarity between the evaluation result set of the similar evaluator (Xr) and the evaluation result set of the first evaluator (Xp) in the presenting step.

As a result, the person who is presented with the evaluation information is
It is possible to judge the degree of authenticity of the information in terms of one's preference.

In the present invention, each evaluation result (E (Xr, Y) of the similar evaluator (Xr) in the presenting step is also provided.
1) ,. ． ． , E (Xr, Yn)) is shown to the first evaluator (Xp), Dp is the standard deviation of the evaluation result set of the first evaluator (Xp), and Dr is the similar evaluation. (Xr) as the standard deviation of the evaluation result set, and H
p is the average of the evaluation result set of the first evaluator (Xp),
When Hr is the average of the evaluation result set of the similar evaluator (Xr), each evaluation result (E
(Xr, Y1) ,. ． ． , E (Xr, Yn)) is Hp +
(Each evaluation result-Hr) Replaced with Dp / Dr (H
p + (E (Xr, Y1) -Hr) (Dp / D
r) ,. ． ． , Hp + (E (Xr, Yn) -Hr) (D
The information obtained from p / Dr)) can be shown.

As a result, the evaluation value of another person can be converted into the own evaluation value for consideration.

In the present invention, each evaluation result (E (Xr, Y) of the similar evaluator (Xr) in the presenting step is also provided.
1) ,. ． ． , E (Xr, Yn)) is shown to the first evaluator (Xp), Dp is the standard deviation of the evaluation result set of the first evaluator (Xp), and Dri is i
The standard deviation of the evaluation result set of the second similar evaluator (Xri), Hp is the average of the evaluation result set of the first evaluator (Xp), and Hri is the i-th similar evaluator (Xr).
Srij as the average of the evaluation result set, and the deemed deviation according to the standard deviation ratio of the i-th similar evaluator (Xri) and the evaluation target (Yj) with the first evaluator (Xp), that is, (E (Xri, Yj) -Hri) Dp / D
ri and Wri is a weight obtained from the similarity to the i-th similarity evaluator (Xri) (typically, the t-th power of the similarity, 0 or more and 1 or less), the first evaluator is not Estimated evaluation of evaluation target (Yu) (Ee (Xp, Y
u)) is expressed by the formula: Hp + (Wr1 · Sr1u + Wr2 · S
r2u + ... + Wrn · Srnu) / (Wr1 + Wr
2 + ... + Wrn), where n is the number of similar evaluators, and the information obtained from the estimated evaluation can be shown.

As a result, the evaluation values of a plurality of similar evaluators (second evaluators) can be comprehensively evaluated.

The present invention is also capable of electronically performing the information indicating the evaluator of the evaluation result, which is the basis of the information shown in the presenting step, for a part or more of the payment procedure to the evaluator. It can further include passing to the payment interface.

As a result, a person who honestly evaluates can provide legitimate evaluation information to a person who has a similar taste. For example, in return, the supplier of the evaluation target (commodity etc.) provides information. It will be possible to receive incentives from those who have been treated.

As a result, the problem 2) is achieved.

The present invention also provides the first evaluator (Xp).
Shows a step of detecting whether or not the evaluation target included in the information shown in the presenting step has been used after the presenting, and, when the use is detected, indicates an evaluator of the evaluation result which is the basis of the presenting. Passing the information to a payment interface, which is capable of electronically performing some or more of the payment procedure for the evaluator.

As a result, not only when the evaluation target is presented (recommended) but also when the product is actually purchased or the service (service) is used by using the presented information, the product is evaluated. It is possible to give incentives to the evaluator of the evaluation result, which served as a medium for sales, from the supplier such as. That is, an honest evaluator can be a good advertiser. As a result, the problems 1), 2) and 3) are achieved more sufficiently.

The present invention also comprises the step of associating the validity period of each evaluation result with the evaluation result and accumulating in the storage device, and the step of deleting the corresponding evaluation result from the storage device after the expiration of each validity period. It can further be included.

The present invention further includes a step of associating the information indicating the range of the attribute of the evaluation target for which each evaluation result is valid with the evaluation result, and storing the information in the storage device. When the attribute of (4) deviates from the range of the attribute of the evaluation target, the evaluation result can be used or the weight can be reduced.

From these, stock forecasts, politician promises,
This makes it possible to apply the present invention to those having a temporal expiration date or other environmental conditions or preconditions that may change, such as snow quality of a ski resort.

The present invention may further include the step of overwriting the storage device with the updated evaluation result.

The present invention further includes a step of associating the portion of the information that is actually presented to the evaluator, the presented evaluator, and the date and time of presentation, and storing them in the storage device. For the evaluation target included in, according to the evaluation newly presented by the presented evaluator (that is, immediately after the presented date and time, etc.), to the information provider of the part,
Through the payment interface, incentive payments can be made from the submitted evaluator, the supplier being evaluated, or both.

As a result, a virtuous cycle of evaluation distribution is promoted. That is, not only can an evaluation call an evaluation, but the person who first made the appropriate evaluation can be rewarded for having made the appropriate evaluation first. Incidentally, a person who intentionally makes an inappropriate evaluation is unlikely to be rewarded because it is unlikely to be a similar evaluator (second evaluator). Therefore, the users of this method receive an incentive to honestly evaluate good products, medium products, and bad products. After all, it is expected that correct evaluation will be distributed in the world, purely good products and good services will be developed, and the world's resources will be used in a constructive direction.

The present invention also provides the above-mentioned new evaluation.
If the evaluation included in the presented portion is substantially close to the evaluation, the incentive can be increased.

As a result, a person who makes a new evaluation different from a person (generally valuable if different from a person and correct)
Can have vested interests in more effective incentives. A person with a similar rating later, instead of subsequently acquiring the right to the incentive (which would be shared with the vested interest), would have first evaluated the neighborhood (and in some cases presented information). This is because giving some kind of reciprocal reply to the person who gave me) leads to a virtuous cycle of incentives.

In the present invention, the evaluation target is identified by a telephone number (or an extension of the evaluation target by adding a branch number or the like) to the evaluation target, or a network address such as a URL related to the evaluation target. Can be identified by.

Thus, for example, if the URL of the Internet corresponding to the product is publicized or even the telephone number corresponding to the service is publicized, the target can be evaluated by the present system.

The present invention is a method, a system (information storage server, information processing server, (portable) terminal, payment interface) for implementing these, and a computer program, or a storage storing the program. It is a medium.

[0072]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 shows an example of the configuration of a system according to the present invention (this is merely an example, and it is possible to replace other components, add components, or combine components together within the scope of the invention. ) Is shown. The realization of each unit is sufficiently possible for those skilled in the field of computer / communication systems (system engineers, programmers, etc.) with reference to the above description in this specification. The control, communication, statistics, calculation formulas, and formatting included in this method are realized by applying a typical programming language (C language, Perl, etc.) or known middleware. You may. Regarding storage, it may be resident in the main storage, stored in a file format, or a relational database or the like may be used. Further, the processor and the memory may be appropriately arranged in a distributed environment. Communication can be realized using the Internet, a private network, or a normal data network such as a VPN in between. The payment interface may be implemented, for example, as a gateway to a (net) bank online system, a simpler protocol may be used, or a human interaction may be used as a written interaction.

FIG. 2 shows a flow chart of the most important part of the present invention. Both the data structure and the algorithm can be specifically implemented.

3 to 5 show examples of data structures when the storage device is realized as a relational database. For example, this could be a relational schema of storage by a relational database.

[0075]

EXAMPLE An example of operation will be described below.

Company K provides an evaluation distribution service. The server PC of the server center of Company K is connected to the user's Internet terminal and mobile terminal via the Internet, and stores evaluations of various products and services in the hard disk of the PC.

The storage method is simple, and the URL of the WWW where the description of the product is displayed or the telephone number of the shop providing the service + menu name and their evaluation (1 to 100 points)
And a comment (if any) and a character or a signal indicating the expiration date (if any) are used as appropriate for the questionnaire screen of the home page, the standard e-mail, or the tone signal of the telephone (or CTI, or voice recognition technology). )
Etc., the evaluator is identified from the sender's IP address, cookie information, mail address, telephone number, etc., and these are stored in association with the date of reception.
The contents are as follows, for example.

[Evaluation result ID: 00000351; Registration date: 20010101; Evaluation target: 03-5840-7
888 + 03 (Miso Ramen); Rating: 95 points; Comment: The best noodles; Expiration date 20100101-20020
101; Evaluator: 002129 (keisuke@aaa.knowrel.
com) ".

In addition, this evaluator also registered his own information in the same manner as described below (stored in the server PC).

[Evaluator ID: 002129; Registration date: 2
0000902; Name of evaluator: Keiemon Nakamura; Category:
Male / Bunkyo-ku, Tokyo / Age 32 / Married; Phone number: 03-
5841-7777; Email: keisuke@aaa.knowrel.co
m; account number: 123-45678 ".

By the way, the ramen shop evaluated by this man had the following evaluation targets registered in the same manner from the mobile terminal of the owner of the ramen shop.

[Evaluation target ID: 055553; Registration date: 19990501; Name: Mochimochi miso ramen:
Identification code: 03-5840-7888 + 03; Category: Food>Ramen> Miso / Bunkyo-ku, Tokyo; Contact: 0
3-5840-7888; Account number: 987-6543
2 ”.

The above-mentioned evaluator (ID: 002129) is
Evaluations were also accumulated for ramen at ramen shops other than this ramen shop. It was my hobby to rush to eat a new ramen shop and register the honest evaluation.

On the other hand, in the database of the PC, a plurality of malicious evaluators (for example, ID: 0066) who register the fictitious evaluation by receiving money from a ramen shop who is not confident in taste.
69) registered various evaluations different from his honest evaluation.

One day, a registered evaluator (ID: 00213) who does not know much about a new ramen shop
0) requested the information (evaluation) provided by the present invention by designating the category (attribute) of ramen shop in Bunkyo Ward.

This evaluator (ID: 002130) usually makes an honest evaluation, and his taste was somewhat similar to that of the evaluator (ID: 002129). Therefore, these two evaluators registered in the PC. Although the evaluation result sets were slightly different, they were generally similar. on the other hand,
The evaluation result set by the malicious evaluator who did not evaluate honestly was far from the evaluation result set by this evaluator (ID: 002130). Therefore, when a similar evaluator is searched for in the PC in response to the information provision request, the evaluator (ID: 006669) who is in good faith and has a similar taste (ID:
002129) was identified, and a valid evaluation by this well-meaning evaluator was presented to the evaluator (ID: 002130).

Further, in this case, the evaluator (ID: 002
By specifying 130), the evaluators having similar evaluations on the evaluation target including “food” as a category are specified (“designation of similar reference category”). This is because the emphasis is placed on the similarity of the taste of food.

Further, by designating the evaluator (ID: 002130), the evaluator including "male" as the category is specified. At least this person wanted a male rating (“designating a similar rater category”).

Also, this evaluator (ID: 002130)
Was requested to evaluate the evaluation target including “ramen” as the category (“Specify evaluation target category”).

In these requests, the above-mentioned information regarding the category in the PC was utilized (for example, character string matching, or combined use with another synonym / classification database).

When the “designation of similar reference category” is omitted, the category (eg ramen) designated in “designation of category to be evaluated” or a superordinate concept (eg food) up to an arbitrary floor It also had an optional mode of automatically using as a "similarity reference category", and it was a convenient system that could omit designated input.

Also, in this example, the evaluator ID: 00213
0 wanted to go to a new store, so he requested information about the evaluation target that he had not yet evaluated. Since it is easy to determine whether or not this self (that is, the evaluator who requested this information: ID002130) is evaluating from the data in the relational database having the above-mentioned data structure, in this case, a similar evaluator. After ID002129 is identified as
ID0 among the evaluation targets registered by 002129
Only the evaluation targets and evaluations that 02130 has not yet evaluated are displayed. Therefore, ID002129's just-registered ramen shop's evaluation of miso ramen and the phone number of that ramen shop are immediately ID0.
It became known to 02130.

Further, in this example, the setting is made so that only the highly evaluated objects are presented (for example, displayed on the display of the mobile phone). The fact that a computer can automatically sort from stored data based on these criteria is undoubtedly obvious with the use of relational database SQL and the like. In this case, ID: 002129
Of the evaluation targets registered as a result of visiting new stores, only those with a high evaluation by ID: 002129 are displayed, and ID: 002130 shows only the stores that have a high possibility of being delicious and have a taste. You can choose to visit. Obviously, it is possible to register the location and the way to provide the evaluation target, and this information can be used as appropriate.

In addition, when it is not possible to incorporate some shops into the schedule of one day in advance, it is necessary to know only "shops that should be avoided". In such a case, "evaluation is similar to the above" It is possible to set (or specify) that only "low" stores are selected and presented.

The comparison between the set of evaluation results by ID: 002130 and the set of evaluation results by each other evaluator is performed, for example, between vectors in which each evaluation target is a coordinate axis and each evaluation result is each corresponding component. It is also possible to make a comparison. For example, the evaluation result of ID: 002130 is (Ramen A, Ramen B, Ramen C) = (3
0 point, 60 point, 40 point), and the evaluation result of ID: 002129 is (Ramen A, Ramen B, Ramen C) =
In the case of (30 points, 60 points, 35 points), it is similar as a vector, and further malicious ID: 006669
Evaluation results of (Ramen A, Ramen B, Ramen C)
= (70 points, 20 points, 80 points), it can be determined that the vectors are considerably different.
Vectors can be compared with each other by various mathematically established methods (focusing on the size, angle, deviation of each component, etc.)
It can be realized by using a computer.

In the comparison between the vectors in this example,
If the ID: 002130 has not yet evaluated the ramen B, the coordinate axis of the ramen B can be ignored so that this component is not the target of vector comparison.

Similarly, although ID: 002130 has already been registered for evaluation, ID: 002129 and ID: 006669 are registered.
Vectors can be compared by ignoring the coordinate axes of the evaluation target (such as ramen in this case) that has not yet been registered for evaluation.

Further, when the evaluation results which are the components of the coordinate axis are missing, it is possible to handle them as the average of other evaluation results by the evaluator for the time being.

Further, when comparing evaluation result sets between a person who is generally astringent in evaluation and a person who is generally weak in evaluation, it is possible not to reflect the level of distribution of the evaluation results in the comparison judgment. Is. If you want to focus on the relative deviation of the evaluation results of each evaluation target by the same evaluator, you can slide the distribution up and down and compare it with the evaluation results of other evaluators using the same computer as other processing. Can be processed automatically.

Also, when comparing a person with a radically different evaluation and a person with a milder evaluation difference, the absolute evaluation value (value) of each evaluator is not focused on the absolute value. From this, it is possible to judge how the evaluation result of each evaluation object is different from the evaluation results of other evaluation objects. For example, the evaluation result of ID: 002130 is (Ramen A, Ramen B, Ramen C) =
(30 points, 50 points, 70 points) and ID: 00212
When the evaluation result of 9 is (Ramen A, Ramen B, Ramen C) = (40 points, 50 points, 60 points), the average of both evaluation results is 50, but ID: 00213
A rating of 0 is moderately extreme and a rating of the other is moderate. Comparing this by focusing on only the absolute value, it can be seen that they are quite different evaluation result sets, but when the deviation from the average is calculated, they are (-20 points, 0, +2
0 points), (-10 points, 0, +10 points). Dividing the former deviation by 2 gives the same deviation. Such standardization can be appropriately and automatically performed by automatically calculating the standard deviation and the like.

The evaluator ID: 002128 is ID:
If the same evaluation as that of 002129 is performed for each evaluation target (that is, the similarity of the evaluation result set is almost the same), ID: 002129 is more likely to open a new ramen shop and register the evaluation. : 00
There should be more registration data from 2129. In such a case, in response to the information request from ID: 002130, I
D: Fine adjustment of the similarity calculation method so that it becomes more similar to 002129 (for example, the normal similarity is multiplied by the ratio of the number of evaluated evaluation objects to the number of evaluable evaluation objects). (As a result, it is easy to be identified as a similar evaluator), or the evaluation result of ID: 002129 can be automatically presented preferentially (displayed on the first line or page in the display of the presentation). It is possible.

In the presenting step, to what extent the evaluation result sets of the similar evaluator (eg, ID: 002129) whose evaluation result is presented and the evaluator (eg, ID: 002130) who requested the information are similar. It is also possible to indicate that The value obtained in the process of calculating the similarity may be stored in association with each evaluator, and may be displayed together when the evaluation result of the evaluator is presented.

When a plurality of similar evaluators are specified, they can be presented in a table format in order (for example, by scrolling on the display of a mobile phone). It is possible to present a single integrated value (estimated evaluation) obtained by weighting the respective evaluation results for the evaluation target of 1. This can be done, for example, by implementing the definition of the calculation formula according to claim 24 on a computer. As an example, ID:
The evaluation result of 002130 is (Ramen A, Ramen B, Ramen C) = (30 points, 50 points, none),
Regarding the evaluation results of the two similar evaluators, ID: 00
The evaluation result of 2129 is (40 points, 50 points, 90 points), I
The evaluation result of D: 002128 is (10 points, 50 points, 60 points
Points), the average of the three points is 40 points,
60 points, 40 points, variance 100, 467, 46 respectively
6 and standard deviations are 10, 21 and 21, respectively (all are approximate numbers). If the distance between vectors in a sphere (circle in this case) obtained by associating 100 points with a radius of 1.0 is d and the similarity is calculated by 1-d, I
The similarity of D: 002130 with ID: 002129 is
1-√ ((0.3-0.4) ^ 2 + (0.5-0.5)
^ 2) = 0.9, while the similarity with ID: 002128 is 1-√ ((0.3-0.1) ^ 2 + (0.5-0.5)
2) = 0.8. Each weight is the first power of the similarity (t =
1: Refer to the claim), the total weight is 0.9+
It becomes 1.7 at 0.8. At this time, the estimated evaluation of the ramen C obtained by ID: 002130 is 40 + (((0.9
(90-60) ・ 10/21) + (0.8 ・ (60-4
0) ・ 10/21)) / (0.9 + 0.8)) = about 52
Becomes This seems low at first glance, but ID: 002
Considering that the average of 130 is 40 points, it can be said that this is a high evaluation, and it reflects a high evaluation by two other similar evaluators. Even if there is only one similar evaluator, it can be automatically calculated by a computer.

From the above description, it is clear that the evaluator who has contributed to the presentation of the evaluation, for example, ID: 002129, can be identified, and when there are two or more similar evaluators, all of them are identified together with the weight (similarity). can do. As a result of presenting these registered information, it is clear that the provider to be evaluated can expect a certain advertising effect, and the provider (or the evaluator who received the evaluation in some cases (ID: in this example: It can be designed to automatically transfer (pay) money (depending on weight) as a reward (or incentive) from the account of (002130)) to the account of the similar evaluator. This does not necessarily have to be official money, and it is clear that the same composition can be made with private (lending and borrowing) points. Appropriate decision functions can be programmed for moving amounts. Alternatively, it may be flatly set at ¥ 1 or ¥ 10, which may be a fixed percentage according to actual usage, an upper limit may be set, or a new evaluation registration after usage may be used as an opportunity to set the amount according to the usage amount. , May be the amount according to the “new evaluation”. The detection of whether or not the usage has been reached may be performed by designing the system in a manner that requires the cooperation of the provider of the evaluation target used (manual input or installation of an automatic detector), various credit cards and prepaid cards. It may be realized by linking with the usage record of a card or the like. The computer (that is, the information processing means in the computer) issues a command of a certain format to the related systems regarding the movement of money or points itself, or the record of temporary movement schedule for collective movement (such as settlement at the end of a period). The mechanism for requesting interlocking may be in accordance with existing standardized technology.

Further, it is possible to provide a validity condition for the registered evaluation result and a matching condition of an attribute (category), which is used as an automatic start program such as Coulomb (see the reference of UNIX (registered trademark) system). Correlate them via SQL etc.) so that they will not be deleted and presented after the effective period of use, will not be used to identify similar evaluators, or will not be deleted but will be subject to a certain aging technique (depending on time). It is possible to reduce the influence by reducing the weight). The attributes to be evaluated (category, eg address) may change,
The provider to be evaluated can change (update) at any time by the same method as the registration. Due to this change, the evaluation results that were originally used (for presentation or for identifying similar evaluators) may no longer be used.

Detection of whether or not the system has been used due to the presentation of the evaluation by this system is possible, for example, by accumulating the presentation history of the evaluation result in addition to the accumulation of the evaluation result. The causal relationship can also be detected based on the closeness of the date of presentation in the presentation history and the registration date of the evaluation result for the evaluation target presented by the evaluator presented. By a computer, for example, when the dates are close (or the same), it is determined that there is a causal relationship, and it is determined from the cash account (or other property or loan management record) of the provider to be evaluated and / or the evaluator who has been presented. It is possible to design some amount (incentive) to be transferred to the evaluation provider. Further, it is possible to appropriately design so that some percentage of these amounts are transferred to the account of the operator of this evaluation distribution service. As a result, the operator of the evaluation distribution service can concentrate on the operation, research, and development of this system, and can quickly develop a system that is more useful to the public welfare.

Further, (there is a causal relationship with the presentation (or it is possible to eliminate this causal relationship depending on the policy of the operator, and in that case, an original effect can be expected)).
An evaluator who has registered a new evaluation will receive evaluators who have already registered similar evaluation results for the same evaluation target.
Design the system to pay the place fee (in addition to the incentive or separately from the incentive) according to the similarity (specifically, programming)
can do. By doing so, it is expected that more people will try to make an evaluation more quickly and more appropriately than others. As a system, at the timing after a new evaluation is registered (however, incentive payment can be made only to the "evaluator who contributed to the presentation of information" only when a causal relationship can be detected) , Launches the so-called "place fee" payment routine,
At that time, the evaluation target of the newly registered evaluation is substantially the same (or it may be designed to include the case where the category or the providers are similar) and the evaluation is similar, for example, a point difference of 10 points. For the following (10-point difference)
Place payments in Japanese Yen can be designed to be done between raters (of course more decision functions need not be linear). If there are multiple existing evaluators with similar evaluations, it is possible to refer to the evaluation result registration date (see above) and pay for the one with the earliest evaluation registered. You can choose to weight things and pay only to those raters with the same exact rating. This date order may be moved up according to the deletion or aging due to the combination with the use expiration date mechanism of the evaluation described above. Deletion and aging at this time can be done by the system internally (however, the system is announced) and forced (for example, after one year) or by aging (not deleted by the evaluator's preference). It is also possible as a system to send a reminder e-mail for new evaluation, etc.). This makes it impossible for a person who registers a valid evaluation earlier than a person to continue to be in that position,
It is possible to re-evaluate the evaluation target at a certain frequency. Most typically, the evaluation result for a certain evaluation target of a certain evaluator once registered without the effective use period, category condition, and aging specification is not deleted or aged until the evaluator updates it. However, one year later, the system is designed so that only the status due to the previous registration (getting the "place fee" above) will be invalid (however, the program will restore the status when registering or updating again) (Specifically, update the registration date field in the evaluation result record)). Specifically, the evaluation result one year after the registration (a record based on the schema of FIG. 5 inside the computer, as a specific example, the schema is slightly different, the above-mentioned “evaluation result ID: 0000035”
1; Registration date: 20010101; Evaluation target: 03-58
40-7888 + 03 (Miso Ramen); Rating: 95
Point: Comment: Noodles are the highest; Expiration date 20100101-
20020101; Evaluator: 002129 (keisuke @ aa
a.knowrel.com) ”) may be programmed such as SQL so as to be excluded (based on the registration date information) from the conditional expression for searching for a partner (that is, a record) who pays the place fee.

[0108] In addition, the evaluation target provider (of course, the evaluation target for the evaluation target not provided by himself at the same time) has not registered detailed information, but the evaluation target other evaluation target If you dare to register the evaluation, you can register the evaluation by identifying the evaluation target with the network address such as the telephone number or URL of the evaluation target, and in the end, the reward from the provider (incentive)
Regarding the mechanism other than the payment of the above, it can be configured to operate in the same manner as when the provider registers and authorizes detailed information. As a result, the evaluation information can be distributed more easily.

[0109]

As described above, according to the present invention,
An evaluator (Xp) who honestly shows his / her own evaluation of some products in the world can obtain an evaluation result that also reflects the taste of another person (Xr) who has also made an honest evaluation. . In particular, it is possible to avoid the malicious presentation of the evaluation.

Further, for example, information from persons who are similar in preference only to the evaluation objects related to the situation at that time is not obtained, instead of seeking persons who have similar tastes based on the preferences regarding all the evaluation objects. Can be obtained efficiently.

Further, in consideration of the preference, the category or the like to be evaluated according to the situation can be designated in a multifaceted manner or efficiently.

Even if evaluation profiles are similar to each other, information can be efficiently provided even when there are evaluators who do not want to be referred to, or a group of evaluators who want to refer to, for example, depending on age group, occupation, and residential area. Can be obtained.

Also, at that time, the evaluation "person" to be referred to
The category of can be specified in multiple ways or efficiently.

Also, by limiting the type of information presented,
You can browse efficiently.

Further, restrictions on the type of information presented are
It can be done in multiple ways or efficiently.

Further, it is possible to automatically consider or easily specify the preference for the evaluation target related to the evaluation target for which information is requested.

Further, when some products / services to be purchased or used are concretely determined, it is possible to request an evaluation only for the evaluation target.

Further, the preference for the evaluation object related to the evaluation object for which information is requested can be specified more specifically, automatically or simply.

Further, when selecting a store, it is possible to efficiently and quickly consider only the information about the highly evaluated objects.

Further, it is possible to carry out shopping while avoiding a store or the like which should not be obviously used.

Also, the similarity of evaluation can be calculated at high speed,
The pruning of the candidate set of similar evaluators can be efficiently performed.

The first evaluator with a small number of evaluation results (those who have requested the presentation of information on the evaluation results by other evaluators)
The evaluation results of can be fully utilized.

Further, it is possible to highly estimate the degree of similarity between the first evaluator and the second evaluator who has an exact evaluation,
Accurate taste evaluation is also possible.

Further, it is possible to prevent the evaluation object which is not evaluated by the second evaluator from being judged unreasonably high or low.

Further, the degree of similarity between the first evaluator and the second evaluator who has a perfect evaluation can be further estimated, and more accurate preference evaluation can be performed.

Further, it is possible to properly consider the difference in evaluation depending on the product of a person who has a weaker evaluation than a person or a sweet person.

Further, it is possible to properly standardize the evaluation from the person who judges the difference in evaluation more vigorously or gently than the person.

Further, it is possible to promote that many shops visit various shops and register evaluations.

Further, the person who is presented with the evaluation information can judge the degree of credibility of the information in terms of his / her preference.

Further, the evaluation value of another person can be converted into the own evaluation value for consideration.

A plurality of similar evaluators (second evaluator)
The evaluation value of can be comprehensively evaluated.

Further, an honest evaluation person can provide legitimate evaluation information to a person with similar taste. In return, for example, the supplier of the evaluation object (product etc.) is provided with the information. It will be possible to receive incentives from those who do. In addition, not only when the evaluation target is presented (recommended) but also when the product is actually purchased or the service (service) is used by using the presented information, the supplier of the product etc. Therefore, it becomes possible to give an incentive to the evaluator of the evaluation result, which is a medium of sales. That is, an honest evaluator can be a good advertiser.

[0133] Also, an efficient distribution of evaluations regarding stock forecasts, politician commitments, snow quality at ski resorts, time expiration dates, and other variable environmental conditions and preconditions. Be peeled off.

Further, a virtuous cycle of evaluation distribution is promoted. That is, not only can an evaluation call an evaluation, but the person who first made the appropriate evaluation can be rewarded for having made the appropriate evaluation first. After all, it is expected that correct evaluation will be distributed in the world, purely good products and good services will be developed, and the world's resources will be used in a constructive direction.

Also, a person who makes a new evaluation different from a person can have a vested right for a more effective incentive.

In addition, the U of the Internet corresponding to the product
If the RL is publicized or even the telephone number corresponding to the service is publicized, the target can be evaluated by the system.

[Brief description of drawings]

FIG. 1 is a schematic diagram of a configuration example of a system according to the present invention.

FIG. 2 is a flow chart showing the main part of the present invention.

FIG. 3 is a diagram showing an example of a data structure of evaluation target information when a storage device is realized as a relational database.

FIG. 4 is a diagram showing an example of a data structure of evaluator information when a storage device is realized as a relational database.

FIG. 5 is a diagram showing an example of a data structure of evaluation result information when a storage device is realized as a relational database.

[Explanation of symbols]

1 Evaluation distribution server 2 Information processing means 3 storage means

─────────────────────────────────────────────────── ───

[Procedure amendment]

[Submission date] June 25, 2001 (2001.6.2)
5)

[Procedure Amendment 1]

[Document name to be amended] Statement

[Correction target item name] 0103

[Correction method] Change

[Correction content]

When a plurality of similar evaluators are specified, they can be presented in a table format in order (for example, by scrolling on the display of a mobile phone). It is possible to present a single integrated value (estimated evaluation) obtained by weighting the respective evaluation results for the evaluation target of 1. This can be done, for example, by implementing the definition of the calculation formula according to claim 24 on a computer. As an example, ID:
The evaluation result of 002130 is (Ramen A, Ramen B, Ramen C) = (30 points, 50 points, none),
Regarding the evaluation results of the two similar evaluators, ID: 00
The evaluation result of 2129 is (40 points, 50 points, 90 points), I
The evaluation result of D: 002128 is (10 points, 50 points, 60 points
Points), the average of the three points is 40 points,
60 points, 40 points, variance 100, 467, 46 respectively
7 and standard deviations are 10, 21 and 21, respectively (all are approximate numbers). Also, as an example, the maximum score of 10 is 10 for each evaluation.
When similarity is calculated by 1-d, where d is the distance between the position vectors of the multidimensional space obtained by associating 0 points with 1.0, (here, for simplification, consideration is given to the case where this becomes negative. Although it has not been performed, for example, in this example, since it is a two-dimensional vector, it is possible to prevent it from becoming negative by explicitly setting 1-d / √2.), ID: 00213
0 is similar to ID: 002129, which is 1−√ ((0.
3-0.4) ^ 2 + (0.5-0.5) ^ 2) = 0.
9, the similarity with ID: 002128 is 1-√
((0.3-0.1) ^ 2 + (0.5-0.5) ^ 2)
= 0.8. When each weight is the first power of the similarity (t = 1: see the claims), the total weight is 0.9 + 0.8, which is 1.7. At this time, the estimated evaluation of the ramen C obtained by ID: 002130 is 40 + (((0.9. (90-
60) / 10/21) + (0.8 ・ (60-40) ・ 1
0/21)) / (0.9 + 0.8)) = about 52.
This seems to be low at first glance, but it can be said that this is a high evaluation considering that the average of ID: 002130 is 40 points, and reflects a high evaluation by two other similar evaluators. Even if there is only one similar evaluator, it can be automatically calculated by a computer.

Claims (39)

[Claims] 1. A method of distributing an evaluation of an evaluation object such as a product or service mainly by computer control, storage, and statistical processing, which is evaluation object information indicating an evaluation object (Y1, ..., Yn). And the evaluator information (X
1 ,. ． ． , Xm) is associated with the evaluation result information indicating the evaluation result (E (Xi, Yj)) by the evaluator to be evaluated and accumulated in the storage device in a computer-readable format, and the first evaluator (Xp ) Each evaluation target (Y
1 ,. ． ． , Yn) of each evaluation result set (E (X
p, Y1) ,. ． ． , E (Xp, Yn)) is a set of evaluation results (E (Xq, Y1), ..., E () for each evaluation target (Y1, ..., Yn) of the second evaluator (Xq). X
q, Yn)), a second specific evaluator (Xq), which is one or more similar evaluators (Xr), specified by a computer from the information accumulated in the storage device, and the similar evaluator. Each evaluation result of (Xr) (E (Xr, Y
1) ,. ． ． , E (Xr, Yn)) to present the information to the first evaluator (Xp). 2. The evaluation target (Y1, ..., Yn) serving as the similar reference in the specific step is limited in accordance with the selection of the first evaluator (Xp) and the like. The evaluation distribution method according to claim 1. 3. The method further comprises the step of storing information indicating an attribute of each evaluation target in a storage device in a computer readable format.
Using this information, the evaluation target (Y
1 ,. ． ． , Yn) are limited, The evaluation distribution method of Claim 2 characterized by the above-mentioned. 4. The second evaluators (X1, ..., Xm) to be the similar target in the specific step are limited in accordance with the selection of the first evaluator (Xp) and the like. The evaluation distribution method according to any one of claims 1 to 3, which is characterized. 5. The method further comprises a step of storing information indicating attributes of each evaluator in a storage device in a computer-readable format, and using the information, the second evaluator (X
1 ,. ． ． , Xm) are limited. The evaluation distribution method according to claim 4, wherein 6. The evaluation distribution according to claim 1, wherein the information shown in the presenting step is limited according to the selection of the first evaluator (Xp) and the like. Method. 7. The method further comprises the step of storing information indicating an attribute of each evaluation target in a storage device in a computer-readable format.
The evaluation distribution method according to claim 6, wherein the evaluation target (Y1, ..., Yn) that is a target of information in the presentation step is limited using the information. 8. The attribute of the evaluation target used to limit the evaluation target (Y1, ..., Yn) that is the target for showing information in the presenting step, and the evaluation serving as a similar reference in the specifying step. The evaluation distribution method according to claim 3 or claim 7, which is also used for limiting an object. 9. The evaluation distribution according to claim 6, wherein the evaluation target (Y1, ..., Yn) that is a target for indicating information in the presenting step is limited to a specific evaluation target (Ys). Method. 10. The attribute of the limited specific evaluation target (Ys), which is a target of information in the presenting step, is also used to limit an evaluation target that is a similar reference in the specific step. The evaluation distribution method according to claim 3 or claim 9, which is characterized. 11. The evaluation target (Y1, ..., Yn), which is a target for indicating information in the presenting step, is limited to an evaluation target that has not been evaluated by the first evaluator (Xi). Item 11. The evaluation distribution method according to any one of Items 1 to 10. 12. The evaluation target (Y1, ..., Yn), which is a target of information in the presenting step, is limited to information about an evaluation target having a high evaluation result by the similar evaluator (Xr). 11. The evaluation distribution method according to any one of 1 to 11. 13. The evaluation target (Y1, ..., Yn), which is a target of information in the presenting step, is limited to information about an evaluation target having a low evaluation result by the similar evaluator (Xr). 11. The evaluation distribution method according to any one of 1 to 11. 14. The similarity in the identification step is determined by each evaluation target (Y) of the first evaluator (Xp).
1 ,. ． ． , Yn) of each evaluation result set (E (X
p, Y1) ,. ． ． , E (Xp, Yn)) and each evaluation result set (E (Xq, Y1), ..., E for each evaluation target (Y1, ..., Yn) of the second evaluator (Xq).
The evaluation distribution method according to any one of claims 1 to 13, wherein the evaluation distribution method is determined by similarity with (Xq, Yn) as a vector configured by each evaluation result as each component. 15. The evaluation distribution method according to claim 14, wherein the evaluation result of an evaluation target that has not been evaluated by the first evaluator is not considered in the determination of the configuration of the vector or the similarity of the vectors. 16. The evaluation distribution method according to claim 14, wherein the evaluation result of an evaluation target that has not been evaluated by the second evaluator is not considered in the determination of the configuration of the vector or the similarity of the vectors. 17. The evaluation result of an evaluation object not evaluated by the second evaluator is regarded as the average of the evaluation results of the second evaluator in the determination of the composition of the vector or the similarity of the vectors. The evaluation distribution method according to claim 14. 18. In the determination of the similarity of the vectors,
18. The similarity is calculated so that the difference between the average values of the evaluation results of the evaluation result set of the first evaluator and the evaluation result set of the second evaluator is not reflected as a difference. The evaluation distribution method described in any one of items. 19. In the determination of the similarity of the vectors,
15. The similarity is calculated so that a difference in standard deviation of evaluation results between the first evaluator evaluation result set and the second evaluator evaluation result set is not reflected as a difference.
The evaluation distribution method according to any one of 1 to 18. 20. In the specifying step, the similarity with the second evaluator having the evaluation result for more evaluation objects is evaluated higher. The evaluation distribution method described in the item. 21. In the presenting step, the second evaluator having an evaluation result for more evaluation objects is preferentially presented.
The evaluation distribution method according to any one of 0. 22. The information regarding the degree of similarity between the evaluation result set of the similar evaluator (Xr) and the evaluation result set of the first evaluator (Xp) is also shown in the presenting step. 21. The evaluation distribution method according to any one of 21. 23. In the presenting step, each evaluation result (E (Xr, Y
1) ,. ． ． , E (Xr, Yn)) is shown to the first evaluator (Xp), Dp is the standard deviation of the evaluation result set of the first evaluator (Xp), and Dr is the similar evaluation. (Xr) as the standard deviation of the evaluation result set, and H
p is the average of the evaluation result set of the first evaluator (Xp),
When Hr is the average of the evaluation result set of the similar evaluator (Xr), each evaluation result (E
(Xr, Y1) ,. ． ． , E (Xr, Yn)) is Hp +
(Each evaluation result-Hr) Replaced with Dp / Dr (H
p + (E (Xr, Y1) -Hr) (Dp / D
r) ,. ． ． , Hp + (E (Xr, Yn) -Hr) (D
The evaluation distribution method according to any one of claims 1 to 22, characterized in that it indicates information obtained from p / Dr)). 24. In the presenting step, each evaluation result (E (Xr, Y
1) ,. ． ． , E (Xr, Yn)) is shown to the first evaluator (Xp), Dp is the standard deviation of the evaluation result set of the first evaluator (Xp), and Dri is the i-th Let Hp be the standard deviation of the evaluation result set of the similar evaluator (Xri), and Hp be the first evaluator (X
p) is the average of the evaluation result set, Hri is the average of the evaluation result set of the i-th similar evaluator (Xr), and Sri
j is the evaluation target (Y) of the i-th similar evaluator (Xri)
Regarding j), the deemed deviation corresponding to the standard deviation ratio with the first evaluator (Xp), that is, (E (Xri, Y
j) -Hri) Dp / Dri, and Wri is a weight derived from the similarity with the i-th similarity evaluator (Xri) (typically, the t-th power of the similarity, 0 or more and 1 or less), The first evaluator calculates the estimated evaluation (Ee (Xp, Yu)) of the evaluation target (Yu) that has not been evaluated by the formula: Hp + (Wr1.
Sr1u + Wr2 ・ Sr2u + ... + Wrn ・ Srn
u) / (Wr1 + Wr2 + ... + Wrn), where n
Is the number of similar evaluators, and indicates information obtained from the estimated evaluation.
The evaluation distribution method according to any one of 2. 25. Information indicating an evaluator of an evaluation result, which is a basis of the information shown in the presenting step, is electronically performed on a part or more of a payment procedure for the evaluator. The evaluation distribution method according to any one of claims 1 to 24, further comprising a passing step. 26. A step of detecting whether or not the first evaluator (Xp) has used the evaluation target included in the information shown in the presenting step after the presenting, and when the use is detected, And a step of passing information indicating an evaluator of the evaluation result, which is the basis of the presentation, to a payment interface which is capable of electronically performing a part or more of a payment procedure for the evaluator. The evaluation distribution method according to any one of claims 1 to 25. 27. The method further comprising: linking the validity period of each evaluation result with the evaluation result and storing the result in the storage device; and deleting the corresponding assessment result from the storage device after the expiration of each validity period. The evaluation distribution method according to any one of claims 1 to 26. 28. The method further comprises the step of accumulating, in the storage device, information indicating the range of the attribute of the evaluation target for which each evaluation result is valid, in association with the evaluation result. 3. If the evaluation result deviates from the range of the attribute, the evaluation result is not used or the weight is reduced and used.
7. The evaluation distribution method according to any one of 7. 29. The evaluation distribution method according to claim 1, further comprising a step of overwriting an updated evaluation result in the storage device. 30. The method further includes the step of associating the portion of the information that is actually presented to the evaluator, the presented evaluator, and the presented date and time in the storage device in association with each other.
With respect to the evaluation target included in the part, according to the evaluation newly presented by the evaluator presented, the information provider of the part
30. The evaluation according to any one of claims 1 to 29, wherein incentive payment is made from the presented evaluator, the supplier to be evaluated, or both via the payment interface. Distribution method. 31. The evaluation distribution method according to claim 30, wherein if the newly shown evaluation is substantially close to the evaluation included in the presented portion, the incentive is increased. 32. The evaluation distribution method according to claim 1, wherein the evaluation target is identified by at least a telephone number associated with the evaluation target. 33. The evaluation distribution method according to claim 1, wherein the evaluation target is identified by at least a network address such as a URL, which is associated with the evaluation target. 34. An information storage server that stores the information according to any one of claims 1 to 29. 35. An information processing server that performs the process according to claim 1. Description: 36. A terminal for inputting, processing, or displaying the information according to any one of claims 1 to 29. 37. A payment interface according to claim 26 or 27. 38. Claims 1 to 29 for one or more computers
A computer program that causes the steps described in any one of 1. 39. Claims 1 to 29 for one or more computers
A storage medium storing a computer program for executing the step described in any one of 1.