JP5416474B2 - User management method and information processing system - Google Patents

Description

  The present invention relates to a user management method and an information processing system for grasping a user's lifestyle.

  Conventionally, various methods for providing a service that matches a user are known. For example, Patent Document 1 below describes a system that provides advertisement information based on a user's lifestyle and behavior pattern. This system estimates the user's interest based on historical information including search keywords entered in the past, website access status, and referenced product information, and delivers advertisement information based on the estimation result To decide. Moreover, estimating a user's lifestyle based on the questionnaire result to a user is also performed.

JP 2008-97545 A

  However, when the questionnaire result is used, problems such as a decrease in reliability due to subjective evaluation and a burden on the user who answers the questionnaire arise. Moreover, it can be said that the method described in Patent Document 1 requires a user to input a keyword and access to a site, which places a burden on the user.

  The present invention has been made to solve the above-described problem, and an object of the present invention is to provide a user management method and an information processing system that can objectively obtain a person's lifestyle without imposing a burden on the user. And

The user management method of the present invention is a user management method executed by the information processing system, the position acquisition step for acquiring position information indicating the position of the measured mobile device, and the position information acquired in the position acquisition step An area estimation step for estimating the area where the user of the mobile device stayed, an area, an area concept indicating the overall characteristics of the area, a lifestyle factor characterizing the lifestyle of the user, and the area concept And an extraction step for extracting data corresponding to the area estimated in the area estimation step, from the first storage means for storing the association degree of the lifestyle factor in association with each other, based on the data extracted in the extraction step, Lifestyle factors that adapt to the area estimated in the area estimation step A lifestyle corresponding to the lifestyle factor estimated in the factor estimation step is extracted from the factor estimation step for estimating the data and the second storage means for storing the lifestyle factor and the lifestyle in association with each other, and the extracted life A lifestyle estimating step of estimating the style as the lifestyle of the user of the mobile device.
In the user management method of the present invention, in the factor estimating step, the probability of each lifestyle factor corresponding to the one or more area concepts is calculated from one or more degrees of association corresponding to the one or more area concepts estimated. The combination of lifestyle factors having the highest probability may be obtained as the estimation result.

In addition, the information processing system of the present invention includes a position acquisition unit that acquires position information indicating the position of the measured mobile device, and an area where the user of the mobile device stayed based on the position information acquired by the position acquisition unit. Area estimation means for estimating the area, an area, an area concept indicating the overall characteristics of the area, a lifestyle factor that characterizes the lifestyle of the user, and an association between the area concept and the lifestyle factor The first storage means for storing and the lifestyle corresponding to the area estimated by the area estimation means based on the extracted data extracted from the first storage means corresponding to the area estimated by the area estimation means A factor estimation means for estimating a factor, and a lifestyle factor and a lifestyle are stored in association with each other. A storage means, and a lifestyle estimation means for extracting a lifestyle corresponding to the lifestyle factor estimated by the factor estimation means from the second storage means, and estimating the extracted lifestyle as the lifestyle of the user of the mobile device; Is provided.
In the information processing system of the present invention, the factor estimating means calculates the probability of each lifestyle factor corresponding to the one or more area concepts from one or more degrees of association corresponding to the one or more area concepts estimated. The combination of lifestyle factors having the highest probability may be obtained as the estimation result.

  According to such a user management method and information processing system, first, an area where a user stays is estimated based on position information (positioning result) of the mobile device. Subsequently, data relating to the area concept, lifestyle factor, and relevance of the area is extracted, and based on the data, a lifestyle factor adapted to the stay area is estimated. Then, the lifestyle corresponding to the estimated factor is extracted and determined as the user's lifestyle. In this way, the user's lifestyle is estimated based on the location information of the mobile device and the information stored in the storage means in advance, so that the person's lifestyle can be objectively determined without imposing a burden on the user. Can be obtained.

  In the user management method of the present invention, the lifestyle factor estimated in the factor estimation step, the storage step for storing in the third storage means in association with the identification information for identifying the user, and the data stored in the first storage means And the degree of association between the area concept and the lifestyle factor is calculated based on the data stored in the third storage unit, and the degree of association stored in the first storage unit is updated with the calculated degree of association. And a relevance degree updating step.

  In this case, the degree of association is calculated and updated based on the estimated lifestyle factor. Thereby, the relevance degree which matched according to the actual condition is set, As a result, the precision of the estimation of a lifestyle can be improved.

  According to such a user management method and information processing system, since the user's lifestyle is estimated based on the location information of the mobile device and information stored in advance, the user's lifestyle can be estimated without imposing a burden on the user. You can get a lifestyle objectively.

It is a figure which shows the function structure of the lifestyle estimation system which concerns on embodiment. It is a figure which shows the hardware constitutions of the moving apparatus shown in FIG. It is a figure which shows the hardware constitutions of the estimation server shown in FIG. It is a figure which shows the area | region shown by position information. It is a figure which shows the example of a user's stay or movement. It is a figure which shows the example of the stay area information memorize | stored in the stay area storage part of FIG. It is a figure which shows the example of the area attribute information memorize | stored in the area attribute storage part of FIG. It is a figure which shows the example of the area concept information memorize | stored in the area concept storage part of FIG. It is a figure which shows the example of the relevant degree information memorize | stored in the relevant degree storage part of FIG. It is a figure which shows the example of the information memorize | stored in the lifestyle storage part of FIG. It is a flowchart which shows the stay area estimation process performed by the estimation system shown in FIG. It is a flowchart which shows the lifestyle estimation process performed by the estimation system shown in FIG. It is a flowchart which shows the relevance degree update process performed by the estimation system shown in FIG.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this embodiment, the information processing system according to the present invention is applied to a lifestyle estimation system (hereinafter simply referred to as “estimation system”). In the description of the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant description is omitted.

  First, the function and configuration of the estimation system 1 according to the embodiment will be described with reference to FIGS.

  The estimation system 1 is a computer system that estimates the lifestyle of a user who owns a mobile device, and includes a mobile device 10 and an estimation server 20. The mobile device 10 and the estimation server 20 can communicate with each other via a network (not shown) (for example, a network including a mobile communication network and the Internet). Examples of the mobile device 10 include, but are not limited to, a mobile phone, a personal digital assistant (PDA), a personal computer, and a portable game machine. Although only one mobile device 10 is shown in FIG. 1, the estimation system 1 may include a plurality of mobile devices 10.

  Next, the functional configuration of the mobile device 10 will be described. As shown in FIG. 1, the mobile device 10 includes a positioning unit 11, a communication unit 12, and a display unit 13 as functional components.

  As shown in FIG. 2, the mobile device 10 includes a CPU 101 that executes an operating system, an application program, and the like, a main storage unit 102 that includes a ROM and a RAM, an auxiliary storage unit 103 that includes a memory, The communication control unit 104 performs data communication, the monitor unit 105 includes a liquid crystal monitor, and the operation unit 106 includes input keys. Each function of the mobile device 10 reads predetermined software on the CPU 101 and the main storage unit 102, operates the communication control unit 104 under the control of the CPU 101, and controls the main storage unit 102 and the auxiliary storage unit 103. This is realized by reading and writing data.

  Returning to FIG. 1, the positioning unit 11 is a part that measures the current position of the mobile device 10 and generates position information indicating the position. Specifically, the positioning unit 11 performs positioning using the global positioning system (GPS) and base station information, and generates position information including a user ID, measurement time, latitude / longitude, and positioning error. Here, the user ID is information for identifying the user of the mobile device 10. The positioning unit 11 outputs the generated position information to the communication unit 12. The positioning unit 11 executes such a series of processes at predetermined time intervals.

  The positioning unit 11 may generate relative position information obtained by positioning using a wireless LAN, an IC tag (Radio Frequency IDentification: RFID), Bluetooth (Bluetooth (registered trademark)), or the like.

  The communication unit 12 is a part that transmits and receives various data to and from the estimation server 20. Specifically, the communication unit 12 transmits the position information input from the positioning unit 11, receives lifestyle information described later, and outputs it to the display unit 13.

  The display unit 13 is a part that displays lifestyle information input from the communication unit 12 on a monitor.

  Next, the functional configuration of the estimation server 20 will be described. As shown in FIG. 1, the estimation server 20 includes, as functional components, a stay determination unit (position acquisition unit) 21, an area estimation unit (area estimation unit) 22, a stay area storage unit 23, an area attribute storage unit (first memory). Means) 24, area concept storage section (first storage means) 25, relevance degree storage section (first storage means) 26, relevance degree learning section 27, factor estimation section (factor estimation means) 28, lifestyle storage section (first 2 storage means) 29, a lifestyle estimation unit (lifestyle estimation means) 30, and an estimation result storage unit 31.

  As shown in FIG. 3, the estimation server 20 includes a CPU 201 that executes an operating system, an application program, and the like, a main storage unit 202 that includes a ROM and a RAM, an auxiliary storage unit 203 that includes a hard disk, The communication control unit 204 includes a network card, an input unit 205 such as a keyboard and a mouse, and an output unit 206 such as a monitor. Each function of the estimation server 20 reads predetermined software on the CPU 201 and the main storage unit 202, operates the communication control unit 204 under the control of the CPU 201, and stores data in the main storage unit 202 and the auxiliary storage unit 203. This is realized by reading and writing.

  Returning to FIG. 1, the stay determination unit 21 is a part that determines whether or not the user of the mobile device 10 is staying in an area of a certain range.

  The stay determination unit 21 receives (acquires) and accumulates the position information sent from the mobile device 10. Subsequently, the stay determination unit 21 determines whether or not there are portions that overlap each other continuously in time among the plurality of regions indicated by the recently acquired predetermined amount of position information. Here, the region indicated by the position information is a rectangular region A having a latitude and longitude p as the center and one side being twice the positioning error e as shown in FIG. Hereinafter, a portion where the areas indicated by the position information overlap with each other is referred to as an “AND area”.

  For example, when the areas A1 to A7 at the times t1 to t7 are spatially arranged as shown in FIG. 5, the stay determination unit 21 sets the AND area B where the areas A3 to A5 overlap in the time period t3 to t5. It is determined that the user is staying, and it is determined that the user is moving in other time zones t1 to t3 and t5 to t7. In FIG. 5, the movement of the user is schematically indicated by an arrow Y.

  Subsequently, the stay determination unit 21 outputs a plurality of pieces of position information corresponding to the areas constituting the AND area to the area estimation unit 22. In the example of FIG. 5, the stay determination unit 21 outputs three pieces of position information corresponding to the areas A <b> 3 to A <b> 5 to the area estimation unit 22. If the AND area does not exist, the stay determination unit 21 ends the process at that time.

  In addition, the timing which the stay determination part 21 performs the said process is not limited. For example, the stay determination unit 21 may start the process upon receiving the location information, or may start the process at a predetermined time interval or at a predetermined time.

  The area estimation unit 22 is a part that estimates the area where the user of the mobile device 10 stayed based on the position information input from the stay determination unit 21. Specifically, the area estimation unit 22 determines the area indicated by the smallest positioning error among the plurality of input position information, that is, the position information constituting the AND area, as the stay area. Moreover, the area estimation part 22 calculates a stay time based on the input positional information. Then, the area estimation unit 22 outputs the stay area information including the user ID, the position information corresponding to the stay area, the stay start time, the stay end time, and the stay time to the stay area storage unit 23.

  In the example of FIG. 5, since the positioning error of the area A4 is the smallest among the areas A3 to A5, the area estimation unit 22 sets the area A4 indicated by the position information at time t4 as the stay area. Moreover, the area estimation part 22 calculates the time from stay start time t3 to stay end time t5 as stay time.

  The stay area storage unit 23 is a part that stores the stay area information input from the area estimation unit 22. An example of information stored in the stay area storage unit 23 is shown in FIG. In the example of FIG. 6, three stay area information is stored for the user indicated by the user ID “u001”. Of course, the stay area storage unit 23 can store stay area information for a plurality of users.

  The area attribute storage unit 24 stores information related to area attributes (hereinafter referred to as “area attribute information”). For example, the area attribute storage unit 24 stores area attribute information as shown in FIG. 7 in advance. The area attribute information in FIG. 7 includes an area range expressed by a plurality of latitudes and longitudes, place names, information indicating area characteristics, and information on facilities in the area. Here, the area characteristics include “sea”, “mountain”, “residential area”, “office town”, etc. in addition to “business district”, but are not limited thereto. In addition, the facility information includes information on an arbitrary artifact or natural object that can be a landmark. The constituent elements of the area attribute information are not limited to those shown in FIG. 7, and various modifications are possible.

  The area concept storage unit 25 is a part that stores information on the concept of a predetermined area (hereinafter referred to as “area concept information”). Here, the area concept refers to the general characteristics of the area, such as a noun concept (eg “convenience store”, “family restaurant”), an adjective concept (eg “fashionable”, “advanced”, “high class”). ), Verb concepts (for example, “eat”, “wear”, “play”, “learn”) and the like. For example, the area concept storage unit 25 stores area concept information as shown in FIG. 8 in advance. As shown in FIG. 8, a plurality of area concepts may be associated with one area.

  The degree-of-association storage unit 26 is a part that stores degree-of-association information indicating the degree of association between the lifestyle factor that characterizes the lifestyle of the user and the area concept. Here, the lifestyle is an index representing the user's lifestyle, the personality or values of the person. The lifestyle factor is a factor constituting the lifestyle, and examples thereof include “fashion”, “art”, and “ecology”. The degree of association is an index indicating the strength of the relationship between the lifestyle factor and the area concept, and the higher this number, the stronger the relationship between the two. For example, the relevance degree storage unit 26 stores relevance degree information as shown in FIG. 9 in advance. As shown in FIG. 9, a plurality of area concepts and relevance levels may be associated with one lifestyle factor.

  The area attribute information, area concept information, and relevance information can be associated with each other. Therefore, it can be said that the area attribute storage unit 24, the area concept storage unit 25, and the association degree storage unit 26 constitute a first storage unit.

  The degree-of-association learning unit 27 is a part that learns and updates the degree of association between the lifestyle factor and the area concept. The relevance learning unit 27 extracts a user group associated with each lifestyle factor from the estimation result storage unit 31. Subsequently, the degree-of-association learning unit 27 extracts a corresponding area concept from the area concept storage unit 25 for each user group. Then, the degree-of-association learning unit 27 calculates the degree of association between the lifestyle factor and the extracted area concept using a predetermined learning method (algorithm). For example, the relevance level learning unit 27 may calculate the relevance level using an existing method such as a maximum likelihood estimation method, a posterior distribution optimization method, or a TF / IDF (Term Frequency-Inverse Document Frequency) method.

Further, the relevance learning unit 27 may calculate the relevance R _{i, j} between the area concept C _i and the lifestyle factor F _j by the following equation (1). In this specification, this method is referred to as a TP / ICF (Term Probability-Inverse Category Frequency) method.

When a sufficient number of data necessary for learning the relevance level cannot be obtained, the relevance level learning unit 27 uses the following formula (based on the co-occurrence obtained using the web search engine: The degree of association R _{i, j} may be calculated according to 2).

  The relevance learning unit 27 outputs the relevance calculated by any of the above methods to the relevance storage unit 26 as relevance information. Thereby, the relevance level information in the relevance level storage unit 26 is updated to a content more suitable for the actual situation.

  The timing at which the relevance learning unit 27 executes the above process is not limited. For example, the association degree learning unit 27 may start the above process at a predetermined time interval or at a predetermined time.

  The factor estimation unit 28 extracts data corresponding to the stay area estimated by the area estimation unit 22 from the stay area storage unit 23, the area attribute storage unit 24, the area concept storage unit 25, and the association degree storage unit 26. This is a part for estimating the lifestyle factor adapted to the stay area based on the data.

  The factor estimation unit 28 reads the stay area information generated by the area estimation unit 22 from the stay area storage unit 23 and reads the area attribute information corresponding to the information from the area attribute storage unit 24. Subsequently, the factor estimation unit 28 reads one or more area concept information corresponding to the read area attribute information from the area concept storage unit 25. Subsequently, the factor estimating unit 28 reads one or more corresponding degree-of-association information from the degree-of-association storage unit 26 for each read area concept information.

Subsequently, the factor estimation unit 28 substitutes each read data into the following equation (3) to calculate the probability probability (y) of each lifestyle factor y.

また、ｙ_ｌ及びｙ_ｌ´は、ライフスタイルファクタｙが存在する場合には１であり、存在しない場合には０である。 However, J is the number of areas concepts associated with stay area, L is the number of lifestyle factors that are associated with each area concept C _j. R _{l, j} is the degree of association between the area concept C _l and the lifestyle factor F _j , x _j is the number of area concepts C _j stored in the area concept storage unit 25, and the area concept C _j It can be paraphrased as the appearance frequency of. H _l (y) is defined by the following equation.

In addition, y _l and y _l'is 1 if the lifestyle factor y exists, if it does not exist is zero.

  The factor estimator 28 calculates probabilities for all combinations when each lifestyle factor is present and not present according to the above equation (3), and estimates a combination of lifestyle factors having the largest value probability (y). As a result. This estimation result means a lifestyle factor adapted to the stay area. The factor estimation unit 28 outputs the estimation result to the lifestyle estimation unit 30.

  The timing at which the factor estimating unit 28 executes the above process is not limited. For example, the factor estimation unit 28 may start the above process at a predetermined time interval or at a predetermined time. In addition, the factor estimation unit 28 may start the above process upon receiving an estimation request from the user (mobile device 10).

  The lifestyle storage unit 29 is a part that stores a lifestyle factor and a lifestyle in association with each other. For example, the lifestyle storage unit 29 stores information as shown in FIG. In FIG. 10, the value “1” indicates that the lifestyle factor and the lifestyle are associated with each other, and the value “0” indicates otherwise.

  The lifestyle estimation unit 30 is a part that estimates the user's lifestyle based on the lifestyle factor estimated by the factor estimation unit 28. When the lifestyle factor estimation result is input from the factor estimation unit 28, the lifestyle estimation unit 30 stores one or more lifestyle information associated with the estimation result (one or more lifestyle factors) in the lifestyle storage unit. Read from 29. For example, on the assumption of the information shown in FIG. 10, when lifestyle factor information “fashion” is input, the lifestyle estimation unit 30 reads lifestyle information “self-supporting achievement type”, and the lifestyle of the user is Presumed to be “independence achievement type”.

  The lifestyle estimation unit 30 transmits the read lifestyle information to the mobile device 10. In addition, the lifestyle estimation unit 30 associates the user ID, the input lifestyle factor estimation result, and the lifestyle estimation result with each other and outputs the result to the estimation result storage unit 31.

  The estimation result storage unit 31 is a part that stores data input from the lifestyle estimation unit 30. The stored data is used by the relevance degree storage unit 26 described above.

  Next, the operation of the estimation system 1 shown in FIG. 1 will be described using FIGS. 11 to 13 and the user management method according to the present embodiment will be described.

  First, a process for estimating a stay area will be described with reference to FIG. When the positioning unit 11 measures the current position in the mobile device 10 and transmits the result as position information to the estimation server 20 (step S11), the stay determination unit 21 receives and accumulates the information (step S12, position acquisition step). ). When the predetermined amount of position information is accumulated, the stay determination unit 21 determines whether or not the user has stayed in any area based on the position information (step S13).

  If it is determined that the user is exclusively moving at this time (step S13; NO), the stay determination unit 21 ends the process and waits until a new amount of position information is accumulated. On the other hand, if it is determined that the user has stayed in a certain area (step S13; YES), the area estimation unit 22 estimates the stay area based on one or more pieces of position information (step S14, area estimation). Step). And the stay area storage part 23 memorize | stores the estimation result as stay area information (step S15).

  Through such a series of processes, information related to the area where the user of the mobile device 10 stays is accumulated in the estimation server 20.

  Next, a process for estimating a lifestyle will be described with reference to FIG. First, the factor estimation unit 28 extracts data corresponding to the area where the user stayed. Specifically, the factor estimation unit 28 displays area attribute information, area concept information, and association degree information corresponding to the stay area information read from the stay area storage unit 23, as an area attribute storage unit 24, an area concept storage unit 25, Each is read from the relevance degree storage unit 26 (step S21, extraction step).

  Subsequently, the factor estimator 28 performs the calculation represented by the above equation (3) for all combinations in the case where each lifestyle factor is present and not present, and the lifestyle having the largest value probability (y). The combination of factors is estimated as a lifestyle factor adapted to the stay area (step S22, factor estimation step).

  Subsequently, the lifestyle estimation unit 30 reads lifestyle information corresponding to the estimated lifestyle factor from the lifestyle storage unit, and uses the information as an estimation result (step S23, lifestyle estimation step). This estimation result is transmitted to the mobile device 10 and displayed on the display unit 13 (step S24). Thereby, the user of the mobile device 10 can confirm the lifestyle estimation result relating to the user.

  Next, the process of updating the relevance will be described with reference to FIG. First, the relevance learning unit 27 extracts a user group associated with each lifestyle factor and further extracts a corresponding area concept for each user group (step S31). Thereby, the relationship between each lifestyle factor and each area concept is extracted. Subsequently, the relevance level learning unit 27 calculates the relevance level by applying any one of the above methods to the extracted data (step S32). Then, the association degree learning unit 27 stores the calculation result in the association degree storage unit 26 as association degree information (step S33). The degree of association is updated by such processing.

  As described above, according to the present embodiment, first, the area where the user stayed is estimated based on the position information (positioning result) of the mobile device 10. Subsequently, data relating to the area concept, lifestyle factor, and relevance of the area is extracted, and based on the data, a lifestyle factor adapted to the stay area is estimated. Then, the lifestyle corresponding to the estimated factor is extracted and determined as the user's lifestyle. As described above, based on the location information of the mobile device 10 and information stored in advance in the storage means (the area attribute storage unit 24, the area concept storage unit 25, the relevance degree storage unit 26, and the lifestyle storage unit 29). Since the user's lifestyle is estimated, the lifestyle of the person can be objectively obtained without placing a burden on the user.

  In the present embodiment, the degree of association is calculated and updated based on the estimated lifestyle factor. Thereby, the relevance degree which matched according to the actual condition is set, As a result, the precision of the estimation of a lifestyle can be improved.

  The present invention has been described in detail based on the embodiments. However, the present invention is not limited to the above embodiment. The present invention can be modified in various ways as described below without departing from the scope of the invention.

  In the said embodiment, although the stay determination part 21 specified the user's stay area, you may abbreviate | omit such a process. In this case, the estimation server 20 may store the received position information as it is in a predetermined storage unit, and the area estimation unit 22 may determine the stay area based on the history of the position information.

  In the above embodiment, the information processing system according to the present invention is applied to the estimation system 1 including the mobile device 10 and the estimation server 20, but each function in the estimation server 20 may be mounted on the mobile device, for example. In this case, the above process can be executed by a single mobile device.

DESCRIPTION OF SYMBOLS 1 ... Lifestyle estimation system (information processing system), 10 ... Mobile device, 11 ... Positioning part, 12 ... Communication part, 13 ... Display part, 20 ... Estimation server, 21 ... Stay determination part (position acquisition means), 22 ... Area estimation unit (area estimation unit), 23 ... stay area storage unit, 24 ... area attribute storage unit (first storage unit), 25 ... area concept storage unit (first storage unit), 26 ... relevance degree storage unit (first) 1 storage means), 27 ... relevance degree learning section, 28 ... factor estimation section (factor estimation means), 29 ... lifestyle storage section (second storage means), 30 ... lifestyle estimation section (lifestyle estimation means), 31 ... an estimation result storage unit.

Claims (5)

A user management method executed by an information processing system,
A position acquisition step for acquiring position information indicating the position of the measured mobile device;
Based on the position information acquired in the position acquisition step, an area estimation step for estimating the area where the user of the mobile device stayed;
From the first storage means for storing the area, the area concept indicating the overall characteristics of the area, the lifestyle factor characterizing the lifestyle of the user, and the association degree of the area concept and the lifestyle factor in association with each other, An extraction step of extracting data corresponding to the area estimated in the area estimation step;
A factor estimation step for estimating a lifestyle factor adapted to the area estimated in the area estimation step based on the data extracted in the extraction step;
A lifestyle corresponding to the lifestyle factor estimated in the factor estimation step is extracted from second storage means for storing the lifestyle factor and the lifestyle in association with each other, and the extracted lifestyle is stored in the mobile device. A lifestyle estimation step for estimating a user's lifestyle;
A user management method comprising: In the factor estimating step, a probability of each lifestyle factor corresponding to the one or more area concepts is obtained from one or more of the relevance levels corresponding to one or more area concepts of the estimated area, and the probability is the highest. The user management method according to claim 1, wherein a combination of large lifestyle factors is used as an estimation result. The lifestyle factor estimated in the factor estimating step;
Storing in a third storage means in association with identification information for identifying the user;
Based on the data stored in the first storage means and the data stored in the third storage means, the degree of association between the area concept and the lifestyle factor is calculated, and the first storage means A relevance update step for updating the stored relevance with the calculated relevance;
Further user management method according to claim 1 or 2, characterized in that it comprises a. Position acquisition means for acquiring position information indicating the position of the measured mobile device;
Area estimation means for estimating the area where the user of the mobile device stayed based on the position information acquired by the position acquisition means;
First storage means for storing an area, an area concept indicating general characteristics of the area, a lifestyle factor that characterizes a user's lifestyle, and a degree of association between the area concept and the lifestyle factor;
A factor that extracts data corresponding to the area estimated by the area estimation unit from the first storage unit, and estimates a lifestyle factor adapted to the area estimated by the area estimation unit based on the extracted data An estimation means;
Second storage means for storing the lifestyle factor and the lifestyle in association with each other;
A lifestyle estimation unit that extracts a lifestyle corresponding to the lifestyle factor estimated by the factor estimation unit from the second storage unit, and estimates the extracted lifestyle as a lifestyle of a user of the mobile device;
An information processing system comprising: The factor estimating means obtains a probability of each lifestyle factor corresponding to the one or more area concepts from one or more of the relevance levels corresponding to one or more area concepts of the estimated area, and the probability is the highest. The information processing system according to claim 4, wherein a combination of large lifestyle factors is an estimation result.

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