JP2011134041A - Evaluation disclosure system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To increase the objectiveness of evaluation by disclosing the evaluation of merchandise by using a correction value eliminating any influence due to the tendency of evaluation of an evaluator. <P>SOLUTION: In an evaluation disclosure system, the average of an evaluation value group is calculated for each user, and each evaluation value is divided by the average, and the quotient is defined as each correction value. On a merchandise page, merchandise images, merchandise names and prices are set as initial information. Also, a merchandise-categorized evaluation value average, a merchandise-categorized correction value average, and further the evaluator name, evaluation value, correction value and evaluation comment of each evaluator, are set by processing of a merchandise page generation part. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a system for disclosing standardized evaluation values of product users.

  Patent Document 1 (product information presentation method and apparatus) discloses a technique that “a user can refer to a third party's evaluation and can increase the degree of freedom as a reference when determining a product”. Yes. In particular, FIG. 6 shows an example in which the user evaluation for each user is displayed by the number of marks. As described above, there is a system that uses a user's evaluation value as product information.

  However, even if the evaluation value range (natural number of 1 to 5) is common as in the five stages, the evaluation value varies depending on the evaluator, so the absolute value may not always be helpful. For example, a dry evaluator “3” and a sweet evaluator “3” have different evaluation weights. In other words, evaluation values with different evaluators may not be suitable for relative comparison.

JP 2002-133264 A

  In this invention, it aims at improving the objectivity of evaluation by disclosing evaluation of goods using the correction value which excluded the influence by the evaluation tendency of an evaluator.

The evaluation disclosure server according to the present invention is:
An evaluation disclosure server that can be connected to a user terminal of a user who evaluates an evaluation target via the Internet, and has the following elements: (1) Evaluation including an evaluation value for the evaluation target from the user terminal Evaluation information receiving unit that receives information (2) For each user, Evaluation information storage unit that stores the received evaluation information in association with an evaluation target (3) For each user, the evaluation value group included in the evaluation information is a population A correction value calculation unit for each user that calculates a correction value for correcting the evaluation characteristic for each user according to the average value and stores the correction value in association with the evaluation target in the evaluation information storage unit.

Furthermore,
(4) An evaluation target page generation unit that reads a correction value corresponding to the evaluation target and generates an evaluation target page that displays the read correction value;
(5) an evaluation target page storage unit that stores the generated evaluation target page;
(6) an evaluation target page request receiving unit that receives an evaluation target page request from a user terminal;
(7) It has an evaluation target page transmission unit that reads out the requested evaluation target page from the evaluation target page storage unit and returns the page.

Furthermore,
The user-specific correction value calculation unit calculates an average of evaluation value groups for each user, divides each evaluation value by the average, and uses a quotient as each correction value.

Furthermore,
The user-specific correction value calculation unit calculates the average and standard deviation of the population using the evaluation value evaluated by the user for each user as a variable, and calculates the correction value by normalization for conversion to a predetermined average and variance population. It is characterized by that.

Furthermore,
The evaluation disclosure server further includes a correction value average calculation unit for each evaluation object that calculates an average of correction values for each evaluation object,
The evaluation target page generation unit includes the calculated evaluation target-specific correction value average in the evaluation target page.

Furthermore,
The evaluation disclosure server further includes an evaluation object-by-evaluation value average calculation unit that calculates an average of evaluation values by evaluation object,
The evaluation target page generation unit includes the calculated evaluation target average evaluation value in the evaluation target page.

Furthermore,
The user-specific correction value calculation unit includes a first correction value for calculating an average of the evaluation value group for each user, a correction value obtained by dividing the evaluation value by the average, and a mother value using the evaluation value evaluated by the user for each user as a variable. Calculate the mean and standard deviation of the population, find the second correction value calculated by normalization to convert to a predetermined mean and variance population,
The evaluation target page generation unit generates both the first correction value and the second correction value.

The evaluation disclosure method according to the present invention is as follows.
An evaluation disclosure method by an evaluation disclosure server that can be connected to a user terminal of a user who evaluates an evaluation object via the Internet, and includes the following elements: (1) Evaluation of an evaluation object from a user terminal Evaluation information receiving step for receiving evaluation information including a value (2) Evaluation information storage step for storing the received evaluation information in association with an evaluation object for each user (3) Evaluation value included in the evaluation information for each user A correction value calculation step for each user that calculates a correction value of an evaluation value according to an average value having a group as a population and stores the correction value in association with the evaluation value.

The program according to the present invention is:
A computer serving as an evaluation disclosure server connectable to a user terminal of a user who evaluates an evaluation object via the Internet executes the following procedure: (1) An evaluation value for the evaluation object is obtained from the user terminal. Evaluation information reception procedure for receiving evaluation information including (2) For each user, evaluation information storage procedure for storing the received evaluation information in association with an evaluation object (3) For each user, an evaluation value group included in the evaluation information A correction value calculation procedure for each user that calculates a correction value of an evaluation value according to an average value as a population and stores the correction value in association with the evaluation value.

  Since the correction value for correcting the evaluation characteristic for each user is used according to the variable distribution having the evaluation value group included in the evaluation information as a population, the effectiveness of comparison between evaluation values is enhanced.

  Since the product-specific correction value average (example of evaluation-target-specific correction value average) and product-specific evaluation value average (example of evaluation-target-specific evaluation value average) are included in the product page (example of evaluation target page), You can know the evaluation tendency.

FIG. 1 is a diagram illustrating a network configuration of the evaluation disclosure system. FIG. 2 is a diagram showing an outline of the operation of the evaluation disclosure system. FIG. 3 is a diagram showing a processing flow for receiving evaluation information. FIG. 4 is a diagram illustrating a configuration of an evaluation disclosure server related to reception of evaluation information. FIG. 5 is a diagram illustrating an example of the evaluation information storage unit. FIG. 6 is a diagram illustrating an example of the evaluation value matrix. FIG. 7 is a diagram illustrating an example of the correction value matrix. FIG. 8 is a diagram illustrating a configuration of an evaluation disclosure server related to evaluation value correction and product page generation. FIG. 9 is a diagram illustrating a processing flow of evaluation value correction and product page generation. FIG. 10 is a diagram illustrating a user-specific correction value calculation flow. FIG. 11 is a diagram illustrating a product-specific evaluation value average calculation processing flow. FIG. 12 is a diagram showing a product-specific correction value average calculation processing flow. FIG. 13 is a diagram illustrating an example of a product page. FIG. 14 is a diagram illustrating a configuration of an evaluation disclosure server related to product page distribution. FIG. 15 is a diagram showing a product page distribution flow. FIG. 16 is a diagram illustrating a user-specific correction value calculation flow.

Embodiment 1 FIG.
FIG. 1 is a diagram illustrating a network configuration of the evaluation disclosure system. The evaluation disclosure server 101 and the user terminal 102 are connected via the Internet. The evaluation disclosure server 101 includes a Web browser. A product will be described as an example of an evaluation target. The evaluation target may be a service, a real estate, or a work. The user terminal 102 includes a user terminal of a user as an evaluator and a user terminal of a user as a viewer.

  FIG. 2 is a diagram showing an outline of the operation of the evaluation disclosure system. When a login request is transmitted from the user terminal 102 to the evaluation disclosure server 101, the evaluation disclosure server 101 performs user authentication and logs in. When a product page request is transmitted from the user terminal 102 to the evaluation disclosure server 101 during login, the evaluation disclosure server 101 returns a product page to the user terminal 102. The product page is configured to request an evaluation page for receiving evaluation information of the product. An evaluation page request is transmitted from the user terminal 102 displaying the product page to the browser to the evaluation disclosure server 101. When the evaluation disclosure server 101 returns an evaluation page to the user terminal 102, the evaluation disclosure server 101 transmits evaluation information to the user terminal 102. Finally, the user terminal 102 transmits a logout request to the evaluation disclosure server 101 and logs out.

  Hereinafter, reception of evaluation information will be described in detail. FIG. 3 is a diagram showing a processing flow for receiving evaluation information. In the login process (S301), the evaluation disclosure server 101 logs in the user and establishes a connection. Then, after distribution of the product page screen, when an evaluation page request is received by an instruction from the product page in the evaluation page request reception process (S302), the evaluation page is transmitted and evaluation information is received in the evaluation page transmission process (S303). In the process (S304), the evaluation information is received. Thereafter, in the logout process (S305), the user logs out to cancel the connection.

  FIG. 4 is a diagram illustrating a configuration of an evaluation disclosure server related to reception of evaluation information. In the login process (S301), the login unit 401 transmits a login screen and receives a user ID and a password. The user table 402 stores a user ID and a password in association with each other. If the matching user ID and password exist in the user table 402, the login unit 401 determines that the user authentication is successful and logs in. The evaluation page request reception unit 403 receives access to the evaluation page URL as an evaluation page request in the evaluation page request reception process (S302), and the evaluation page transmission unit 404 stores the evaluation page in the evaluation page transmission process (S303). The evaluation page screen (HTML) is read from the unit 405 and transmitted. The evaluation page storage unit 405 stores an evaluation page that prompts input of evaluation information (evaluation value and evaluation comment). The evaluation information receiving unit 406 receives the evaluation information in the evaluation information receiving process (S304), and stores it in the evaluation information storage unit 407 in association with the product ID and the user ID. Since the evaluation information receiving unit 406 accepts access with a different URL for each product, the product ID can be specified based on the URL. Or the information which identifies goods as a parameter is received, and goods ID is specified. The user ID is specified in the login process (S301). Initially undecided.

  FIG. 5 is a diagram illustrating an example of the evaluation information storage unit. For each user, a user ID is provided in the header, and for each evaluation information, a product ID, an evaluation value, an evaluation comment, and a correction value are stored in association with each other. The product is an example of an evaluation target, and the product ID is an example of an evaluation target ID that identifies the evaluation target. In this example, the evaluation value is set in five stages. The evaluation comment is a character string. The correction value is a value for correcting the evaluation characteristics for each user according to the average value of the evaluation values of the specific user and the standard deviation of the evaluation values with the evaluation value group of the specific user as a population.

  Here, the evaluation value matrix and the correction value matrix will be illustrated, and the significance of conversion into correction values will be described. FIG. 6 is a diagram illustrating an example of the evaluation value matrix. In this figure, the evaluation values are shown in a matrix with user IDs and product IDs. An average evaluation value for each user and an average evaluation value for each product are also shown. U001 users tend to have low ratings. On the other hand, the user of U003 tends to make a high evaluation. Therefore, the evaluation value “3” of the product C10 by the user U001 and the evaluation value “3” of the product C04 by the user U003 are absolutely equivalent, but the substantial evaluation weights are different.

  FIG. 7 is a diagram illustrating an example of the correction value matrix. The correction values are shown in a matrix of user IDs and product IDs. The correction value in this example is obtained by dividing the evaluation value by the user average of the evaluation values. For example, the correction value of the product C10 by the user U001 is “1.80” obtained by dividing the evaluation value “3” by the user average “1.67” of U001. Similarly, the correction value of the product C04 by the user U003 is “0.72” by dividing the evaluation value “3” by the user average “4.14” of U003. Thus, it can be seen that the evaluation value “3” by the dry evaluator U001 is more weighted as the evaluation value than the evaluation value “3” by the sweet evaluator U003. In the figure, the correction value average for each user and the correction value average for each product are also shown for reference. The correction value calculation process will be described later.

  Further, the correction value in FIG. 7 may be a value obtained using a standard deviation. Specifically, as the correction value, the standard deviation σ of the evaluation value is obtained for each evaluator, and then the deviation of the user average is obtained. Thereby, relative judgment can be known about the goods evaluated by the evaluator.

  Subsequently, operations of evaluation value correction and product page generation will be described. FIG. 8 is a diagram illustrating a configuration of an evaluation disclosure server related to evaluation value correction and product page generation. In addition to the above-described evaluation information storage unit 407, the evaluation disclosure server 101 includes a user-specific correction value calculation unit 801, a product-specific evaluation value average calculation unit 802, a product-specific correction value average calculation unit 803, a product page generation unit 804, and A product page storage unit 805 is included.

  FIG. 9 is a diagram illustrating a processing flow of evaluation value correction and product page generation. The correction value for each user is calculated in the correction value calculation processing for each user (S901) by the correction value calculation unit for each user 801. Details will be described with reference to FIG. Then, the following processing is repeated for each product (S902). In the product-by-product evaluation value average calculation process (S903) by the product-by-product evaluation value average calculation unit 802, the average of the product-by-product evaluation value set is calculated. Details will be described with reference to FIG. Furthermore, the average of the correction value set for each product is calculated by the correction value average calculation processing for each product by the product-specific correction value average calculation unit 803 (S904). Details will be described in detail with reference to FIG. In response to the result, a product page is generated by a product page generation process (S905) by the product page generation unit 804. Details will be described with reference to FIG. Then, the process ends when all the products are processed (S906).

  FIG. 10 is a diagram illustrating a user-specific correction value calculation flow. The following processing is repeated for each user (S1001). First, the average value of the user's evaluation value distribution is calculated (S1002), and for each user's evaluation value (S1003), the evaluation value is divided by the average value to obtain a correction value, and the evaluation information is associated with the evaluation value. The data is stored in the storage unit 407 (S1004). When all the evaluation values have been processed, the process proceeds to the next user process (S1005), and the process ends when all the users have been processed (S1006).

  FIG. 11 is a diagram illustrating a product-specific evaluation value average calculation processing flow. The following processing is repeated for each user (S1101). When the product ID is evaluated (S1102), the evaluation value is added to the total value parameter (initial value 0) (S1103), and 1 is added to the evaluation number parameter (initial value 0) (S1104). All users are processed (S1105), the total value parameter is divided by the evaluation number parameter, and the quotient is regarded as the average evaluation value for each product (S1106).

  FIG. 12 is a diagram showing a product-specific correction value average calculation processing flow. The following processing is repeated for each user (S1201). When the product ID is evaluated (S1202), the correction value is added to the total value parameter (initial value 0) (S1203), and 1 is added to the evaluation number parameter (initial value 0) (S1204). Processing is performed for all users (S1205), the total value parameter is divided by the evaluation number parameter, and the quotient is taken as the product-specific correction value average (S1206).

  FIG. 13 is a diagram illustrating an example of a product page displayed on a user terminal of a user as a viewer. In the product page screen (HTML), product images, product names, and prices are set as initial information. In the product page generation process (S905) by the product page generation unit 804, the evaluation value average for each product, the average correction value for each product, and the evaluator name, evaluation value, correction value, and evaluation comment are set for each evaluator. To do. For this purpose, the product-specific evaluation value average is acquired from the product-specific evaluation value average calculation unit 802. The product-specific correction value average is acquired from the product-specific correction value average calculation unit 803. Further, a record including the product ID is specified in the evaluation information storage unit 407, a user ID is acquired from the header of the record, and a name corresponding to the user ID is acquired from the user table 402 and used as an evaluator name. The user table 402 stores names associated with user IDs. The evaluation value, the correction value, and the evaluation comment are read from the record in the evaluation information storage unit 407.

  The correction value on the product page screen of FIG. 13 indicates an evaluation value based on the average value, but the correction value using the average value and the standard deviation (the value in the lower column of FIG. 7) is displayed in a switchable manner. It is also possible to display correction values of different types together. Thereby, it is less dependent on the subjectivity of the evaluator, and it is possible to know multifaceted and complex evaluation.

  Next, an operation for distributing a product page to a viewer will be described. FIG. 14 is a diagram illustrating a configuration of an evaluation disclosure server related to product page distribution. The evaluation disclosure server 101 includes a product page request reception unit 1401 and a product page transmission unit 1402 in addition to the product page storage unit 805.

  FIG. 15 is a diagram showing a product page distribution flow. When the product page request is received from the user terminal 102 of the viewer in the product page request reception process (S1501) by the product page request reception unit 1401, the product page storage unit is processed in the product page transmission process (S1502) by the product page transmission unit 1402. The product page is read from 805 and returned to the user terminal 102.

Embodiment 2. FIG.
In the above-described example, the example in which the evaluation value is divided by the average and the quotient is used as the correction value has been described. In the present embodiment, an example of calculating a correction value by normalization by calculating an average and a standard deviation of a population using a user-evaluated evaluation value for each user as a variable and converting to a predetermined average and variance population explain.

  The process of calculating the correction value for each user (S901 in FIG. 19) will be described. FIG. 16 is a diagram illustrating a user-specific correction value calculation flow. For each user (S1601), the average value of the user's evaluation value distribution is calculated (S1602), and the standard deviation of the user's evaluation value distribution is calculated (S1603). The calculation method of the average value and the standard deviation is based on a well-known method. For each user evaluation value (S1604), the evaluation value is normalized to obtain a correction value (S1605). Specifically, the average value (predetermined average) is subtracted from the evaluation value, the difference is multiplied by the standard deviation of the correction value (predetermined standard deviation), the product is divided by the standard deviation of the evaluation value, and the quotient is corrected. And The process is repeated for all evaluation values (S1606), and the process ends when all the users are processed (S1607).

  For example, when the average value of the correction value distribution is 50 and the standard deviation of the correction value distribution is normalized to 10, a deviation value is obtained as the correction value. Further, when the average value of the correction value distribution is normalized to 0 and the standard deviation of the correction value distribution is normalized to 1, the correction value corresponds to a standard normal distribution.

  The evaluation disclosure server 101 is a computer, and each element can execute processing by a program. Further, the program can be stored in a storage medium so that the computer can read the program from the storage medium.

101 Evaluation Disclosure Server 102 User Terminal 401 Login Unit 402 User Table 403 Evaluation Page Request Receiving Unit 404 Evaluation Page Transmitting Unit 405 Evaluation Page Storage Unit 406 Evaluation Information Receiving Unit 407 Evaluation Information Storage Unit 801 User-Specific Correction Value Calculation Unit 802 Evaluation by Product Value average calculation unit 803 Correction value average calculation unit for each product 804 Product page generation unit 805 Product page storage unit 1401 Product page request reception unit 1402 Product page transmission unit

Claims (9)

An evaluation disclosure server that can be connected to a user terminal of a user who evaluates an evaluation target via the Internet, and has the following elements: (1) an evaluation value for the evaluation target from the user terminal Evaluation information receiving unit (2) that receives evaluation information including the evaluation information storage unit (3) that stores the received evaluation information in association with the evaluation object (3) Evaluation value group included in the evaluation information for each user The correction value calculation part according to user which calculates the correction value which corrects the evaluation characteristic according to user according to the average value which uses as a population, and associates the correction value with the evaluation object and stores it in the evaluation information storage part. Furthermore,
(4) An evaluation target page generation unit that reads a correction value corresponding to the evaluation target and generates an evaluation target page that displays the read correction value;
(5) an evaluation target page storage unit that stores the generated evaluation target page;
(6) an evaluation target page request receiving unit that receives an evaluation target page request from a user terminal;
(7) The evaluation disclosure server according to claim 1, further comprising an evaluation target page transmission unit that reads out and returns the requested evaluation target page from the evaluation target page storage unit.   3. The evaluation according to claim 1, wherein the correction value calculation unit for each user calculates an average of the evaluation value group for each user, divides each evaluation value by the average, and uses the quotient as each correction value. Disclosure server.   The user-specific correction value calculation unit calculates the average and standard deviation of the population using the evaluation value evaluated by the user for each user as a variable, and calculates the correction value by normalization for conversion into a predetermined average and variance population. The evaluation disclosure server according to claim 1 or 2. The evaluation disclosure server further includes a correction value average calculation unit for each evaluation object that calculates an average of correction values for each evaluation object,
The evaluation disclosure server according to claim 1, wherein the evaluation target page generation unit includes the calculated average correction value for each evaluation target in the evaluation target page. The evaluation disclosure server further includes an evaluation object-by-evaluation value average calculation unit that calculates an average of evaluation values by evaluation object,
The evaluation disclosure server according to claim 5, wherein the evaluation target page generation unit includes the calculated evaluation target-specific evaluation value average in the evaluation target page. The user-specific correction value calculation unit includes a first correction value for calculating an average of the evaluation value group for each user, a correction value obtained by dividing the evaluation value by the average, and a mother value using the evaluation value evaluated by the user for each user as a variable. Calculate the mean and standard deviation of the population, find the second correction value calculated by normalization to convert to a predetermined mean and variance population,
The evaluation disclosure server according to claim 2, wherein the evaluation target page generation unit generates both the first correction value and the second correction value. An evaluation disclosure method by an evaluation disclosure server connectable to a user terminal of a user who evaluates an evaluation object via the Internet, comprising the following elements: (1) Evaluation from a user terminal Evaluation information receiving step (2) for receiving evaluation information including an evaluation value for the object (2) Evaluation information storage step (3) for storing the received evaluation information in association with the evaluation object (3) Included in the evaluation information for each user A user-specific correction value calculation step of calculating a correction value of the evaluation value according to an average value using the evaluation value group as a population and storing the correction value in association with the evaluation value. A program for causing a computer serving as an evaluation disclosure server connectable to a user terminal of a user who evaluates an evaluation object via the Internet to execute the following procedure (1) Evaluation including an evaluation value for the evaluation object from the user terminal Evaluation information reception procedure for receiving information (2) For each user, evaluation information storage procedure for storing the received evaluation information in association with an evaluation object (3) For each user, a set of evaluation value groups included in the evaluation information A correction value calculation procedure for each user that calculates a correction value of the evaluation value according to the average value and stores the correction value in association with the evaluation value.

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