Encounter rate calculation device, encounter rate calculation method, program, and recording medium

Abstract

To provide an encounter degree calculation device capable of calculating an encounter degree.SOLUTION: In an encounter degree calculation device 10 according to the present invention, an acquisition unit 11 acquires visit information and encounter information; a specification unit 12 specifies continuers from among visitors on the basis of the visit information; an encounter frequency calculation unit 13 calculates the number of encounters of one continuer with the other continuers for each of the other continuers on the basis of the visit information and the encounter information; a coefficient setting unit 14 sets a coefficient to the number of encounters for each of the other continuers on the basis of the number of encounters; a visit frequency calculation unit 15 calculates the number of times of visiting an arbitrary place for each of the other continuers for each time zone on the basis of the visit information; and an encounter degree calculation unit 16 uses the number of visiting times and the coefficient to calculate an encounter degree showing how easily to encounter the other continuers having a few number of times of encountering the one continuer for each of a plurality of time zones.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an encounter rate calculation device, an encounter rate calculation method, a program, and a recording medium. [Background technology]

[0002] As a technology to help users meet each other, for example, Patent Document 1 reports a technology that compares the requested information and personal information of both parties to determine whether they match, and if they match, notifies both parties of the other person's contact information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2000-242657 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology of Patent Document 1 requires that both the request information and personal information match, so there is a problem in that the number of people who can be presented to the user is limited.

[0005] Therefore, an object of the present invention is to provide an encounter degree calculation device, an encounter degree calculation method, a program, and a recording medium capable of calculating an encounter degree indicating the likelihood of meeting other users. [Means for solving the problem]

[0006] In order to achieve the above object, the encounter rate calculation device of the present invention comprises: The system includes an acquisition unit, a specification unit, an encounter count calculation unit, a coefficient setting unit, a visit count calculation unit, and an encounter degree calculation unit, The acquisition unit acquires visit information and encounter information, The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location, The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter, The identification unit identifies a returning attendee from among the plurality of attendees based on the attendance information, The encounter frequency calculation unit calculates the number of encounters between one continuing person and the other continuing person for each continuing person based on the attendance information and the encounter information, The coefficient setting unit sets a coefficient for the number of encounters for each of the other continuing parties based on the number of encounters; The visit frequency calculation unit calculates the number of visits to the arbitrary place by time period for each of the other continuing users based on the visit information, The encounter degree calculation unit is a device that calculates an encounter degree indicating the likelihood of meeting one continuing person who has a smaller number of encounters with the other continuing person for each of a plurality of time periods using the number of visits and the coefficient.

[0007] The encounter rate calculation method of the present invention includes the steps of: The method includes an acquisition step, a specification step, an encounter frequency calculation step, a coefficient setting step, a visit frequency calculation step, and an encounter frequency calculation step, The acquiring step acquires visit information and encounter information, The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location, The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter, The identification step includes identifying a returning attendee from among the plurality of attendees based on the attendance information, The encounter count calculation step calculates the number of encounters between one continuing person and the other continuing person for each continuing person based on the attendance information and the encounter information, The coefficient setting step includes setting a coefficient for the number of encounters for each of the other continuing users based on the number of encounters; The visit frequency calculation step calculates the number of visits to the arbitrary place by time period for each of the other continuing users based on the visit information, The encounter degree calculation step is a method for calculating, for each of a plurality of time periods, an encounter degree indicating the likelihood of meeting the one continuing person with whom the number of encounters is small, using the number of visits and the coefficient. Effect of the Invention

[0008] According to the present invention, it is possible to quantify the ease with which users can meet other users. Also, according to the present invention, it is possible to support users in meeting each other. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing an example of a configuration of an encounter probability calculation device according to the first embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of a hardware configuration of the encounter degree calculation device according to the first embodiment. [Diagram 3] FIG. 3 is a flowchart illustrating an example of processing in the encounter probability calculation device of the first embodiment. [Figure 4] FIG. 4 is a schematic diagram showing an example of a process for identifying a continuing party. [Diagram 5] FIG. 5 is a schematic diagram showing an example of a process related to calculation of the number of encounters. [Figure 6] FIG. 6 is a schematic diagram showing an example of a process related to setting of the coefficient. [Figure 7] FIG. 7 is a schematic diagram showing an example of a process related to calculation of an encounter degree. [Figure 8] FIG. 8 is a schematic diagram showing an example of a process related to calculation of importance. [Figure 9] FIG. 9 is a schematic diagram showing an example of a process related to setting of a coefficient when the importance is calculated. [Figure 10] FIG. 10 is a schematic diagram showing an example of a process related to generation of the aggregated data. [Figure 11]FIG. 11 is a schematic diagram showing an example of a process for setting a prediction formula and a standard error. [Figure 12] FIG. 12 is a schematic diagram showing an example of a process related to calculation of a predicted value. [Figure 13] FIG. 13 is a schematic diagram showing an example of a process for calculating an error between the number of encounters in the aggregated data and a predicted value. [Figure 14] FIG. 14 is a graph showing the results of an analysis of the correlation between the number of visits and the number of encounters. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] In the encounter rate calculation device of the present invention, for example, The coefficient setting unit may be configured to standardize the number of encounters to calculate a standardized variable of the number of encounters, and set a coefficient for the number of encounters based on the following conditions (1) and (2). (1) The coefficient is the value obtained by reversing the sign of the standardized variable. (2) If the value obtained by reversing the sign of the standardized variable is less than or equal to 0, the coefficient is set to 0.

[0011] In the encounter rate calculation device of the present invention, for example, The coefficient setting unit may be configured to set the coefficient to 1 when the number of encounters is equal to or less than a preset number, and to set the coefficient to 0 when the number of encounters is equal to or more than the preset number.

[0012] In the encounter rate calculation device of the present invention, for example, The encounter degree calculation unit calculates the numerical values ​​A1 to A2 for each of the other continuing customers by multiplying the number of visits in the same time period between the other continuing customers by the respective coefficients. 1+n Add up the numbers and get the total A. S Divide by the number of occurrences of the time period within the given period, and the resulting number A m The encounter rate may be set to be the encounter rate.

[0013] The encounter rate calculation device of the present invention, for example, Further, a significance calculation unit is included, The importance calculation unit creates a network between the one continuer and the other continuer, with the one continuer as a center point, based on the number of encounters, and calculates an importance indicating the importance of the other continuer on the network using an arbitrary algorithm; The coefficient setting unit may set a coefficient for the number of encounters for each of the other continuing parties based on the number of encounters and the importance.

[0014] The encounter rate calculation device of the present invention, for example, Further, a filtering unit is included, The acquisition unit acquires at least one of available visit time period information and attribute information, The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location, The attribute information is information indicating attributes of the visitor, The filtering unit filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information, The encounter count calculation unit calculates the encounter count based on the filtered visit information and the filtered encounter information, The visit count calculation unit may calculate, based on the filtered visit information, the number of visits to the arbitrary location for each of the other continuing customers by time period.

[0015] The encounter rate calculation device of the present invention, for example, Further, the recording unit is included. The recording unit may record at least one of the attendance information and the encounter information.

[0016] The encounter rate calculation device of the present invention, for example, Further, the apparatus includes an extracting unit and a notifying unit, The extraction unit extracts a time period that satisfies an arbitrary condition related to the encounter degree from a plurality of time periods, The notification unit may notify the one continuing party of the time period.

[0017] In the encounter rate calculation method of the present invention, for example, The coefficient setting step may be implemented by standardizing the number of encounters to calculate a standardized variance of the number of encounters, and setting a coefficient for the number of encounters based on the following conditions (1) and (2). (1) The coefficient is the value obtained by reversing the sign of the standardized variable. (2) If the value obtained by reversing the sign of the standardized variable is less than or equal to 0, the coefficient is set to 0.

[0018] In the encounter rate calculation method of the present invention, for example, The coefficient setting step may be configured to set the coefficient to 1 when the number of encounters is equal to or less than a preset number, and to set the coefficient to 0 when the number of encounters is equal to or more than the preset number.

[0019] In the encounter rate calculation method of the present invention, for example, The encounter degree calculation step is to calculate the numerical values ​​A1 to A2 for each of the other continuing customers by multiplying the number of visits in the same time period between the other continuing customers by the respective coefficients. 1+n Add up the numbers and get the total A. S Divide by the number of occurrences of the time period within the given period, and the resulting number A m The encounter rate may be set to be the encounter rate.

[0020] The method for calculating the encounter rate of the present invention includes, for example, Further, the method includes a step of calculating an importance level, The importance calculation step creates a network between the one continuer and the other continuer, with the one continuer as a center point, based on the number of encounters, and calculates an importance indicating the importance of the other continuer on the network using an arbitrary algorithm; The coefficient setting step may be implemented by setting a coefficient for the number of encounters for each of the other continuing parties based on the number of encounters and the importance.

[0021] The method for calculating the encounter rate of the present invention includes, for example, Further, a filtering step is included, The acquiring step acquires at least one of available visit time zone information and attribute information, The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location, The attribute information is information indicating attributes of the visitor, The filtering step filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information, The encounter count calculation step calculates the encounter count based on the filtered visit information and the filtered encounter information, The number of visits calculation step may be a step of calculating the number of visits to the arbitrary location by time period for each of the other continuing customers based on the filtered visit information.

[0022] The method for calculating the encounter rate of the present invention includes, for example, Further, a recording step is included, The recording step may be performed in a mode in which at least one of the attendance information and the encounter information is recorded.

[0023] The method for calculating the encounter rate of the present invention includes, for example, Further, the method includes an extraction step and a notification step, The extraction step includes extracting a time period that satisfies any condition related to the encounter rate from a plurality of time periods; The notification step may be implemented in a manner that notifies the one continuing party of the time period.

[0024] The program of the present invention is a program for causing a computer to execute each step of the method of the present invention as a procedure.

[0025] The recording medium of the present invention is a computer-readable recording medium having the program of the present invention recorded thereon.

[0026] In the present invention, an "encounter" is defined as two or more visitors being present at the same time and in the same place, and should be interpreted in the broadest sense, including, for example, passing each other, passing by each other, passing by each other, crossing each other, etc., and is not limited in any sense. Furthermore, an "encounter" is not limited to visitors acknowledging each other, such as by greeting each other or having a conversation.

[0027] Next, an embodiment of the present invention will be described with reference to the drawings. The present invention is not limited to the following embodiment. In each of the drawings, the same parts are given the same reference numerals. In addition, the explanations of each embodiment can be mutually incorporated unless otherwise specified, and the configurations of each embodiment can be combined unless otherwise specified.

[0028] [Embodiment 1] Fig. 1 is a block diagram showing an example of the configuration of an encounter degree calculation device 10 of this embodiment. As shown in Fig. 1, this device 10 includes an acquisition unit 11, a specification unit 12, an encounter count calculation unit 13, a coefficient setting unit 14, a visit count calculation unit 15, and an encounter degree calculation unit 16. In addition, this device 10 may further include an importance calculation unit 17, a filtering unit 18, a recording unit 19, an extraction unit 20, and a notification unit 21 as optional components.

[0029] The device 10 may be, for example, a single device including each of the above-mentioned units, or a device to which each of the above-mentioned units can be connected via a communication line network. The device 10 may be connected to an external device described later via the communication line network. The communication line network is not particularly limited, and a known network may be used, for example, wired or wireless. Examples of the communication line network include an Internet line, a World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), a Local 5G, and a LPWA. Examples of the wireless communication may include a form in which each device directly communicates with each other (Ad Hoc communication), an infrastructure (infrastructure communication), and an indirect communication via an access point. The device 10 may be incorporated into a server as a system, for example. The device 10 may be, for example, a personal computer (PC, for example, desktop type or notebook type) on which the program of the present invention is installed, a smartphone, a tablet terminal, etc. Furthermore, the device 10 may be in the form of cloud computing or edge computing, for example, in which at least one of the units is on a server and the other units are on a terminal.

[0030] 2 illustrates a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, a display device 106, and a communication device 107. The components of the device 10 are connected to each other via the bus 103 and their respective interfaces (I / F).

[0031] The central processing unit 101 is responsible for the overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program of the present invention and other programs, and also reads and writes various information. Specifically, for example, the central processing unit 101 functions as an acquisition unit 11, an identification unit 12, an encounter count calculation unit 13, a coefficient setting unit 14, a visit count calculation unit 15, and an encounter degree calculation unit 16. The central processing unit 101 also functions as, for example, an importance calculation unit 17, a filtering unit 18, a recording unit 19, an extraction unit 20, and a notification unit 21.

[0032] The bus 103 can also be connected to, for example, an external device. Examples of the external device include an external storage device (external database, etc.), a printer, an external input device, an external display device, an external imaging device, etc. The present device 10 can be connected to an external network (the above-mentioned communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

[0033] An example of the memory 102 is a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operation programs, such as the program of the present invention, stored in the storage device 104 described below, and the central processing unit 101 receives data from the memory 102 and executes the program. The main memory is, for example, a RAM (random access memory). The memory 102 may also be, for example, a ROM (read only memory).

[0034] The storage device 104 is also called an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores an operation program including the program of the present invention. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing from and to the recording medium. The recording medium is not particularly limited, and may be, for example, an internal or external type, such as a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or memory card. The storage device 104 may be, for example, a hard disk drive (HDD) in which a recording medium and a drive are integrated, or a solid state drive (SSD).

[0035] In the present device 10, the memory 102 and the storage device 104 can store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 10, and information used when the present device 10 executes processing. At least a part of the information may be stored, for example, in an external server other than the memory 102 and the storage device 104, or may be stored in a distributed manner in multiple terminals using block chain technology or the like. The memory 102 and the storage device 104 may also store, for example, various information recorded by the recording unit 19.

[0036] The device 10 may further include, for example, an input device 105 and a display device 106. The input device 105 is, for example, a touch panel, a keyboard, a mouse, etc. The display device 106 is, for example, an LED display, a liquid crystal display, etc.

[0037] Next, an example of the encounter degree calculation method of this embodiment will be described based on the flowchart of Fig. 3. The encounter degree calculation method of this embodiment is implemented as follows, for example, using the encounter degree calculation device 10 of Fig. 1. Note that the encounter degree calculation method of this embodiment is not limited to use of the encounter degree calculation device 10 of Fig. 1. Also, the steps in parentheses in Fig. 3 are optional steps.

[0038] First, the acquisition unit 11 acquires the visit information and the encounter information (S11a, acquisition step). The visit information is information linking identification information indicating each visitor with time information indicating the time when the visitor arrived at an arbitrary location. The encounter information is information linking the identification information of each visitor encountered at the arbitrary location with time information indicating the time of the encounter. The acquisition unit 11 may acquire the visit information and the encounter information from an external server or an external database via the communication line network, or may acquire the visit information and the encounter information by reading out the visit information and the encounter information stored in the memory 102 and the storage device 104. The identification information may be, for example, identification information of a portable item carried by the visitor when the visitor arrived at the arbitrary location. Examples of the portable item include an admission ticket, an IC (integrated circuit) card, a mobile phone, a smartphone, a wearable device, and the like. The identification information is, for example, an ID (identification), an email address, biometric information (face, fingerprint, voice, and the like), and the like. The arbitrary place is not particularly limited, and may be indoors, outdoors, or inside a moving body such as a train. Specifically, the arbitrary place may be, for example, a garbage station, a park, a supermarket, etc. The visitor may also be, for example, a user.

[0039] Next, the identification unit 12 identifies a continuing visitor from among the multiple visitors based on the attendance information (S12, identification step). The continuing visitor means a visitor who has been visiting the arbitrary location continuously. For example, the identification unit 12 may identify the visitor who has visited the arbitrary location within a certain period going back from a reference date as a continuing visitor based on the attendance information. The reference date is, for example, the date on which the step (S12) was executed. The certain period is not particularly limited and can be set arbitrarily, for example, two weeks, one month, three months, etc. In addition, the identification unit 12 may identify the continuing visitor from the attendance tendency of each visitor, for example, using the technology described in JP 2020-149240 A.

[0040] Next, the encounter count calculation unit 13 calculates the number of encounters between one continuing party and the other continuing party for each continuing party based on the attendance information and the encounter information (S13, encounter count calculation step). Note that the encounter count is, for example, a number equal to or greater than 0. Note that the step (S13) may be executed, for example, in parallel with the step (S12) or before the step (S12).

[0041] Next, the coefficient setting unit 14 sets a coefficient for the number of encounters for each of the other continuing users based on the number of encounters (S14, coefficient setting step). As will be described later, the coefficient setting unit 14 may set the coefficient for the number of encounters by, for example, calculating a standardized variance of the number of encounters.

[0042] Furthermore, the coefficient setting unit 14 may, for example, set the coefficient to 1 when the number of encounters is equal to or less than a preset number, and set the coefficient to 0 when the number of encounters is equal to or more than the preset number. The preset number is not particularly limited and may be set arbitrarily.

[0043] Next, the visit count calculation unit 15 calculates the number of visits to the arbitrary place by time period for each of the other continuing persons based on the visit information (S15, visit count calculation step). The visit count calculation unit 15 may, for example, calculate the number of visits by time period for each of the other continuing persons within the same period as "within a certain period going back from the reference date" in the processing of the identification unit 12, or may calculate the total number of visits to the arbitrary place.

[0044] Then, the encounter degree calculation unit 16 calculates an encounter degree indicating the likelihood of meeting the other continuing person who has a smaller number of encounters with the one continuing person for each of a plurality of time periods using the number of visits and the coefficient (S16, encounter degree calculation step), and ends the process (END). Specifically, the encounter degree calculation unit 16 calculates, for example, the numerical values ​​A1 to A2 for each of the other continuing persons calculated by multiplying the number of visits in the same time period between the other continuing persons by the respective coefficients. 1+n Add up the numbers and get the total A. SDivide by the number of occurrences of the time period within the given period, and the resulting number A m The device 10 may output the encounter degree to another device via the communication line network, for example.

[0045] As described above, the device 10 may further include, for example, an importance calculation unit 17. The importance calculation unit 17 creates a network between the one continuer and the other continuer with the one continuer as a central point based on the number of encounters, and calculates the importance of the other continuer on the network using any algorithm (S17, importance calculation step). The algorithm is not particularly limited, and may be, for example, a page rank algorithm, an eigenvector centrality algorithm, a degree centrality algorithm, or the like. The "importance on the network" is, for example, higher as the number of encountered continuers and the number of encounters with the encountered continuer are larger. The step (S17) may be executed, for example, after the step (S13) and before the step (S14), as shown in FIG. 3.

[0046] The importance calculation unit 17 may calculate the importance using, for example, the number of encounters and the predicted value. Specifically, the importance calculation unit 17 may include, for example, a compilation data generation unit, a setting unit, a predicted value calculation unit, and an error calculation unit. In this case, first, the compilation data generation unit generates compilation data that compilations the number of visits and the number of encounters for each visitor based on the visit information and the encounter information. Since it is considered that the number of encounters increases as the number of visits increases, it can be said that there is a positive correlation between the number of visits and the number of encounters, as described later. Next, the setting unit analyzes the compilation data and sets a prediction formula and a standard error that calculate a predicted value of the number of encounters. Next, the predicted value calculation unit calculates a predicted value of the number of encounters for each visitor using the prediction formula. Next, the error calculation unit calculates an error between the number of encounters in the compilation data and the predicted value for each visitor based on the number of encounters in the compilation data, the predicted value of the number of encounters, and the standard error. Then, the importance calculation unit 17 calculates the importance of the error. This will be specifically described in the third embodiment.

[0047] After executing the step (S17), in the step (S14), the coefficient setting unit 14 sets a coefficient for the number of encounters for each of the other continuing users based on the number of encounters and the importance. Specifically, the coefficient setting unit 14 may set the coefficient to a value calculated by multiplying the standardized variable of the number of encounters by -1 when the value is greater than 0. On the other hand, for example, when the standardized variable of the number of encounters by -1 is less than 0, the coefficient may be set to 0. In this way, by setting a coefficient taking into account the importance, for example, it is possible to increase the encounter rate during a time period when the other continuing users who have a small number of encounters and many acquaintances (other continuing users who have a large number of encounters with the continuing users they have encountered) are likely to encounter.

[0048] As described above, the device 10 may further include, for example, a filtering unit 18. In this case, the acquisition unit 11 acquires at least one of the available visit time zone information and the attribute information (S11b, acquisition step). The available visit time zone information is information indicating the time zone during which the visitor can visit the arbitrary place. The time zone may be, for example, a 30-minute time zone or an hourly time zone. The time zone may also be, for example, a time zone by day of the week. The attribute information is information indicating the attributes of the visitor, and the attributes include, for example, attributes such as gender, age, hobbies, and occupation. The available visit time zone information and the attribute information are, for example, linked to the identification information of the visitor. The acquisition unit 11 may acquire the available visit time zone information and the attribute information from an external server or an external database via the communication line network, or may acquire the available visit time zone information and the attribute information by reading out the available visit time zone information and the attribute information stored in the memory 102 and the storage device 104. The step (S11b) may be performed in parallel with the step (S11a) as shown in Fig. 3, or may be performed in sequence with the step (S11a). The order is not particularly limited.

[0049] After executing the steps (S11a) and (S11b), the filtering unit 18 filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the attribute information (S18, filtering step). In other words, if the time period indicated by the available attendance time period information for visitor X is "Monday to Friday, 14:00 to 17:00", the filtering unit 18 extracts (filters) attendance information and encounter information having time information within the time period "Monday to Friday, 14:00 to 17:00". In addition, if the attribute indicated by the attribute information for visitor Y is "40s, female", the filtering unit 18 extracts (filters) attendance information and encounter information of visitors having attribute information of "40s, female" from the attendance information and encounter information.

[0050] After executing the step (S18), in the step (S13), the encounter count calculation unit 13 calculates the encounter count based on the filtered visit information and the encounter information. Also, in the step (S15), the visit count calculation unit 15 calculates the visit count to the arbitrary place by time period for each of the other continuing users based on the filtered visit information.

[0051] In this way, by filtering the attendance information and the encounter information using the possible attendance time period information, it is possible to specify, for example, a time period with a high encounter probability within the time period when visitor X can visit the arbitrary location. Also, by filtering the attendance information and the encounter information using the attribute information, it is possible to specify, for example, a time period when visitor X is likely to get to know (encounter) people who have the same hobbies or people of the same generation as visitor X.

[0052] As described above, the device 10 may further include, for example, a recording unit 19. The recording unit 19 records information such as the visit information, the encounter information, the available visit time period information, and the attribute information (S19, recording step). The step (S19) may be executed, for example, before the step (S11a) and the step (S11b) as shown in Fig. 3. The method of recording each piece of information is not particularly limited.

[0053] When recording the attendance information, the recording unit 19 may, for example, record the attendance information by reading the identification information of the carried item when the visitor carrying the carried item enters the arbitrary location. Also, when recording the attendance information, the recording unit 19 may, for example, record the attendance information by detecting that the visitor carrying the carried item has entered the arbitrary location using Wi-Fi (registered trademark), Bluetooth (registered trademark), or the like. Furthermore, when recording the attendance information, the recording unit 19 may record the attendance information by detecting that the visitor has entered the arbitrary location using a sensor, a camera, or the like.

[0054] When recording the encounter information, the recording unit 19 may record the encounter information by detecting that a plurality of visitors carrying the portable items have entered the arbitrary location, for example, using Wi-Fi (registered trademark), Bluetooth (registered trademark), etc. Furthermore, when recording the encounter information, the recording unit 19 may, for example, presume that one visitor and the other visitor have encountered each other when another visitor arrives at the arbitrary location within a certain time (for example, within 5 minutes) before or after the arrival of the other visitor, and record the encounter information.

[0055] When recording the visitor possible time zone information and the attribute information, the recording unit 19 may record the visitor possible time zone information and the attribute information, for example, by accepting input of the visitor possible time zone information and the attribute information from the visitor's terminal (e.g., a mobile phone, smartphone, PC, wearable device, etc.).

[0056] As described above, the device 10 may further include, for example, an extraction unit 20 and a notification unit 21. As shown in FIG. 3, for example, following the step (S16), the extraction unit 20 extracts, from among a plurality of time periods, time periods that match an arbitrary condition related to the encounter degree as a time period to be recommended to the one continuing user (S20, extraction step). The arbitrary condition is not particularly limited as long as it is a condition related to the encounter degree, and can be set arbitrarily. Specifically, for example, a preset number of time periods may be extracted in order of high or low encounter degree.

[0057] After executing the step (S20), the notification unit 21 notifies the one continuing visitor of the time period (S21, notification step), and the process ends (END). The method of notification is not particularly limited, and for example, the time period extracted by the extraction unit 20 may be notified to the visitor's terminal, or the time period may be displayed on the display device 106. By notifying the visitor of the time period, the visitor can know the time when it is easy to meet other visitors with whom he or she has had few encounters.

[0058] According to this embodiment, for example, it is possible to calculate the encounter degree indicating the ease of meeting other users, and by using the encounter degree, it is possible to support encounters between users. The technology related to Patent Document 1 requires the trouble of registering information such as request information and personal information in advance, and when there is a change in this information, it is necessary to update it each time. In addition, the technology related to Patent Document 1 extracts and notifies other users recommended to the user based on this information, so there is no diversity in the extracted users. However, this embodiment does not extract "other users", but can extract and notify "time periods" when it is easy to meet other users who have few encounters, and can encourage the user to meet various other users. Then, the user can increase new acquaintances by going to the arbitrary place during the time period when the encounter degree is high. In addition, when a user called an influencer wants to tell other users something by word of mouth, it is possible to efficiently convey it by going to the arbitrary place during the time period when the encounter degree is high.

[0059] [Embodiment 2] An example of the process of identifying a continuing user, calculating the number of encounters, setting a coefficient, calculating the encounter degree, and calculating the importance degree in the present invention will be described using the encounter degree calculation device 10 in Fig. 1. Note that these processes are not limited to the use of the encounter degree calculation device 10 in Fig. 1.

[0060] FIG. 4 is a diagram showing an example of a process for identifying a continuing visitor. An example of the visit information is shown in the upper part of FIG. 4. In the figure, "user" means the identification information of the visitor, and "date" means each of the time information (similarly in other figures). For example, as shown in the middle part of FIG. 4, the identification unit 12 extracts the date on which each of the visitors A to C and Z last visited based on the visit information. Then, the identification unit 12 determines, for example, visitor Z who has not visited within the last two weeks from the reference date (November 24) as a leaver, and determines visitors A to C who have visited within the last two weeks from the reference date (November 24) as continuing visitors. From among them, visitors A to C are identified as continuing visitors. Note that this is just an example and is not limited to this. As described above, the identification unit 12 is required to be able to identify returning visitors from among the multiple visitors, and may identify the returning visitors from the attendance trends of each visitor, for example, using the technology described in Patent Publication 2020-149240.

[0061] FIG. 5 is a diagram showing an example of a process for calculating the number of encounters. An example of the encounter information is shown in the upper part of FIG. 5. For example, in the figure, the encounter information in which "A" is recorded in the "user_1" item, "B" is recorded in the "user_2" item, and "2020-11-20 10:00" is recorded in the "date" item indicates that visitor A and visitor B encountered each other at 10:00 on November 20, 2020. Hereinafter, the drawings and processing of each part from FIG. 5 onwards will be described with a focus on visitor A, but similar processing is also performed for other visitors B to D. For example, as shown in the lower part of FIG. 5, the encounter count calculation unit 13 calculates the number of encounters for visitor A for each of the other visitors B to D based on the encounter information. In addition, the encounter count calculation unit 13 may also calculate the number of encounters based on the visit information, for example, to include "0" in the number of encounters.

[0062] FIG. 6 is a diagram showing an example of a process for setting a coefficient. The "user" and "number of encounters" in the data table shown in FIG. 6 are the same as those in the data table shown in the lower part of FIG. 5. The coefficient setting unit 14 sets a coefficient for the number of encounters for each of the visitors B to D based on the number of encounters in order to, for example, reflect the visiting tendency of a visitor who has a small number of encounters (for example, visitor D) among the visitors B to D more strongly than a visitor who has a large number of encounters (for example, visitors B and C). Note that "small number of encounters" or "large number of encounters" may mean, for example, a larger or smaller relationship compared to a preset number of encounters, or a case where a standardized variate of the number of encounters described later is less than 0 may be referred to as "small number of encounters," and a case where a standardized variate of the number of encounters described later is 0 or more than 0 may be referred to as "large number of encounters." Specifically, the coefficient setting unit 14 may, for example, standardize the number of encounters to calculate the standardized variate of the number of encounters, and set a coefficient for the number of encounters based on the following conditions (1) and (2). In the following conditions (1) and (2), "reverse the sign" means, for example, multiplying by -1. (1) The coefficient is the value obtained by reversing the sign of the standardized variable. (2) If the value obtained by reversing the sign of the standardized variable is less than or equal to 0, the coefficient is set to 0.

[0063] The standardized variate (Z) of the number of encounters is, for example, as shown in equation (2) in FIG. i ) by the average number of encounters (X overline) and then divided by the standard deviation (S) of the number of encounters.

[0064] 6 and the description of the process of the coefficient setting unit 14 based on FIG. 6 are merely examples, and are not limited thereto. As described above, the coefficient setting unit 14 may set the coefficient based on the number of encounters, and may, for example, set the coefficient to 1 when the number of encounters is n or less, or set the coefficient to 0 when the number of encounters is n or more (n=an integer of 1 or more). The coefficient setting unit 14 may also set the coefficient to 0 or more, for example.

[0065] FIG. 7 is a diagram showing an example of a process for calculating the encounter degree. The table data of the visit information shown in FIG. 7 is the same as the visit information shown in the upper part of FIG. 4, and the table data of the coefficient for each visitor shown in FIG. 7 is the same as the table data shown in FIG. 6. The encounter degree calculation unit 16 calculates the encounter degree indicating the ease of meeting the other continued visitor who has a smaller number of encounters with the continued visitor A for each of a plurality of time periods using the number of visits calculated by the number of visits calculation unit 15 and the coefficient set by the coefficient setting unit 14. An example of a table showing the encounter degree for each time period for visitor A is shown in the lower part of FIG. In this table, the darker the color, the higher the encounter degree. Specifically, the encounter degree is calculated, for example, as shown in the figure, by multiplying the total number of visits of each continued visitor B to C by the coefficient of each continued visitor B to C in a formula for calculating the average number of visitors by time period.

[0066] The visit frequency calculation unit 15 may use visit information including visitors other than the continuing visitor, as shown in FIG. 7, for example, or may extract visit information of a continuing visitor (visitor) identified by the identification unit 12 from the visit information including visitors other than the continuing visitor, and use only the extracted visit information.

[0067] FIG. 8 is a diagram showing an example of a process related to the calculation of importance. The encounter information shown in the upper part of FIG. 8 is the same as the encounter information shown in the upper part of FIG. 5. For example, as shown in the middle part of FIG. 8, the importance calculation unit 17 creates a network between one continuing person A and the other continuing persons B to D, with one continuing person A as the center point, based on the number of encounters. In the illustrated network, dots represent each visitor, and the thickness of the line represents the number of encounters. In addition, the importance calculation unit 17 calculates the importance of the other continuing persons B to D, which indicates their importance on the network, using an algorithm such as a page rank algorithm. An example of the number of encounters and the importance for each of the other continuing persons B to D is shown in the data table at the bottom of FIG. 8.

[0068] An example of a process for setting a coefficient when the importance is calculated is shown in Fig. 9. The coefficient setting unit 14 sets a coefficient for the number of encounters for each of the visitors B to D, for example, based on the number of encounters and the importance. Specifically, the coefficient setting unit 14 calculates a standardized variable of the number of encounters in the same manner as described above, for example, as shown in the figure, and when a numerical value obtained by multiplying the standardized variable of the number of encounters by -1 exceeds 0, multiplies the numerical value by the importance and sets the calculated numerical value as the coefficient. On the other hand, the coefficient setting unit calculates a standardized variable of the number of encounters in the same manner as described above, for example, as shown in the figure, and when a numerical value obtained by multiplying the standardized variable of the number of encounters by -1 is 0 or less than 0, sets the coefficient to 0.

[0069] [Embodiment 3] A description will be given of an example in which the importance calculation unit 17 calculates the importance by an algorithm that uses the encounter count and a predicted value. As described above, the process by the importance calculation unit 17 is not limited to this.

[0070] FIG. 10 is a diagram showing an example of processing related to the generation of aggregated data. An example of the visit information and the encounter information is shown in the upper part of FIG. 10. In the figure, "user" means the identification information of the visitor, and "date" means each of the time information. The aggregated data generation unit generates aggregated data by aggregating the number of visits and the number of encounters for each visitor from the visit information and the encounter information, as shown in the middle part of FIG. 10. As described above, since there is a positive correlation between the number of visits and the number of encounters, for example, a scatter diagram as shown in the lower part of FIG. 10 is generated from the aggregated data.

[0071] FIG. 11 is a diagram showing an example of a process for setting a prediction formula and a standard error. The top part of FIG. 11 shows the tabulated data shown in FIG. 10. The setting unit can set a prediction formula and a standard error for the prediction formula by performing a linear regression analysis using the number of encounters in the tabulated data as a target variable and the number of visits as an explanatory variable. The middle part of FIG. 11 shows an example of the prediction formula and the standard error set based on the analysis result by performing a linear regression analysis using the tabulated data shown in the top part of FIG. 11. In other words, the setting unit can be said to perform a process for obtaining a linear approximation shown by a dotted line in the scatter diagram shown in the bottom part of FIG. 11. In this example, the prediction formula is set by an approach for obtaining a linear approximation as described above, but this is an example, and the setting of the prediction formula by the setting unit is not limited to this. The setting unit may set the prediction formula by another method, such as an approach for obtaining a curve approximation. Hereinafter, the setting unit will be described assuming that the setting unit sets the prediction formula by an approach for obtaining a linear approximation.

[0072] Fig. 12 is a diagram showing an example of a process for calculating a predicted value. The aggregated data shown in Fig. 12 is data obtained by adding the predicted value of the encounter count to the aggregated data shown in Fig. 10. The predicted value of the encounter count is calculated by the predicted value calculation unit using the prediction formula shown in the lower part of Fig. 11.

[0073] Fig. 13 is a diagram showing an example of a process for calculating an error between the number of encounters in the aggregated data and the predicted value. The aggregated data shown in Fig. 13 is data obtained by adding an error between the number of encounters and the predicted value to the aggregated data shown in Fig. 12. The error is calculated by the error calculation unit using the standard error shown in the lower part of Fig. 11. Specifically, the error is calculated by the following formula (1), for example, as also shown in Fig. 13. In other words, the error calculation unit can be said to perform a process for calculating a difference between each plot and the linear approximation in a scatter diagram shown in the lower part of Fig. 13. Error = (number of encounters – predicted number of encounters) / standard error (1)

[0074] Then, the importance calculation section 17 determines the error calculated by the above formula (1) as the importance.

[0075] [Embodiment 4] Based on the results of the verification carried out by the present inventors, the correlation between the number of visits and the number of encounters will be described.

[0076] In this verification, a recording device that records the visitor information was installed at the entrance of the experimental site. The recording device records the identification information of the visitor's terminal (smartphone) when the visitor holds the terminal over the device. In this verification, visitors who visited the site less than 5 minutes apart were determined to have "encountered" each other, and the number of visits and the number of encounters were calculated for each visitor. Then, the correlation coefficient between the number of visits and the number of encounters was calculated, and the correlation was analyzed. Figure 14 shows the results of the analysis of the correlation between the number of visits and the number of encounters. As shown in Figure 14, it was found that there is a strong positive correlation between the number of visits and the number of encounters.

[0077] [Embodiment 5] The program of the present embodiment is a program for causing a computer to execute each step of the method of the present invention as a procedure. In the present invention, the "procedure" may be read as "processing." The program of the present embodiment may be recorded, for example, in a computer-readable recording medium. The recording medium is not particularly limited, and examples thereof include a read-only memory (ROM), a hard disk (HD), and an optical disk.

[0078] Although the present invention has been described above with reference to the embodiment, the present invention is not limited to the above embodiment. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0079] <Additional Notes> Some or all of the above embodiments may be described as follows, but are not limited to the following supplementary notes. (Appendix 1) The system includes an acquisition unit, a specification unit, an encounter count calculation unit, a coefficient setting unit, a visit count calculation unit, and an encounter degree calculation unit, The acquisition unit acquires visit information and encounter information, The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location, The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter, The identification unit identifies a returning attendee from among the plurality of attendees based on the attendance information, The encounter frequency calculation unit calculates the number of encounters between one continuing person and the other continuing person for each continuing person based on the attendance information and the encounter information, The coefficient setting unit sets a coefficient for the number of encounters for each of the other continuing parties based on the number of encounters; The visit frequency calculation unit calculates the number of visits to the arbitrary place by time period for each of the other continuing users based on the visit information, The encounter degree calculation unit uses the number of visits and the coefficient to calculate an encounter degree indicating the likelihood of meeting one continuing person who has a smaller number of encounters with the other continuing person for each of a plurality of time periods. (Appendix 2) The encounter rate calculation device according to claim 1, wherein the coefficient setting unit standardizes the number of encounters to calculate a standardized variance of the number of encounters, and sets a coefficient for the number of encounters based on the following conditions (1) and (2). (1) The coefficient is the value obtained by reversing the sign of the standardized variable. (2) If the value obtained by reversing the sign of the standardized variable is less than or equal to 0, the coefficient is set to 0. (Appendix 3) The encounter rate calculation device according to claim 1, wherein the coefficient setting unit sets the coefficient to 1 when the number of encounters is less than or equal to a predetermined number, and sets the coefficient to 0 when the number of encounters is greater than or equal to the predetermined number. (Appendix 4) The encounter degree calculation unit calculates the numerical values ​​A1 to A2 for each of the other continuing customers by multiplying the number of visits in the same time period between the other continuing customers by the respective coefficients. 1+n Add up the numbers and get the total A. S Divide by the number of occurrences of the time period within the given period, and the resulting number A m The encounter rate calculation device according to any one of claims 1 to 3, wherein the encounter rate is (Appendix 5) Further, a significance calculation unit is included, The importance calculation unit creates a network between the one continuer and the other continuer, with the one continuer as a center point, based on the number of encounters, and calculates an importance indicating the importance of the other continuer on the network using an arbitrary algorithm; 5. The encounter degree calculation device according to claim 1, wherein the coefficient setting unit sets a coefficient for the number of encounters for each of the other continuing parties based on the number of encounters and the importance. (Appendix 6) Further, a filtering unit is included, The acquisition unit acquires at least one of available visit time period information and attribute information, The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location, The attribute information is information indicating attributes of the visitor, The filtering unit filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information, The encounter count calculation unit calculates the encounter count based on the filtered visit information and the filtered encounter information, The encounter rate calculation device according to any one of appendices 1 to 5, wherein the visit frequency calculation unit calculates the number of visits to the arbitrary location by time period for each of the other continuing users based on the filtered visit information. (Appendix 7) Further, the recording unit is included. 7. The encounter degree calculation device according to claim 1, wherein the recording unit records at least one of the attendance information and the encounter information. (Appendix 8) Further, the apparatus includes an extracting unit and a notifying unit, The extraction unit extracts a time period that satisfies an arbitrary condition related to the encounter degree from a plurality of time periods, 8. The encounter probability calculation device according to claim 1, wherein the notification unit notifies the one continuing party of the time period. (Appendix 9) The method includes an acquisition step, a specification step, an encounter frequency calculation step, a coefficient setting step, a visit frequency calculation step, and an encounter frequency calculation step, The acquiring step acquires visit information and encounter information, The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location, The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter, The identification step includes identifying a returning attendee from among the plurality of attendees based on the attendance information, The encounter count calculation step calculates the number of encounters between one continuing person and the other continuing person for each continuing person based on the attendance information and the encounter information, The coefficient setting step includes setting a coefficient for the number of encounters for each of the other continuing users based on the number of encounters; The visit frequency calculation step calculates the number of visits to the arbitrary place by time period for each of the other continuing users based on the visit information, The encounter degree calculation process uses the number of visits and the coefficient to calculate an encounter degree indicating the likelihood of meeting one continuing person who has a smaller number of encounters with the other continuing person for each of a plurality of time periods. (Appendix 10) The encounter frequency calculation method according to claim 9, wherein the coefficient setting step standardizes the number of encounters to calculate a standardized variance of the number of encounters, and sets a coefficient for the number of encounters based on the following conditions (1) and (2). (1) The coefficient is the value obtained by reversing the sign of the standardized variable. (2) If the value obtained by reversing the sign of the standardized variable is less than or equal to 0, the coefficient is set to 0. (Appendix 11) The encounter rate calculation method according to claim 9, wherein the coefficient setting step sets the coefficient to 1 if the number of encounters is less than or equal to a predetermined number, and sets the coefficient to 0 if the number of encounters is greater than or equal to the predetermined number. (Appendix 12) The encounter degree calculation step is to calculate the numerical values ​​A1 to A2 for each of the other continuing customers by multiplying the number of visits in the same time period between the other continuing customers by the respective coefficients. 1+n Add up the numbers and get the total A. S Divide by the number of occurrences of the time period within the given period, and the resulting number A m 12. The method for calculating an encounter rate according to any one of claims 9 to 11, wherein the encounter rate is (Appendix 13) Further, the method includes a step of calculating an importance level, The importance calculation step creates a network between the one continuer and the other continuer, with the one continuer as a center point, based on the number of encounters, and calculates an importance indicating the importance of the other continuer on the network using an arbitrary algorithm; 13. The encounter degree calculation method according to any one of appendices 9 to 12, wherein the coefficient setting step sets a coefficient for the number of encounters for each of the other continuing parties based on the number of encounters and the importance. (Appendix 14) Further, a filtering step is included, The acquiring step acquires at least one of available visit time zone information and attribute information, The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location, The attribute information is information indicating attributes of the visitor, The filtering step filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information, The encounter count calculation step calculates the encounter count based on the filtered visit information and the filtered encounter information, The encounter frequency calculation method according to any one of appendices 9 to 13, wherein the visit frequency calculation step calculates the number of visits to the arbitrary location by time period for each of the other continuing users based on the filtered visit information. (Appendix 15) Further, a recording step is included, The encounter rate calculation method according to any one of appendices 9 to 14, wherein the recording step records at least one of the attendance information and the encounter information. (Appendix 16) Further, the method includes an extraction step and a notification step, The extraction step includes extracting a time period that satisfies any condition related to the encounter rate from a plurality of time periods; 16. The method for calculating an encounter probability according to any one of claims 9 to 15, wherein the notification step notifies the one continuing party of the time period. (Appendix 17) A program for causing a computer to execute procedures including an acquisition procedure, a specification procedure, an encounter count calculation procedure, a coefficient setting procedure, a visit count calculation procedure, and an encounter degree calculation procedure: The acquisition step acquires visit information and encounter information, The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location, The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter, The identification step includes identifying a returning visitor from among the plurality of visitors based on the attendance information; The encounter count calculation step calculates the number of encounters between one continuing person and the other continuing person for each continuing person based on the attendance information and the encounter information; the coefficient setting step includes setting a coefficient for the number of encounters for each of the other continuing users based on the number of encounters; The visit frequency calculation step calculates the number of visits to the arbitrary place by time period for each of the other continuing users based on the visit information; The encounter degree calculation step calculates, for each of a plurality of time periods, an encounter degree indicating the likelihood of meeting the one continuing person with whom the one continuing person has a smaller number of encounters, using the number of visits and the coefficient. (Appendix 18) The program described in Appendix 17, wherein the coefficient setting step standardizes the number of encounters to calculate a standardized variance of the number of encounters, and sets a coefficient for the number of encounters based on the following conditions (1) and (2). (1) The coefficient is the value obtained by reversing the sign of the standardized variable. (2) If the value obtained by reversing the sign of the standardized variable is less than or equal to 0, the coefficient is set to 0. (Appendix 19) The program of claim 17, wherein the coefficient setting step sets the coefficient to 1 if the number of encounters is less than or equal to a preset number, and sets the coefficient to 0 if the number of encounters is greater than or equal to the preset number. (Appendix 20) The encounter degree calculation step is to calculate the numerical values ​​A1 to A2 for each of the other continuing customers by multiplying the number of visits in the same time period between the other continuing customers by the respective coefficients. 1+n Add up the numbers and get the total A. S Divide by the number of occurrences of the time period within the given period, and the resulting number A m 20. The program according to any one of appendices 17 to 19, wherein the encounter rate is (Appendix 21) Further, a procedure for calculating importance is included, The importance calculation step includes creating a network between the one continuer and the other continuer, with the one continuer as a center point, based on the number of encounters, and calculating an importance indicating the importance of the other continuer on the network using an arbitrary algorithm; 21. The program according to any one of appendices 17 to 20, wherein the coefficient setting step sets a coefficient for the number of encounters for each of the other continuers based on the number of encounters and the importance. (Appendix 22) Further comprising a filtering step, The acquisition step acquires at least one of available visit time zone information and attribute information, The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location, The attribute information is information indicating attributes of the visitor, the filtering step filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information; The encounter count calculation step calculates the encounter count based on the filtered visit information and the filtered encounter information, The program described in any one of Appendices 17 to 21, wherein the visit count calculation step calculates the number of visits to the arbitrary location by time period for each of the other continuing users based on the filtered visit information. (Appendix 23) Further, the method includes a recording procedure. 23. The program described in any one of Appendices 17 to 22, wherein the recording step records at least one of the visit information and the encounter information. (Appendix 24) Further, the method includes an extraction step and a notification step, The extraction step includes extracting a time period that satisfies any condition related to the encounter degree from a plurality of time periods; The program described in any one of appendices 17 to 23, wherein the notification procedure notifies the one of the continuing parties of the time period. (Appendix 25) A computer-readable recording medium having a program according to any one of appendices 17 to 24 recorded thereon. [Industrial Applicability]

[0080] According to the present invention, it is possible to calculate the encounter rate. Therefore, the present invention is useful, for example, in supporting encounters between users. [Explanation of symbols]

[0081] 10 Encounter degree calculation device 11 Acquisition Department 12 Specific part 13 Encounter Count Calculation Section 14 Coefficient setting section 15 Number of visitors 16 Encounter degree calculation part 17 Importance calculation part 18 Filtering section 19 Recording Section 20 Extraction part 21 Notification Department 101 Central Processing Unit 102 Memory 103 Bus 104 Storage device 105 Input Device 106 Display device 107 Communication Devices

Claims

1. The system includes an acquisition unit, a specification unit, an encounter count calculation unit, a coefficient setting unit, a visit count calculation unit, and an encounter degree calculation unit, The acquisition unit acquires visit information and encounter information, The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location, The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter, The identification unit identifies a returning attendee from among the plurality of attendees based on the attendance information, The encounter count calculation unit calculates the number of encounters between one continuing person and the other continuing person for each of the other continuing persons based on the attendance information and the encounter information, The coefficient setting unit sets a coefficient for the number of encounters for each of the other continuing parties based on the number of encounters; The visit frequency calculation unit calculates the number of visits to the arbitrary place by time period for each of the other continuing users based on the visit information, The encounter degree calculation unit uses the number of visits and the coefficient to calculate an encounter degree indicating the likelihood of meeting one continuing person who has a smaller number of encounters with the other continuing person, for each of a plurality of time periods.

2. 2. The encounter frequency calculation device according to claim 1, wherein the coefficient setting unit standardizes the encounter frequency to calculate a standardized variance of the encounter frequency, and sets a coefficient for the encounter frequency based on the following conditions (1) and (2): (1) The coefficient is determined by reversing the sign of the standardized variable. (2) If the value obtained by reversing the sign of the standardized variable is equal to or less than 0, the coefficient is set to 0.

3. The encounter degree calculation unit calculates the numerical value A for each of the other continuing customers by multiplying the number of visits in the same time period between the other continuing customers by the respective coefficients. １ ~A １＋ｎ The sum of these is added up to obtain the value A Ｓ Divide by the number of occurrences of the time period within a certain period, and the resulting number A ｍ 3. The encounter rate calculation device according to claim 1, wherein the encounter rate is set to be:

4. Further, a significance calculation unit is included, The importance calculation unit creates a network between the one continuer and the other continuer, with the one continuer as a center point, based on the number of encounters, and calculates an importance indicating the importance of the other continuer on the network using an arbitrary algorithm; The encounter degree calculation device according to claim 1 , wherein the coefficient setting unit sets a coefficient for the number of encounters for each of the other continuing parties based on the number of encounters and the importance.

5. Further, a filtering unit is included, The acquisition unit acquires at least one of available visit time period information and attribute information, The available visit time period information is information indicating a time period during which the visitor can visit the arbitrary location, The attribute information is information indicating attributes of the visitor, The filtering unit filters the attendance information and the encounter information using at least one of the acquired available attendance time period information and the acquired attribute information, The encounter count calculation unit calculates the encounter count based on the filtered visit information and the filtered encounter information, The encounter degree calculation device according to any one of claims 1 to 4, wherein the visit frequency calculation unit calculates the number of visits to the arbitrary location by time period for each of the other continuing persons based on the filtered visit information.

6. Further, the recording unit is included. The encounter probability calculation device according to claim 1 , wherein the recording unit records at least one of the attendance information and the encounter information.

7. Further, the apparatus includes an extracting unit and a notifying unit, The extraction unit extracts a time period that satisfies an arbitrary condition related to the encounter degree from a plurality of time periods, The encounter probability calculation device according to claim 1 , wherein the notification unit notifies the one continuing party of the extracted time period.

8. The method includes an acquisition step, a specification step, an encounter frequency calculation step, a coefficient setting step, a visit frequency calculation step, and an encounter frequency calculation step, The acquiring step acquires visit information and encounter information, The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location, The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter, The identification step includes identifying a returning attendee from among the plurality of attendees based on the attendance information, The encounter count calculation step calculates the number of encounters between one continuing person and another continuing person for each of the other continuing persons based on the attendance information and the encounter information, The coefficient setting step includes setting a coefficient for the number of encounters for each of the other continuing users based on the number of encounters; The visit frequency calculation step calculates the number of visits to the arbitrary place by time period for each of the other continuing users based on the visit information, The encounter degree calculation process uses the number of visits and the coefficient to calculate an encounter degree indicating the likelihood of meeting one continuing person who has a smaller number of encounters with the other continuing person for each of a plurality of time periods.

9. A program for causing a computer to execute procedures including an acquisition procedure, a specification procedure, an encounter count calculation procedure, a coefficient setting procedure, a visit count calculation procedure, and an encounter rate calculation procedure: The acquisition step acquires visit information and encounter information, The attendance information is information linking identification information indicating each attendee with time information indicating the time when the attendee arrived at any location, The encounter information is information linking the identification information of each of the visitors encountered at the arbitrary location with time information indicating the time of the encounter, The identification step includes identifying a returning visitor from among the plurality of visitors based on the attendance information; The encounter count calculation step calculates the number of encounters between one continuing person and another continuing person for each of the other continuing persons based on the attendance information and the encounter information; the coefficient setting step includes setting a coefficient for the number of encounters for each of the other continuing users based on the number of encounters; The visit frequency calculation step calculates the number of visits to the arbitrary place by time period for each of the other continuing users based on the visit information; The encounter degree calculation step calculates, for each of a plurality of time periods, an encounter degree indicating the likelihood of meeting the one continuing person with whom the one continuing person has a smaller number of encounters, using the number of visits and the coefficient.

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