JP2017174329A - Information management device, information management method, and computer program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce the burden of work for a user to set permission of provision of personal information.SOLUTION: An information management device of the present invention comprises: a clustering unit for clustering a plurality of users into a plurality of clusters on the basis of a set value included in a setup file; a sorter generation unit for generating a softer that derives one of the plurality of clusters using the set values of a prescribed number of items in learning data; an evaluation unit for calculating the evaluation value of a sorter; an estimation model determination unit for determining a combination of a prescribed number of question items on the basis of the evaluation value of a sorter, and determining a sorter corresponding to the combination of the prescribed number of question items as an estimation model; and a file setting unit for acquiring a set value for the combination of the prescribed number of question items, deriving a cluster by using the estimation model, with the set value for the combination of the prescribed number of question items accepted as input thereto, and determining the set value of a user to be set who is included in the setup file, on the basis of the derived cluster and the set value for the combination of the prescribed number of question items.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information management apparatus, an information management method, and a computer program.

  In recent years, in order to provide more useful services, for example, personal data relating to individuals as big data has been actively used. When using personal data, it is necessary to consider personal privacy. For example, according to the OECD (Organization for Economic Co-operation and Development) guidelines, the purpose of use of collected personal data should be clarified to the collected person. Data must not be used for purposes other than those for which consent has been obtained from the collectee.

  Non-Patent Document 1 describes P3P (Platform for Privacy Preference), which is a standard relating to a mechanism for controlling personal data provided to a web service. Non-Patent Document 2 proposes an agent system that issues a warning when a user tries to use a service that violates the preference by setting the preference according to P3P.

W3C, “The platform for privacy preferences 1.0 (P3P1.0) specicati”, 2002. L. F. Cranor, et. Al., “Use of a P3P user agent by early adapters,” In Proceedings of the 2002 ACM workshop on Privacy in the Electronic Society, WPES’02, pp. 1-10, 2002.

  However, in the above-described conventional technology, the user sets whether or not his / her personal data can be used for the purpose of using personal data. However, this setting work may be a burden on the user. For example, there are a huge number of combinations of personal data usage purposes (for example, for navigation services, product guidance services, etc.) and personal data types (for example, gender, age, residence, current location, etc.). Expected to be a quantity. It can be a heavy burden on the user to set whether to use each combination.

  The present invention has been made in view of such circumstances, and provides an information management apparatus, an information management method, and a computer program that can reduce the burden of work set by a user for permission to provide personal information. The task is to do.

(1) According to one aspect of the present invention, a setting file storage unit that stores a setting file that stores setting values of a plurality of items that are set for permission of provision of personal information for each of a plurality of users, and the setting file A clustering unit that clusters the plurality of users into a plurality of clusters based on the setting values included in the setting file, and uses the setting values of a predetermined number of items among the plurality of items included in the setting file as learning data A classifier generator for generating a classifier for deriving any one of the plurality of clusters with respect to input values for the predetermined number of items, and an evaluation unit for calculating an evaluation value of the classifier And based on the evaluation value of the classifier for each combination of the predetermined number of items selected from the plurality of items included in the setting file, Guess to determine any combination of the predetermined number of items included in the setting file as the combination of the predetermined number of question items, and to determine the classifier corresponding to the combination of the predetermined number of question items as an estimation model For the model determination unit and the setting target user, the setting value for the combination of the predetermined number of question items is acquired, and the estimation model is used with the setting value for the acquired combination of the predetermined number of question items as an input value. The setting target user setting to be included in the setting file based on the set value for the combination of the derived cluster and the acquired predetermined number of question items is derived from any of the plurality of clusters A file setting unit for determining a value.

(2) According to one aspect of the present invention, in the information management device according to (1), the file setting unit is configured based on a representative value of setting values included in the setting file of a user belonging to the derived cluster. It is an information management apparatus which determines the remaining setting value other than the setting value with respect to the combination of the acquired said predetermined number of question items about the setting object user.

(3) According to one aspect of the present invention, in the information management device according to (1) or (2) above, the combination of the predetermined number of question items is transmitted to the terminal device of the setting target user via a communication line. In addition, the information management apparatus further includes an inquiry unit that receives a setting value for the combination of the predetermined number of question items from the terminal device via a communication line.

(4) According to one aspect of the present invention, the information management device stores a setting file that stores setting values for each of a plurality of items set for permission to provide personal information for each of a plurality of users. A clustering step in which the information management device clusters the plurality of users into a plurality of clusters based on the setting value included in the setting file; and the plurality of information management devices included in the setting file. Using the set values of a predetermined number of items among the items as learning data, generate a classifier that derives one of the plurality of clusters with respect to the input value for the predetermined number of items A classifier generating step; an evaluation step in which the information management device calculates an evaluation value of the classifier; and the information management device in the setting Based on the evaluation value of the classifier for each combination of the predetermined number of items selected from the plurality of items included in the file, any of the predetermined number of items included in the setting file A guess model determining step of determining a combination as a combination of the predetermined number of question items and determining the classifier corresponding to the combination of the predetermined number of question items as a guess model; and Any one of the plurality of clusters by obtaining a set value for the combination of the predetermined number of question items and using the guess model with the set value for the acquired combination of the predetermined number of question items as an input value And the setting file is set based on the derived cluster and the set value for the acquired combination of the predetermined number of question items. A file setting determining Mel the setting target user settings, the information management method including.

(5) According to one aspect of the present invention, a setting file storage function for storing a setting file for storing setting values of each of a plurality of items set for permission of provision of personal information for each of a plurality of users in a computer; Based on the setting values included in the setting file, a clustering function for clustering the plurality of users into a plurality of clusters, and learning the setting values of a predetermined number of items among the plurality of items included in the setting file A classifier generation function for generating a classifier for deriving any one of the plurality of clusters with respect to an input value for the predetermined number of items and calculating an evaluation value of the classifier The classifier for each combination of the predetermined number of items selected from the plurality of items included in the setting file Based on the evaluation value, the combination of any one of the predetermined number of items included in the setting file is determined as the combination of the predetermined number of question items, and the classifier corresponding to the combination of the predetermined number of question items And a setting value for the combination of the predetermined number of question items for the setting target user, and a setting value for the combination of the predetermined number of question items acquired as an input value. Then, any one of the plurality of clusters is derived by using the estimation model, and the setting file is set based on the derived cluster and a setting value for the acquired combination of the predetermined number of question items. And a file setting function for determining a setting value of the setting target user to be included.

  According to the present invention, there is an effect that it is possible to reduce the burden of work set by the user for permission to provide personal information.

It is a figure showing an example of composition of an information management system concerning one embodiment. It is a figure which shows the structural example of the hardware of the information management apparatus 1 which concerns on one Embodiment. It is a figure which shows the structural example of the setting file 21 which concerns on one Embodiment. It is a flowchart which shows the example of the information management method which concerns on one Embodiment. It is a figure for demonstrating clustering of the set of users which concerns on one Embodiment. It is a figure which shows the structural example of the user provision label information which concerns on one Embodiment. It is a figure which shows the structural example of the representative value of the cluster which concerns on one Embodiment. It is a flowchart which shows the example of the information management method which concerns on one Embodiment.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is a diagram illustrating a configuration example of an information management system according to an embodiment of the present invention. In FIG. 1, an information management device 1 and a terminal device 3 send and receive data via a communication line. The information management apparatus 1 includes a setting file storage unit 11, a clustering unit 12, a classifier generation unit 13, an evaluation unit 14, an estimation model determination unit 15, a file setting unit 16, and an inquiry unit 17. Is provided. The setting file storage unit 11 stores a setting file 21. The terminal device 3 includes an inquiry handling unit 31.

  In the information management apparatus 1, the setting file storage unit 11 stores a setting file 21. The setting file 21 stores setting values of each of a plurality of items set for permission of provision of personal information for each of a plurality of users. The clustering unit 12 clusters a plurality of users into a plurality of clusters based on setting values included in the setting file 21. The classifier generation unit 13 uses the setting values of a predetermined number of items among the plurality of items included in the setting file 21 as learning data, and among the plurality of clusters for the input values for the predetermined number of items. A classifier that derives one of the clusters is generated.

  The evaluation unit 14 calculates the evaluation value of the classifier. The inference model determination unit 15 selects one of the predetermined items included in the setting file 21 based on the evaluation value of the classifier for each combination of a predetermined number of items selected from the plurality of items included in the setting file 21. A combination of a number of items is determined as a combination of a predetermined number of question items, and a classifier corresponding to the combination of the predetermined number of question items is determined as an inference model.

  The file setting unit 16 acquires a setting value for a predetermined number of question item combinations for a setting target user, and uses a guess model by using the acquired setting values for the predetermined number of question item combinations as input values. One of the plurality of clusters is derived, and the setting value of the setting target user to be included in the setting file 21 is determined based on the derived cluster and the setting value for the acquired combination of the predetermined number of question items. . The inquiry unit 17 transmits a combination of a predetermined number of question items to the terminal device 3 of the setting target user via the communication line, and sets a setting value for the combination of the predetermined number of question items from the terminal device 3 via the communication line. Receive.

  In the terminal device 3, the inquiry handling unit 31 answers in response to an inquiry from the inquiry unit 17 of the information management device 1. The inquiry handling unit 31 transmits a setting value for a combination of a predetermined number of question items received from the information management apparatus 1 via the communication line to the information management apparatus 1 via the communication line.

  FIG. 2 is a diagram illustrating a hardware configuration example of the information management apparatus 1 according to the present embodiment. In FIG. 2, the information management apparatus 1 includes a CPU 51, a storage unit 52, and a communication unit 53. Each of these units is configured to exchange data.

  The CPU 51 controls the information management apparatus 1. This control function is realized by the CPU 51 executing a computer program. The storage unit 52 stores a computer program executed by the CPU 51 and various data. The storage unit 52 stores the setting file 21 and the program 54. The function of each unit of the information management apparatus 1 shown in FIG. 1 is realized by the CPU 51 shown in FIG. 2 executing the program 54 stored in the storage unit 52. The setting file storage unit 11 of the information management apparatus 1 shown in FIG. 1 is provided in the storage unit 52 shown in FIG. The communication unit 53 communicates with other devices via a communication line. The communication unit 53 communicates with the terminal device 3 via a communication line.

  Note that the information management device 1 may be configured using a general-purpose computer device, or may be configured as a dedicated hardware device.

  FIG. 3 is a diagram illustrating a configuration example of the setting file 21 according to the present embodiment. In FIG. 3, the setting file 21 stores setting values of a plurality (M) of items 1 to M in association with user identification information (user ID). The setting file 21 stores setting values of M items 1 to M for each of a plurality of user IDs. One item is an item set for permission to provide personal information for the item. For example, an item is an item for setting whether or not to “provide current position information to the navigation service” is permitted. Further, for example, a certain item is an item for setting whether or not to “provide the user's gender and age to the product guidance service” is permitted.

In the example of FIG. 3, the setting value of the item is “numerical value 1 (indicating YES)” when indicating that the provision of personal information is permitted for the item, and indicates that the provision of personal information is not permitted. “Numerical value 0 (represents NO confirmation)”, and “Numerical value 2 (represents confirmation required)” indicates that confirmation is made every time an opportunity for provision of personal information is confirmed.
Note that the setting value of the item may be binary (permitting or not permitting provision of personal information), or may be three or more. The present embodiment can be applied when the setting value of an item is either binary or ternary or higher. An example in which the setting value of the item is 3 or more is given below.
(Example 1: Item setting value is 3 or more) Example of 3 values
Numerical value 0: Provision of personal information is not permitted.
Number 1: Permits the provision of personal information.
Number 2: Confirm whether to permit each time an opportunity to provide personal information.
(Example where the setting value of the item is 3 or more: Example of 4 values)
Numerical value 0: Provision of personal information is not permitted.
Number 1: Always allow the provision of personal information.
Numerical value 2: Only a specific user (for example, a user whose attribute is a friend) is allowed to provide personal information.
Numerical value 3: The provision of personal information is permitted only to a specific group (for example, a group of service providers whose attributes are games).

  Next, with reference to FIG. 4, the operation | movement which concerns on determination of the estimation model of the information management apparatus 1 is demonstrated. FIG. 4 is a flowchart illustrating an example of the information management method according to the present embodiment. The operation of the information management apparatus 1 will be described with reference to FIG.

(Step S <b> 1) The clustering unit 12 clusters a set of users registered in the setting file 21. The number of clusters (K) is set in the information management apparatus 1 in advance. The clustering method is not particularly limited. As a clustering method, for example, a K-means method may be used.

  In clustering a set of users, the clustering unit 12 sets the user ID included in the setting file 21 as a clustering target, and associates the user ID with the clustering target user ID included in the setting file 21 by, for example, the K-means method. Based on the set value, the clustered user IDs are classified into K clusters. The user IDs to be clustered may be all user IDs included in the setting file 21 or some user IDs. Clustering user IDs (users) are classified into K clusters by clustering a set of users.

  The clustering unit 12 assigns a label to each of the K clusters. Further, the clustering unit 12 calculates a representative value for each of the K clusters. The representative value of a cluster is a representative value of setting values stored in the setting file 21 in association with user IDs belonging to the cluster. The representative value is calculated based on, for example, the center of gravity, average, mode value, and the like.

  FIG. 5 is a diagram for explaining clustering of a set of users according to the present embodiment. In the example of FIG. 5, the number of clusters is three (K = 3). In FIG. 5, the users U1 to U13 to be clustered are classified into three clusters CL1, CL2, and CL3 by clustering a set of users. A label 1 is assigned to the cluster CL1. A label 2 is assigned to the cluster CL2. A label 3 is assigned to the cluster CL3.

  Further, the centroids D1, D2, D3 are calculated as representative values of the clusters CL1, CL2, CL3. The representative value of the cluster CL1 is the centroid D1 of the setting values stored in the setting file 21 in association with the user IDs belonging to the cluster CL1. The representative value of the cluster CL2 is the center of gravity D2 of the setting values stored in the setting file 21 in association with the user IDs belonging to the cluster CL2. The representative value of the cluster CL3 is the centroid D3 of the setting values stored in the setting file 21 in association with the user IDs belonging to the cluster CL3.

  The clustering unit 12 records the label of the cluster to which the user ID belongs in association with the user ID to be clustered. FIG. 6 is a diagram illustrating a configuration example of user-assigned label information according to the present embodiment. In FIG. 6, the user-assigned label information has a label value indicating a label in association with the user ID. The user-assigned label information is stored in the storage unit 52.

  FIG. 7 is a diagram illustrating a configuration example of representative values of clusters according to the present embodiment. In the example of FIG. 7, the representative value is the center of gravity. The example of FIG. 7 corresponds to FIG. In FIG. 7, a centroid vector having the set values of M items 1 to M as elements is calculated as a representative value of each of the clusters CL1, CL2, and CL3. The representative value of the cluster is stored in the storage unit 52 in association with the label of the cluster.

  Returning to FIG. Subsequent steps S2 to S5 are repeatedly executed by the number of the combinations while sequentially selecting a predetermined number (N) of combinations from the M items 1 to M included in the setting file 21. Is done. The value of N may be arbitrarily set or may be a predetermined default value. However, “N <M”. N is the number of items for which the user is inquired about the set value. For this reason, the smaller the value of N, the smaller the burden on the user's setting value setting.

(Step S <b> 2) The classifier generation unit 13 selects N items from the M items 1 to M included in the setting file 21.

(Step S3) The classifier generation unit 13 uses the setting values of the N items selected in step S2 among the M items 1 to M included in the setting file 21 as learning data, A classifier that generates one of the K clusters from the input values of the items is generated. In the generation of the classifier, the classifier generation unit 13 acquires the setting values of the N items selected in step S2 from the setting file 21. The target user IDs for which the setting values of the N items are acquired from the setting file 21 may be all user IDs included in the setting file 21 or some user IDs. The classifier generation unit 13 generates a classifier using the setting values of the N items acquired from the setting file 21 as learning data. The generated classifier is a classifier for deriving any one of the K clusters from the input values for the N items. For example, the classifier outputs a label indicating the resulting cluster. For example, a support vector machine (SVM) may be used as the classifier. As a multi-level classifier, for example, a multi-level support vector machine may be used.

(Step S4) The evaluation unit 14 calculates the evaluation value of the classifier generated by the classifier generation unit 13. In the calculation of the evaluation value of the classifier, the evaluation unit 14 uses the user-assigned label information stored in the storage unit 52 as correct data and compares the result with the derivation result of the cluster of the classifier. For example, the evaluation unit 14 calculates a correct answer rate as a result of the comparison.

  The user-assigned label information in FIG. 6 will be specifically described as an example. For example, the user ID “UID_A” is taken as an example. The evaluation unit 14 acquires setting values of N items associated with the user ID “UID_A” from the setting file 21. The evaluation unit 14 executes the classifier using the acquired setting values of the N items as input values, and obtains the labels of the clusters derived as a result of the execution. The evaluation unit 14 acquires a label (referred to as a correct answer label) “1” associated with the user ID “UID_A” from the user-assigned label information. The evaluation unit 14 compares the cluster label derived as the execution result of the classifier with the correct answer label “1”. As a result of this comparison, if both match, the answer is correct, and if both do not match, the answer is incorrect.

  The evaluation unit 14 evaluates the classifier for each user ID included in the user-assigned label information, and obtains an evaluation value by combining the evaluation results. For example, the evaluation unit 14 determines the correct answer or incorrect answer of the classifier for each user ID included in the user-assigned label information, and calculates the correct answer rate by combining the determination results. The target user IDs used for the evaluation of the classifier may be all user IDs included in the user-assigned label information, or some user IDs.

(Step S <b> 5) The classifier generation unit 13 determines whether generation and evaluation of the classifier have been completed for all N items of the M items 1 to M included in the setting file 21. To do. As a result of the determination, if the process is completed, the process proceeds to step S6. On the other hand, if not completed yet, the process returns to step S2, and classifiers are generated and evaluated for combinations of N items that have not yet been selected.

(Step S6) The estimation model determination unit 15 estimates based on the evaluation value of the classifier for each combination of N items selected from the M items 1 to M included in the setting file 21. Determine the model. In the determination of the estimation model, the estimation model determination unit 15 determines the classifier that is the best evaluation value among the classifiers for each combination of N items as the estimation model. For example, the estimation model determination unit 15 determines the classifier having the maximum correct answer rate calculated by the evaluation unit 14 among the classifiers generated by the classifier generation unit 13 as the estimation model. Further, the speculative model determination unit 15 determines a combination of N items corresponding to the classifier determined as the speculative model as a combination of N question items.

  Next, an operation related to the setting of the setting file 21 of the information management apparatus 1 will be described with reference to FIG. FIG. 8 is a flowchart illustrating an example of the information management method according to the present embodiment. The operation of the information management apparatus 1 will be described with reference to FIG.

(Step S11) The inquiry unit 17 transmits the combination of N question items determined by the estimation model determination unit 15 to the terminal device 3 of the setting target user via a communication line. The terminal device 3 of the setting target user receives the combination of the N question items from the information management device 1 via the communication line. In the terminal device 3 of the setting target user, the inquiry handling unit 31 displays the received combination of N question items on the display screen of the terminal device 3. The setting target user inputs the setting value of each question item for the combination of N question items displayed on the display screen using the input device of the terminal device 3. For example, the setting target user inputs “numerical value 1 (represents YES)” to permit the question item to be set for permission to “provide current location information to the navigation service”. If not, enter “numerical value 0 (represents NO)”, and enter “numerical value 2 (represents confirmation required)” when confirming whether to allow the current location information every time it is provided.

(Step S <b> 12) In the terminal device 3 of the setting target user, the inquiry handling unit 31 transmits the setting value input to the combination of N question items by the setting target user to the information management device 1. The information management device 1 receives a setting value for a combination of N question items from the terminal device 3 of the setting target user via a communication line. The inquiry unit 17 of the information management apparatus 1 passes the setting value for the received combination of N question items to the file setting unit 16.

(Step S <b> 13) The file setting unit 16 acquires a setting value for a combination of N question items for the setting target user from the inquiry unit 17. The file setting unit 16 executes the estimation model using the set values for the obtained combinations of N question items as input values, and obtains the labels of the clusters derived as a result of the execution. The file setting unit 16 acquires the representative value of the cluster associated with the cluster label of the execution result of the speculative model from the storage unit 52. The file setting unit 16 includes the cluster representative value acquired from the storage unit 52 and the setting value for the combination of N question items for the setting target user acquired from the inquiry unit 17 in the setting file 21. Determine the setting value of the target user.

  For example, the file setting unit 16 uses the setting value for the combination of N question items for the setting target user acquired from the inquiry unit 17 as it is as the setting value of the corresponding item. On the other hand, the file setting unit 16 corresponds to a setting value other than the setting value for the combination of N question items for the setting target user acquired from the inquiry unit 17 among the representative values of the clusters acquired from the storage unit 52. Use the representative value of the item.

(Step S14) The file setting unit 16 stores the setting value of the setting target user in the setting file 21 in association with the user ID of the setting target user.

  According to the present embodiment, with respect to the setting file 21 including the setting values of M items 1 to M, the setting values of M items 1 to M are set according to the setting values for the combination of N question items. It can be included in the file 21. Thereby, the effect that the burden of the operation | work which a user sets about permission of provision of personal information can be reduced is acquired. For example, in the present embodiment, the number of items included in the setting file 21 is 100 (M = 100), and the number of question items included in the combination of question items presented to the user is 10 (N = In the case of 10), the user can include setting values of 100 items in the setting file 21 by inputting setting values for combinations of 10 question items.

  Further, according to the present embodiment, it is not necessary to generate a classifier for deriving setting values for M items 1 to M included in the setting file 21 for each item for which the setting value is estimated. Thereby, it is possible to reduce the processing amount for generating the classifier. For example, when the number of items included in the setting file 21 is 100 (M = 100) and the number of question items included in the combination of question items presented to the user is 10 (N = 10) In addition, if a classifier is generated for each item for which a set value is estimated, each classifier for deriving the remaining 90 set values other than the set values for a combination of 10 question items, that is, 90 classes Must be created. However, according to the present embodiment, only one classifier corresponding to a certain combination of 10 question items needs to be generated.

  As mentioned above, although embodiment of this invention was explained in full detail with reference to drawings, the specific structure is not restricted to this embodiment, The design change etc. of the range which does not deviate from the summary of this invention are included.

  For example, in the above-described embodiment, a setting value related to the user included in the setting file 21 may be transmitted to the terminal device 3 of a certain user, and a change in the setting value may be received from the terminal device 3 of the user.

  Alternatively, the service usage history of each user may be recorded, and users belonging to the same cluster may recommend services with high service usage frequency based on the recording.

Further, a computer program for realizing the functions of the information management apparatus 1 described above is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into the computer system and executed. Good. Here, the “computer system” may include an OS and hardware such as peripheral devices.
“Computer-readable recording medium” refers to a flexible disk, a magneto-optical disk, a ROM, a writable nonvolatile memory such as a flash memory, a portable medium such as a DVD (Digital Versatile Disc), and a built-in computer system. A storage device such as a hard disk.

Further, the “computer-readable recording medium” means a volatile memory (for example, DRAM (Dynamic DRAM) in a computer system that becomes a server or a client when a program is transmitted through a network such as the Internet or a communication line such as a telephone line. Random Access Memory)), etc., which hold programs for a certain period of time.
The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

DESCRIPTION OF SYMBOLS 1 ... Information management apparatus, 3 ... Terminal device, 11 ... Setting file storage part, 12 ... Clustering part, 13 ... Classifier production | generation part, 14 ... Evaluation part, 15 ... Guess model determination part, 16 ... File setting part, 17 ... Inquiry part, 21 ... setting file, 31 ... inquiry correspondence part, 51 ... CPU, 52 ... storage part, 53 ... communication part, 54 ... program

Claims (5)

A setting file storage unit for storing a setting file for storing setting values of each of a plurality of items set for permission of provision of personal information for each of a plurality of users;
A clustering unit that clusters the plurality of users into a plurality of clusters based on the setting value included in the setting file;
Using the setting values of a predetermined number of items among the plurality of items included in the setting file as learning data, any cluster of the plurality of clusters with respect to an input value for the predetermined number of items A classifier generator for generating a classifier for deriving
An evaluation unit for calculating an evaluation value of the classifier;
Based on the evaluation value of the classifier for each combination of the predetermined number of items selected from the plurality of items included in the setting file, any one of the predetermined number included in the setting file A speculative model determining unit that determines a combination of items as a combination of the predetermined number of question items, and determines the classifier corresponding to the predetermined number of question item combinations as a speculative model;
For the setting target user, a set value for the combination of the predetermined number of question items is acquired, and the set value for the acquired combination of the predetermined number of question items is used as an input value to use the guess model. A file for deriving any one of the clusters and determining the setting value of the setting target user to be included in the setting file based on the derived cluster and the setting value for the acquired combination of the predetermined number of question items A setting section;
An information management device comprising: The file setting unit, for the setting target user, based on a representative value of setting values included in the setting file of users belonging to the derived cluster, other than setting values for the acquired combination of the predetermined number of question items Determine the remaining settings for
The information management apparatus according to claim 1. An inquiry unit that transmits a combination of the predetermined number of question items to the terminal device of the setting target user via a communication line and receives a setting value for the combination of the predetermined number of question items from the terminal device via the communication line Further comprising
The information management apparatus according to claim 1 or 2. A setting file storage step for storing a setting file for storing a setting value of each of a plurality of items set by the information management apparatus for permission to provide personal information for each of a plurality of users;
A clustering step in which the information management device clusters the plurality of users into a plurality of clusters based on the setting value included in the setting file;
The information management apparatus uses the setting values of a predetermined number of items among the plurality of items included in the setting file as learning data, and uses the plurality of clusters for input values of the predetermined number of items. A classifier generation step for generating a classifier for deriving any of the clusters;
An evaluation step in which the information management device calculates an evaluation value of the classifier;
Any of the information management devices included in the configuration file based on the evaluation value of the classifier for each combination of the predetermined number of items selected from the plurality of items included in the configuration file. Determining a combination of the predetermined number of items as a combination of the predetermined number of question items, and determining a classifier corresponding to the predetermined number of question item combinations as a speculative model;
The information management device acquires setting values for the predetermined number of question item combinations for the setting target user, and uses the guess model using the acquired setting values for the predetermined number of question item combinations as input values By deriving one of the plurality of clusters, the setting target user to be included in the setting file based on the derived cluster and the setting value for the acquired combination of the predetermined number of question items A file setting step for determining the setting value;
Information management method. On the computer,
A setting file storage function for storing a setting file for storing setting values of a plurality of items set for permission to provide personal information for each of a plurality of users;
A clustering function for clustering the plurality of users into a plurality of clusters based on the setting values included in the setting file;
Using the setting values of a predetermined number of items among the plurality of items included in the setting file as learning data, any cluster of the plurality of clusters with respect to an input value for the predetermined number of items A classifier generation function for generating a classifier for deriving
An evaluation function for calculating an evaluation value of the classifier;
Based on the evaluation value of the classifier for each combination of the predetermined number of items selected from the plurality of items included in the setting file, any one of the predetermined number included in the setting file A speculation model determination function for determining a combination of items as a combination of the predetermined number of question items and determining the classifier corresponding to the combination of the predetermined number of question items as a speculation model;
For the setting target user, a set value for the combination of the predetermined number of question items is acquired, and the set value for the acquired combination of the predetermined number of question items is used as an input value to use the guess model. A file for deriving any one of the clusters and determining the setting value of the setting target user to be included in the setting file based on the derived cluster and the setting value for the acquired combination of the predetermined number of question items Setting function,
Computer program for realizing.

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