JP2019046387A - Program, device and method for estimating peer pressure in group to prescribed action - Google Patents

Abstract

To provide a program, etc., for estimating peer pressure in a group to a prescribed action.SOLUTION: Provided is a program for causing a computer to function that is installed in a device that estimates peer pressure in a group to the decision making of a user u from a plurality of other users u', the program causing the computer to function as: influence probability calculation means for calculating, for each user group, influence probability (u'|u), with regard to a prescribed action c, that indicates positional or attributional proximity; individual peer pressure calculation means for calculating, for each undecided user u not having made decision, individual peer pressure SP(u|Gma) received from each of other decided users u'(=1 thru Gma) having made decision; and group peer pressure calculation means for calculating group peer pressure SP(Gmi|Gma) by multiplying the sum of individual peer pressure SP(u|Gma) on all undecided users u(=1 thru Gma) in a group by a ratio of the number of decided users to the number of undecided users.SELECTED DRAWING: Figure 1

Description

  The present invention relates to the art of marketing research based on the influence of human relationships. Specifically, the present invention relates to a technique for estimating the influence received from other members in a group on the user's usage behavior for a predetermined product service.

  In recent years, technology has been developed to estimate human relationships from all kinds of data related to daily life. For example, it is also possible to estimate a human relationship network using social media information of a social networking service (SNS) site to which a user browses and posts, position information and a call history in a portable terminal owned by the user. Moreover, it is also possible to discover groups having some kind of relationship from these human relationship networks by centrality-based clustering. Human relationship networks and group information obtained in this way have been used for marketing purposes.

BACKGROUND Conventionally, there is a technique for estimating the degree of influence that one user's action induces another user's action.
For example, there is a technique of defining a propagation model of information in a known social work using social media information (see, for example, Non-Patent Document 1).
In addition, there is also a technology for estimating not only the relationship between users but also the action history to estimate the degree of influence between users (for example, see Non-Patent Document 2).
Furthermore, even when the relationship between users is unknown, there is also a technique for estimating the degree of influence between users at a certain time with respect to a certain event (for example, see Non-Patent Document 3).

Saito, K., Nakano, R., and Kimura, M. 2008. Prediction of Informatization Diffusion Probabilities for Independent Cascade Model. In Proceedings of the 12th International Conference on Knowledge-Based Intelligent Systems and Engineering Systems, Part III (Zagreb, Croatia, September 03-05, 2008). I. Lov rek, RJ Howlett, and LC Jain, Eds. Lecture Notes In Artificial Intelligen ce, vol. 5179. Springer-Verlag, Berlin, Heidelberg, 67-75, 2008. Goyal, A., Bonchi, F., and Lakshmanan, LV 2010. Learning in probability probabilities in social networks. In Proceedings of the ACM. Interim conference on Web Search and Data Mining (New York, New York, USA, February y. 04-06, 2010) WSDM '10. ACM, New York, NY, 241-250. Ching-man Au Yeung and Tomoharu Iwata. 2010. Capturing implicit user influence in online social sharing. In Proceedings of the 21st ACM conference on Hypertext and hypermedia (HT '10). ACM, New York, NY, USA, 245-254. Ishizuka Hiroki, Ono Tomohiro, Yamamoto Naoto, Ito Masato, Sezaki Atsushi, Implementation and Evaluation of Real-space Human Relationship Extraction Method Using Proximity with Bluetooth, Multimedia, Distributed, Cooperative and Mobile (DICOMO 2015) Symposium, 1721-1728 Palla, G., Derenyi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435 (7043), 814-818.

In any of the above-described conventional techniques, the degree of influence between users (user-user) is estimated.
Here, the inventors of the present application considered that the decision-making of one user in the group may be influenced by other multiple users having various intentions. That is, it was considered that the coordination pressure in the group is affecting the decision making of the user for the predetermined action.

  Therefore, an object of the present invention is to provide a program, an apparatus, and a method for estimating the synchronized pressure in a group for a predetermined action.

According to the present invention, there is provided a program that causes a computer mounted on a device for estimating tuning pressure from a plurality of other users u ′ for making a decision of a user u in a group to function.
Influence probability calculation means for calculating an influence probability P (u ′ | u) representing positional or attributional proximity for each user for the predetermined action c;
Individual tuning pressure calculation means for calculating an individual tuning pressure SP (u | Gma) received from each other decided user u ′ (= 1 to Gma) that has made a decision for each undecided user u who has not made a decision;
Within a group, a group tuning pressure SP (Gmi | Gma) obtained by multiplying the sum of the individual tuning pressures SP (u | Gma) at all pending users u (= 1 to Gma) by the ratio of the number of pending users to the number of pending users. The computer functions as a group tuning pressure calculation means for calculating.

According to another embodiment of the program of the invention:
The individual tuning pressure calculation means determines that the individual tuning pressure SP (u | Gma) is larger as the variance value of the influence probability P (u ′ | u) of the other determined users u ′ with respect to the pending user u in the group is smaller. It is also preferable to have the computer function to be a value.

According to another embodiment of the program of the invention:
The influence probability calculation means calculates a plurality of influence probabilities P (u ′ | u, c) for different predetermined actions c for the group,
The individual tuning pressure calculation means calculates the individual tuning pressure SP (u | Gma, c),
The group tuning pressure calculating means calculates a group tuning pressure SP (Gmi | Gma, c),
It is also preferable to further cause the computer to function as a category selection means for selecting a predetermined action c in which the group tuning pressure SP (Gmi | Gma, c) is higher than a predetermined condition.

According to another embodiment of the program of the invention:
The influence probability calculation means calculates a plurality of influence probabilities P (u ′ | u, t) for different time zones t of the group,
The individual tuning pressure calculation means calculates the individual tuning pressure SP (u | Gma, t),
The group tuning pressure calculating means calculates a group tuning pressure SP (Gmi | Gma, t),
It is also preferable to further cause the computer to function as a category selection means for selecting a time zone t in which the group tuning pressure SP (Gmi | Gma, t) is higher than a predetermined condition.

According to another embodiment of the program of the invention:
The influence probability calculation means calculates a plurality of influence probabilities P (u ′ | u, c, t) for different predetermined actions c and different time zones t for the group,
The individual tuning pressure calculation means calculates the individual tuning pressure SP (u | Gma, c, t),
The group tuning pressure calculation means calculates a group tuning pressure SP (Gmi | Gma, c, t),
It is also preferable to further cause the computer to function as category selection means for selecting a predetermined behavior c and a time zone t in which the group synchronization pressure SP (Gmi | Gma, c, t) is higher than a predetermined condition.

According to another embodiment of the program of the invention:
It is also preferable to cause the computer to function so that the predetermined action c is a use action based on a product service.

According to another embodiment of the program of the invention:
Preferably, the group causes the computer to function as a plurality of users clustered based on positional or attributional proximity.

According to the invention, a tuning pressure estimation device for estimating tuning pressure from a plurality of other users u ′ for a decision of a user u in a group,
Influence probability calculation means for calculating an influence probability P (u ′ | u) representing positional or attributional proximity for each user for the predetermined action c;
Individual tuning pressure calculation means for calculating an individual tuning pressure SP (u | Gma) received from each other decided user u ′ (= 1 to Gma) that has made a decision for each undecided user u who has not made a decision;
Within a group, a group tuning pressure SP (Gmi | Gma) obtained by multiplying the sum of the individual tuning pressures SP (u | Gma) at all pending users u (= 1 to Gma) by the ratio of the number of pending users to the number of pending users. And group tuning pressure calculating means for calculating.

According to the invention, there is provided a method of tuning pressure estimation of a device for estimating tuning pressure from a plurality of other users u 'for decision making of a user u within a group,
The device is
A first step of calculating an influence probability P (u ′ | u) representing positional or attributional proximity for each user for the predetermined action c;
A second step of calculating an individual tuning pressure SP (u | Gma) received from each other decided user u ′ (= 1 to Gma) who has made a decision for each undecided user u who has not made a decision;
Within a group, a group tuning pressure SP (Gmi | Gma) obtained by multiplying the sum of the individual tuning pressures SP (u | Gma) at all pending users u (= 1 to Gma) by the ratio of the number of pending users to the number of pending users. And calculating a third step of calculating.

  According to the program, apparatus and method of the present invention, it is possible to estimate the in-group tuning pressure for a predetermined action.

It is a functional block diagram of the tuning pressure estimation apparatus in this invention. It is explanatory drawing showing the influence degree of the users in a group. It is explanatory drawing showing the example 1 and the example 2 of the influence probability of the users in a group.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a functional block diagram of a tuning pressure estimation device in the present invention.

  According to FIG. 1, the tuning pressure estimation device 1 of the present invention communicates with the service server 2 via the Internet. The tuning pressure estimation device 1 can obtain service usage history information for each user from the service server 2 via an API (Application Programming Interface).

  The tuning pressure estimation device 1 of the present invention estimates tuning pressure from a plurality of other users u ′ for decision making of the user u within a group. Here, the tuning pressure estimation device 1 of the present invention estimates tuning pressure from a group of established users for a group of pending users for a predetermined behavior in a group. The predetermined action may be, for example, a use action based on a commodity service. This makes it possible to know how much coordination pressure is working in the group with regard to the use behavior (predetermined behavior) of the commodity service.

  Here, "tuned pressure" is assumed that a plurality of users use a specific coupon, for example, in the same circle of the school. At that time, a user who does not use the coupon may know that another user is using the coupon in a conversation. The greater the percentage of users using the coupon within the group, the higher the likelihood that the user will use the coupon. That is, in the group in the same action environment or belonging environment, the coordination pressure means the degree to which the decision of other plural users influences the decision of one user.

  In addition, the user group included as a member in the group may change depending on not only positionally or attribution but also, for example, a time zone. According to the present invention, since it is possible to know the tuning pressure for each of the configured groups, it is possible to compare marketing data by comparing their magnitudes.

The service server 2 collects action history information for each user from a large number of terminals 3 owned by the user. As the action history information, service usage history information is associated with user-specific information of the user.
[User-specific information]-[Service usage history information]
The service usage history information is information that has been decided or undecided for decision making. For example, it may be history information of use / unuse of service in offline, or history information of browsing of a web site in online. Specifically, the time of decision or the time zone is associated with the determined / undecided.
The user unique information may be position history information periodically collected from a smartphone or the like possessed by the user, or attribution information in a company, a school, a circle, or a social networking service (SNS). The position information is associated with the time or time zone when the position was acquired.

  The tuning pressure estimation device 1 may be one in which user's action history information (user specific information and service use history information) is stored in advance in a database. That is, it is not essential for the tuning pressure estimation device 1 to communicate with the service server 2.

  The tuning pressure estimation device 1 includes a group classification unit 10, an influence probability calculation unit 11, an individual tuning pressure calculation unit 12, a group tuning pressure calculation unit 13, and a category selection unit 14 as well as a communication interface. These functional components are realized by executing a program that causes a computer installed in the device to function. The flow of processing of these functional components can also be understood as a method for estimating the tuning pressure of the device.

[Group classification unit 10]
The group classification unit 10 classifies a plurality of users into a clustered group based on positional or attributional proximity using behavior history information.
"Positional" may be based on, for example, positional proximity between users or frequency of contact.
The term "attributive" may be based on, for example, the proximity of human relations (see, for example, Non-Patent Document 4).
Thereby, clustering can be performed using the similarity of a plurality of users as an index and classified into a plurality of groups (see, for example, Non-Patent Document 5).
That is, a plurality of users are grouped in some sense. Specifically, in the simplest case, they may be grouped according to the whereabouts of the user (e.g., company or school).

FIG. 2 is an explanatory view showing the influence of users in a group.
A plurality of users u belong to each group G. For example, for a specific group G, it is assumed that five users are included as follows.
Users of group G: U∈u1, u2, u3, u4, u5

[Influence probability calculation unit 11]
The influence probability calculation unit 11 calculates an influence probability P (u ′ | u) representing positional or attributional proximity for each user with respect to the predetermined action c.

The action history information S based on the predetermined action (whether or not the service can be used in the real space) is defined as follows, for example.
S = <U, C, Y>
U: set of all users, u: each user, U∈u
C: A set of actions (services) c: each action (services) C∈c
Y: collection of action (service) history, Yu: action history of user u Action history information (u, c, t) ∈ Y means that user u ∈ U used service c ∈ C at time t Represents

For the group G, it is assumed that the user u decides on an action (service use) influenced by each other user u ′. At this time, for example, the probability that the user u uses the service c at time t can be expressed as follows.
U _G ∈u, u '
P (u '| u): Probability of influence that user u will be propagated from another user u' B (c | u ', t): service in service usage history of user u' at time t c is
Event decision making probability included P (c | u, t) = u u '∈ _U P (u' | u) B (c | u ', t)
_{U u '} P (u' | u) = 1, P (u '| u) ≧ 0
_{C c} B (c | u ', t) = 1, B (c | u', t) ≧ 0
B (c | u ', t) = 1 / H (u', t, τ _i ) if (u ', c, t') ∈Y∧t-t '≦ τ _i
0 other
τ _i : Period to calculate the event decision probability of the user
H (u ', t, τ _i ) = | {c | (u', c, t ') ∈Y∧t-t' ≦ τ _i } |
: Number of services used by other user u 'in period [t-τ _i , t]
(Of course, the period [t-τ _i , t] may be the entire period)
The equation B (c | u ', t) may be weighted so that the recently used service has a high probability.

Using the event decision probability B, the influence probability P (u ′ | u) that the user u receives from another user u ′ is calculated. The influence probability P is calculated based on the maximum likelihood method that maximizes the likelihood. Specifically, the influence probability P maximizes the likelihood by an EM (Expectation Maximization) algorithm based on the following constraint equation.
U u '∈ _U P (u' | u) = 1
Of course, not only the EM algorithm but also other optimization methods such as the Newton method may be used.

When using the EM algorithm, first, as the E (Expectation) step, the posterior probability is calculated as follows.
P (u '| u, c , t) = (P (u' | u) B (c | u ', t)) / Σ u''∈U (P (u''| u) B (c | u '', t))
Next, an influence probability is calculated as follows as an M (Maximization) step.
P (u '| u) Σ Σ _t ∈ _c∈C P (u' | u, d, t)
Then, the calculation of these influence probabilities is repeated until the end condition represented by the following equation is satisfied.
log L _u = _{t t} ∈ _{c ∈} C _log u u '∈ _U P (u' | u) B (c | u ', t)
L _u : Likelihood of user u Further, as another end condition, the parameter may be converged, it may be repeated a predetermined number of times or more, or a predetermined time or more may be elapsed. Also, instead of the maximum likelihood method, any of a posteriori probability maximization method, a Markov chain Monte Carlo method, and a posteriori probability estimation method by variational Bayesian may be used.

  Finally, the influence probability calculation unit 11 outputs, to the individual tuning pressure calculation unit 12, all the influence probabilities P (u '| u) calculated round-round for each user with respect to the predetermined action c.

[Individual tuning pressure calculation unit 12]
The individual tuning pressure calculation unit 12 calculates the individual tuning pressure SP (u | Gma) received from each other decided user u ′ (= 1 to Gma) who has made the decision for each undecided user u who has not made the decision.
Specifically, for the predetermined action c in the group, the individual tuning pressure from the determined user for the undetermined user is estimated from the variance value of the influence probability of the determined user for the undetermined user.

For example, for the group G, it is assumed that the predetermined behavior (service use) c of each user is as follows.
u1: Pending user
u2: The decided user
u3: The decided user
u4: The decided user
u5: Pending user
Gmi = u1, u5, group of pending users
Gma = u2, u3, u4, a group of established users

For group G, each pending user u (u1, u5) receives an individual tuning pressure from the established user u '(u2, u3, u4), and is expressed as follows.
SP (u, Gma) =. _SIGMA.Gma (P (u '| u)-(. SIGMA.P (u' | u) / Gma) ² ) / Gma
According to this equation, the smaller the variance of the influence probability P (u ′ | u) of the other established users u ′ with respect to the pending user u in the group, the larger the value of the individual tuning pressure SP (u | Gma) (Individual tuning pressure is strong).
The fact that the variance value of the influence probability P (u ′ | u) is the smallest means that the influence from the determined user on the relevant undetermined user is equally transmitted.

  The individual tuning pressure SP (u | Gma) received from each other preset user u ′ for each pending user u is output to the group tuning pressure calculation unit 13.

[Group tuning pressure calculation unit 13]
The group tuning pressure calculation unit 13 is a group obtained by multiplying the sum w of the individual tuning pressures SP (u | Gma) in all the pending users u (u1 to Gma) by the ratio w of the number of pending users to the number of pending users in the group. The tuning pressure SP (Gmi | Gma) is calculated and expressed as follows.
SP (Gmi, Gma) = _{G Gmi} SP (u, Gma) · w
w = determined user / pending user group tuning pressure SP (Gmi | Gma) is output to the category selection unit 14.

<Example of individual tuning pressure and group tuning pressure>
FIG. 3 is an explanatory view showing Example 1 and Example 2 of the influence probability between users in a group.

Here, P (c, u, t)-> P (u) is used to clarify the simplest example. That is, the fixed predetermined action c and the fixed time zone t are used.
According to FIG. 3, Example 11 and Example 12 are clarified below as what P (u '| u) of a different value was calculated.
[Example 11]
P (u2 | u1) = 0.25
P (u3 | u1) = 0.25
P (u4 | u1) = 0.25
P (u5 | u1) = 0.25
ΣP (u '| u1) = 1
--------------
P (u1 | u5) = 0.2
P (u2 | u5) = 0.2
P (u3 | u5) = 0.4
P (u4 | u5) = 0.2
ΣP (u '| u5) = 1
--------------
SP (u1, Gma) =. SIGMA. (P (u '| u1)-(. SIGMA.P (u' | u1) / Gma) ² ) / Gma
= ((0.25-0.75 / 3) ² + (0.25-0.75 / 3) ² + (0.25-0.75 / 3) ² ) / 3
= 0
SP (u5, Gma) =. SIGMA. (P (u '| u5)-(. SIGMA.P (u' | u5) / Gma) ² ) / Gma
= ((0.2-0.8 / 3) ² + (0.4-0.8 / 3) ² + (0.2-0.8 / 3) ² ) / 3
=. 0044 + 0.0178 + 0.0044
= 0.0266
SP (Gmi, Gma) = SPSP (u1, Gma) · w
= (0 + 0.0266) 3/2
= 0.0399
[Example 12]
P (u2 | u1) = 0.3
P (u3 | u1) = 0.4
P (u4 | u1) = 0.2
P (u5 | u1) = 0.1
ΣP (u '| u1) = 1
--------------
ΣP (u | u5) = 1
P (u1 | u5) = 0.1
P (u2 | u5) = 0.3
P (u3 | u5) = 0.1
P (u4 | u5) = 0.5
--------------
SP (u1, Gma) =. SIGMA. (P (u '| u1)-(. SIGMA.P (u' | u1) / Gma) ² ) / Gma
= ((0.3-0.9 / 3) ² + (0.4-0.9 / 3) ² + (0.2-0.9 / 3) ²⁾ / 3
= (0 + 0.01 + 0.01) / 3
= 0.0066
SP (u5, Gma) =. SIGMA. (P (u '| u5)-(. SIGMA.P (u' | u5) / Gma) ² ) / Gma
= ((0.3-0.7 / 3) ² + (0.1-0.7 / 3) ² + (0.5-0.7 / 3) ² ) / 3
= (0.0044 + 0.0178 + 0.0237) / 3
= 0.0459
SP (Gmi, Gma) = SPSP (u ', Gma) · w
= (0.0066 + 0.0469) 2/3
= 0.0350
In this case, it can be understood that the tuning pressure is stronger in [Example 11] than in [Example 12].

<Estimation of tuning pressure of different predetermined action c and / or different time zone t>
According to the above example, as the simplest example, the fixed predetermined action c and the fixed time zone t are used. Here, it is also preferable to make a plurality of tuning pressures as different predetermined actions c and / or different time zones t for one group.

[Example 21]
It is assumed that the influence probability calculation unit 11 calculates a plurality of influence probabilities P (u ′ | u, c) for different predetermined actions c for a group.
In this case, the individual tuning pressure calculation unit 12 calculates the individual tuning pressure SP (u | Gma, c).
Further, the group tuning pressure calculation unit 13 calculates a group tuning pressure SP (Gmi | Gma, c).
[Example 22]
It is assumed that the influence probability calculation unit 11 calculates a plurality of influence probabilities P (u ′ | u, t) for different time zones t of a group.
In this case, the individual tuning pressure calculation unit 12 calculates the individual tuning pressure SP (u | Gma, t).
Further, the group tuning pressure calculation unit 13 calculates a group tuning pressure SP (Gmi | Gma, t).
[Example 23]
It is assumed that the influence probability calculation unit 11 calculates a plurality of influence probabilities P (u ′ | u, c, t) for different predetermined actions c and different time zones t for the group.
In this case, the individual tuning pressure calculation unit 12 calculates the individual tuning pressure SP (u | Gma, c, t).
Further, the group tuning pressure calculation unit 13 calculates a group tuning pressure SP (Gmi | Gma, c, t).

[Category selection unit 14]
The category selection unit 14 extracts an event having a high value among the plurality of group tuning pressures obtained from the group tuning pressure calculation unit 13. Hereinafter, the selection processing will be described in association with the above-described examples 21 to 23.
[Example 21] The category selection unit 14 selects a predetermined action c in which the group tuning pressure SP (Gmi | Gma, c) is higher than a predetermined condition. As a result, it is possible to know the predetermined action c in which the group tuning pressure is high.
[Example 22] The category selection unit 14 selects a time zone t in which the group tuning pressure SP (Gmi | Gma, t) is higher than a predetermined condition. This makes it possible to know the time zone t in which the group tuning pressure is high.
[Example 23] The category selection unit 14 selects a predetermined behavior c and a time zone t in which the group tuning pressure SP (Gmi | Gma, c, t) is higher than a predetermined condition. This makes it possible to know the time zone t in which the group tuning pressure is high for the predetermined action c.

  As described above, if it is possible to know the ranking in descending order of group synchronization pressure, it is possible to adjust the group itself, predetermined behavior, and time zone as a marketing research.

  As described above in detail, according to the program, apparatus and method of the present invention, it is possible to estimate the in-group tuning pressure for a predetermined action. Specifically, for within a group, it may be possible to estimate the tuning pressure that a group of pending users of a particular decision will receive from the group of established users. This is suitable for marketing research based on the influence of human relationships, and can estimate the influence received from other members in the group on the user's usage behavior for a predetermined product service.

  For the various embodiments of the present invention described above, various modifications, corrections and omissions of the scope of the technical idea and aspect of the present invention can be easily made by those skilled in the art. The above description is merely an example and is not intended to be limiting in any way. The present invention is limited only as defined in the following claims and the equivalents thereto.

DESCRIPTION OF SYMBOLS 1 tune pressure estimation apparatus 10 group classification part 11 influence probability calculation part 12 individual tune pressure calculation part 13 group tune pressure calculation part 14 category selection part 2 service server 3 terminal

Claims (9)

A program for causing a computer mounted on a device for estimating tuning pressure from a plurality of other users u ′ for making a decision of a user u in a group to function.
Influence probability calculation means for calculating an influence probability P (u ′ | u) representing positional or attributional proximity for each user for the predetermined action c;
Individual tuning pressure calculation means for calculating an individual tuning pressure SP (u | Gma) received from each other decided user u ′ (= 1 to Gma) that has made a decision for each undecided user u who has not made a decision;
Within a group, a group tuning pressure SP (Gmi | Gma) obtained by multiplying the sum of the individual tuning pressures SP (u | Gma) at all pending users u (= 1 to Gma) by the ratio of the number of pending users to the number of pending users. A program characterized by causing a computer to function as group tuning pressure calculation means for calculating. The individual tuning pressure calculation means calculates the individual tuning pressure SP (u | Gma) as the variance value of the influence probability P (u ′ | u) of the other determined users u ′ with respect to the pending user u in the group becomes smaller. The program according to claim 1, characterized in that the computer is operated to be a large value. The influence probability calculation means calculates a plurality of influence probabilities P (u ′ | u, c) for different predetermined actions c for the group,
The individual tuning pressure calculation means calculates the individual tuning pressure SP (u | Gma, c),
The group tuning pressure calculation means calculates a group tuning pressure SP (Gmi | Gma, c),
The program according to claim 1 or 2, further causing the computer to function as category selection means for selecting a predetermined action c in which the group synchronization pressure SP (Gmi | Gma, c) is higher than a predetermined condition. The influence probability calculation means calculates a plurality of influence probabilities P (u ′ | u, t) for different time zones t with respect to the group;
The individual tuning pressure calculation means calculates the individual tuning pressure SP (u | Gma, t),
The group tuning pressure calculation means calculates a group tuning pressure SP (Gmi | Gma, t),
The program according to claim 1 or 2, further causing the computer to function as category selection means for selecting a time zone t in which the group tuning pressure SP (Gmi | Gma, t) is higher than a predetermined condition. The influence probability calculation means calculates a plurality of influence probabilities P (u ′ | u, c, t) for the predetermined predetermined action c and the different time zones t for the group,
The individual tuning pressure calculation means calculates an individual tuning pressure SP (u | Gma, c, t),
The group tuning pressure calculation means calculates a group tuning pressure SP (Gmi | Gma, c, t),
The computer according to claim 1 or 2, characterized in that the computer is further functioned as a category selection means for selecting a predetermined action c and a time zone t in which the group synchronization pressure SP (Gmi | Gma, c, t) is higher than a predetermined condition. Programs. The program according to claim 3 or 5, wherein the predetermined action c causes the computer to function so as to be a use action based on a commodity service. The program according to any one of claims 1 to 6, wherein the group causes the computer to function so that a plurality of users are clustered based on positional or attributional proximity. . A tuning pressure estimation device for estimating tuning pressure from a plurality of other users u 'for the decision of user u in a group, comprising:
Influence probability calculation means for calculating an influence probability P (u ′ | u) representing positional or attributional proximity for each user for the predetermined action c;
Individual tuning pressure calculation means for calculating an individual tuning pressure SP (u | Gma) received from each other decided user u ′ (= 1 to Gma) that has made a decision for each undecided user u who has not made a decision;
Within a group, a group tuning pressure SP (Gmi | Gma) obtained by multiplying the sum of the individual tuning pressures SP (u | Gma) at all pending users u (= 1 to Gma) by the ratio of the number of pending users to the number of pending users. And a group tuning pressure calculating unit that calculates a tuning pressure estimation unit. A method for tuning pressure estimation of a device for estimating tuning pressure from a plurality of other users u 'for a decision of a user u in a group, comprising:
The device
A first step of calculating an influence probability P (u ′ | u) representing positional or attributional proximity for each user for the predetermined action c;
A second step of calculating an individual tuning pressure SP (u | Gma) received from each other decided user u ′ (= 1 to Gma) who has made a decision for each undecided user u who has not made a decision;
Within a group, a group tuning pressure SP (Gmi | Gma) obtained by multiplying the sum of the individual tuning pressures SP (u | Gma) at all pending users u (= 1 to Gma) by the ratio of the number of pending users to the number of pending users. Calculating the tuning pressure of the device.

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