KR102540174B1 - Method and device for providing merchant information - Google Patents

Abstract

obtaining information about a user's predicted movement path and preferred characteristics; determining some affiliated stores from among the plurality of affiliated stores based on a first comparison result of the unique characteristics and the preferred characteristics of the plurality of affiliated stores; determining emotional weights for some of the affiliated stores based on a second comparison result of the relative characteristics and the preferred characteristics of the partial affiliated stores; determining distance weights for the partial affiliate stores based on a third comparison result of the locations of the partial affiliate stores and the predicted movement path; and providing recommendation information for the some affiliated stores based on the emotion weight and the distance weight.

Description

Method and device for providing merchant information {Method and device for providing merchant information}

The technical field of the present disclosure relates to a method and device for providing affiliated store information, and more particularly, to a method and device capable of providing recommendation information on some affiliated stores determined according to a plurality of characteristic factors according to a user account. .

Since human diet is the most basic and essential factor for the survival of mankind, there have been many developments and changes in dietary life as we come to modern times. In general, the service industry, such as the restaurant industry, is mainly clustered in a specific area or developed by gathering in the same type of industry. Accordingly, a large number of restaurant operators have devised various ways to attract customers in order to survive in the fierce competition. Therefore, the affiliate store recommendation service through the application is being implemented, but since only the type of technology that provides information related to the affiliate store desired by the user according to the search is used, the provision of affiliate store information according to user characteristics is not provided. there was

Therefore, in providing affiliate store recommendation information, there was a social need for a method of providing some affiliate store recommendation information that was filtered to reflect the convenience according to user characteristics as much as possible, but there was no appropriate product or technology that could solve this social need.

There is a need for a technology capable of providing useful information about a plurality of affiliated stores according to user's personal characteristics.

Korean Patent Publication No. 10-2008-002720 (2008.01.04) Product (or merchant) recommendation system

The problem to be solved by the present disclosure is to recommend information on an affiliate store that is highly suitable for the user by assigning different weights to each element using a user's moving path and a plurality of users' characteristic information in a system that provides affiliate store information It is to provide a service that can provide.

As a technical means for achieving the above-described technical problem, a method for providing affiliated store information by a device according to a first aspect of the present disclosure includes obtaining information about a predicted movement path and preferred characteristics of a user; determining some affiliated stores from among the plurality of affiliated stores based on a first comparison result of the unique characteristics and the preferred characteristics of the plurality of affiliated stores; determining emotional weights for some of the affiliated stores based on a second comparison result of the relative characteristics and the preferred characteristics of the partial affiliated stores; determining distance weights for the partial affiliate stores based on a third comparison result of the locations of the partial affiliate stores and the predicted movement path; and providing recommendation information for the some affiliated stores based on the emotion weight and the distance weight.

In addition, the unique characteristic may include at least one of vegan support, spicy taste support, pork support, and shellfish exclusion.

In addition, the relative characteristic may include at least one of whether a specific payment method is supported, a star rating level, a search number level, and a sales level.

In addition, the distance weight may be determined as a positive or negative number according to a deviation level indicating a degree of deviation from the predicted movement path.

In addition, the distance weight may be determined by the deviation level, the user's age, the user's gender, a certain number per unit time for the user, and the expected route if the user has a visit history for an area adjacent to the travel route. It may be determined according to the traffic convenience level for the adjacent area of

In addition, the distance weight may be determined by the deviation level, the user's age, the user's gender, a certain number per unit time for the user, and the expected route if the user has a visit history for an area adjacent to the travel route. It may be determined according to the traffic convenience level for the adjacent area of

A device for providing affiliated store information according to a second aspect of the present disclosure includes a receiver configured to obtain information about a predicted movement path and preference characteristics of a user; Determine some affiliated stores from among the plurality of affiliated stores based on the unique characteristics of the plurality of affiliated stores and the first comparison result of the preferred characteristic, and based on the relative characteristics of the partial affiliated stores and the second comparison result of the preferred characteristic, the Determining emotional weights for some affiliated stores, determining distance weights for some affiliated stores based on a third comparison result for locations of the partial affiliated stores and the predicted movement path, and based on the emotional weights and the distance weights It may include; a processor providing recommendation information on some of the affiliated stores.

According to an embodiment of the present disclosure, user convenience can be improved in that recommendation information is provided to an affiliated store that is more suitable for the user because a plurality of affiliated stores filtered according to the user's location are provided as recommendation information.

In addition, since a plurality of affiliated stores determined based on user characteristics are provided as recommendation information, user satisfaction can be improved and efficiency can be improved in that there is an advertising effect for the plurality of affiliated stores.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a block diagram schematically illustrating a configuration of a device according to an exemplary embodiment.
2 is a flowchart illustrating a method for a device to provide recommendation information for some affiliated stores, according to an exemplary embodiment.
3 is a diagram for explaining an example of an affiliate store filter according to unique characteristics and preference characteristics of an affiliate store according to an exemplary embodiment.
4 is a diagram for explaining an example in which a device determines a distance weight according to an exemplary embodiment.
5 is a diagram illustrating a configuration of a device according to an exemplary embodiment in more detail.

Advantages and features in the present disclosure, and methods for achieving them will become clear with reference to embodiments described later in detail in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms, only the present embodiments are intended to make the disclosure complete, and the scope of the present disclosure to those skilled in the art belonging to the technical field. It is provided to fully inform you.

Terminology used in this specification is for describing embodiments and is not intended to limit the present disclosure. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present disclosure.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

The spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as encompassing different orientations of elements in use or operation in addition to the orientations shown in the drawings. For example, if you flip a component that is shown in a drawing, a component described as "below" or "beneath" another component will be placed "above" the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

Hereinafter, various embodiments will be described in detail with reference to the drawings.

1 is a block diagram schematically illustrating a configuration of a device 100 according to an exemplary embodiment.

Referring to FIG. 1 , a device 100 may include a receiver 110 and a processor 120 .

The receiving unit 110 according to an embodiment may obtain information about a user's predicted movement path and preference characteristics.

The processor 120 according to an embodiment may determine some affiliated stores from among the plurality of affiliated stores based on a first comparison result of unique characteristics and preferred characteristics of the plurality of affiliated stores. Also, the processor 120 may determine emotional weights for some affiliated stores based on the second comparison result of relative characteristics and preferred characteristics of some affiliated stores. In addition, the processor 120 may determine distance weights for some affiliate stores based on a third comparison result of the predicted movement paths and locations of some affiliate stores. Also, the processor 120 may provide recommendation information on some affiliated stores based on the emotion weight and the distance weight.

In addition, the device 100 providing affiliated store information receives the predicted movement path and preference characteristics of the user from the user account in the receiving unit 110, and determines the first comparison result and the second comparison result in the processor 120 to obtain It should be noted that in the process of providing recommendation information based on emotional weight and distance weight for some affiliated stores, various conventional network combinations such as Internet networks or mobile communication networks may be combined, and there is no particular limitation on this.

In addition, those skilled in the art can understand that other general-purpose components other than the components shown in FIG. 1 may be further included in the device 100 providing affiliated store information. For example, the device 100 providing affiliate store information may include information about a movement route and preference characteristics acquired from a user account, some affiliate stores determined from the processor 120, emotion weight, distance weight, recommendation information for some affiliate stores, etc. A memory (not shown) for storing may be further included. Alternatively, in the case of other embodiments, those skilled in the art may understand that some of the components shown in FIG. 1 may be omitted.

The device 100 for providing affiliated store information according to an embodiment may be used by a user, and a touch screen panel such as a mobile phone, smart phone, PDA (Personal Digital Assistant), PMP (Portable Multimedia Player), tablet PC, etc. It can be linked with all types of handheld-based wireless communication devices equipped with, and in addition to this, there is a basis for installing and running applications, such as desktop PCs, tablet PCs, laptop PCs, and IPTV including set-top boxes. It may be included in or interlocked with the prepared device.

The device 100 providing affiliated store information may be implemented as a terminal such as a computer that operates through a computer program for realizing the functions described in this specification.

The device 100 providing affiliated store information according to an embodiment may include a system (not shown) for providing recommendation information on an affiliated store and a related server (not shown), but is not limited thereto. A server according to an embodiment may support an application that provides recommendation information on an affiliated store.

Hereinafter, the description will focus on an embodiment in which the device 100 providing affiliated store information independently provides affiliated store recommendation information according to an embodiment, but as described above, it may be performed through interworking with a server. That is, it can be seen that the device 100 and the server providing affiliated store information according to an embodiment may be integrated and implemented in terms of their functions, and the server may be omitted, and is not limited to any one embodiment. .

2 is a flowchart illustrating a method of providing recommendation information for some affiliated stores by the device 100 according to an embodiment.

Referring to step S210, the device 100 according to an embodiment may obtain information about a user's predicted movement path and preferred characteristics. In an embodiment, the user's predicted movement path may mean a path from a starting point to a destination input from the user. The starting point may correspond to the user's current location. Also, in one embodiment, the unique characteristic means a characteristic corresponding to an affiliated store and may mean a fixed element such as a type of menu. For example, the unique characteristic may include at least one of vegan support, spicy taste support, pork support, and shellfish exclusion. The availability of vegan support may be an element indicating whether the franchisee provides vegan cuisine, the availability of spicy support may be an element indicating whether the franchisee provides spicy, medium, non-spicy, etc., and the availability of pork support may be a franchisee It may be an element indicating whether pork is provided in this dish, and whether shellfish can be excluded may be an element indicating whether or not crustaceans are excluded from the dish provided by the franchisee.

Referring to step S220, the device 100 according to an embodiment may determine some affiliated stores from among the plurality of affiliated stores based on the first comparison result of the unique characteristics and preferred characteristics of the plurality of affiliated stores. In an embodiment, the preferred characteristic means a characteristic of a user, and may mean a characteristic preferred by a user according to a unique characteristic of an affiliated store. For example, if the user prefers vegan support and does not prefer spicy taste, the preference characteristics may include a vegan menu and a menu corresponding to non-spicy taste. Accordingly, the device 100 may determine the first comparison result according to the unique characteristics of the affiliated stores and the user's preferred characteristics, and may determine some affiliated stores suitable for the user among a plurality of affiliated stores based on the first comparison result. Step S220 may be described with reference to FIG. 3 . Referring to FIG. 3 , at least one of the affiliate stores A to H may be recommended according to the unique characteristics of the affiliate stores and the user's preferred characteristics. In FIG. 3 , a plurality of affiliate store types are displayed on the vertical axis, and a list of vegetarian, spicy, lactose, pork, vegan, and shellfish corresponding to unique characteristics of the affiliate store may be displayed on the horizontal axis. In addition, depending on whether it is provided, if it is provided, it can be displayed as 1, and if it is not provided, it can be displayed as 0. Therefore, it is possible to determine a recommended affiliated store according to the user's preferred characteristics obtained from the user account, and to determine some affiliated stores among the determined plurality of affiliated stores.

Referring to step S230, the device 100 according to an embodiment may determine emotional weights for some affiliated stores based on a second comparison result of relative characteristics and preferred characteristics of some affiliated stores. In one embodiment, the relative characteristic means a characteristic corresponding to an affiliated store, and may mean an element that can change over time, such as evaluation information of a plurality of users of an affiliated store. For example, the relative characteristic may include at least one of whether a specific payment method is supported, a star rating level, a search number level, and a sales level. Whether or not to support a specific payment method may be an element indicating whether the affiliate store provides a specific payment method such as Samsung Pay or Naver Pay, and the star rating level may be an element corresponding to evaluation information of the affiliate store evaluated from a plurality of user accounts, The search number level is to check how many times a plurality of users have searched for the affiliated store, and may be an element indicating the level of interest according to a plurality of users, and the sales level is sales for a predetermined time at the affiliated store and is steadily above a certain level. It is for checking whether or not it is maintained, and may be an element indicating popularity according to a plurality of users. Accordingly, the device 100 may firstly determine the emotional weight for each of some affiliated stores determined based on the primary comparison result. The emotional weight may be an element given to each affiliated store according to relative characteristics changeable over time according to the affiliated store. The device 100 may determine a second comparison result based on the relative characteristics of some affiliated stores and the user's preferred characteristics, and may determine emotional weights for some affiliated stores based on the second comparison result.

Referring to step S240 , the device 100 according to an embodiment may determine distance weights for some affiliate stores based on a third comparison result of the predicted movement path and the location of some affiliate stores. In an embodiment, the distance weight may be determined as a positive or negative number according to a deviation level indicating a degree of deviation from the predicted movement path. Step S240 may be described with reference to FIG. 4 . Referring to FIG. 4 , the device 100 according to an embodiment may differently determine a degree of deviation from a movement path according to a preset distance. For example, “a” may correspond to a starting point and “b” may correspond to a destination. A path from “a” to “b” may be a predicted movement path for the user. In addition, the device 100 may determine a preset distance deviating from the predicted movement path determined from “A” to “B” as 100 m, 250 m, and 500 m according to the determined locations of some affiliated stores. A departure level may be determined according to the predetermined distance, and a distance weight may be assigned accordingly. For example, the exit levels may be determined as A, B, and C. In FIG. 4 , the preset distance is indicated in a straight line, but is not limited thereto and may mean a circular boundary area within a preset radius. Accordingly, the device 100 may determine the deviation level as A when the user's location is located within a radius of 100 m on the predicted movement path, and determine the deviation level as B when the user's location is located within a radius of 100 m or more and 250 m on the predicted movement route. If the user's position is located within a radius of 250m or more and 500m in the predicted movement path, the deviation level can be determined as C. The device 100 may determine the distance weight as a positive number or a negative number according to the determined departure level. For example, the device 100 may determine the distance weight as a positive number when the departure levels are A and B, and may determine the distance weight as a negative number when the departure level is C. That is, the distance weight may be updated to a negative number from a point in time exceeding a predetermined distance. The aforementioned preset distance may be set by a manager, but is not limited thereto and may be differently determined according to situations and inputs of a manager. In addition, the distance weight is determined by the user's departure level, the user's age, the user's gender, a certain number per unit time for the user, and the traffic convenience level for the adjacent area of the expected route, if the user has a visit history for the adjacent area of the travel route. can be determined according to For example, the distance weight may be determined according to weights gradually decreasing in the order of a departure level, a user's age, a user's gender, a certain number of users per unit time, and a transportation convenience level. The departure level is an element that indicates the degree of deviation from the predicted travel path. If the user has a visit history to an area adjacent to the travel path (predicted travel path), the user's current location is likely to be close to the travel path. The highest weight can be given to the departure level in that checking can have the greatest influence on determining the distance weight. In addition, if the user is old, it may take a lot of time to move the travel route and there may be difficulties in moving. In that it may be desirable to recommend, a second high weight may be given to the user's age. In addition, when the user's age corresponds to a predetermined or higher age, since males generally have superior physical strength, considering this, when the user's age corresponds to a predetermined or higher age, the distance weight is greater than that of males when the user corresponds to a female. Thus, it may be desirable to recommend an affiliate store with a short distance, so a third high weight may be assigned to the user's gender. In addition, it may take more than a certain level of time to move the travel route. If the user has many schedules per unit time, it may be desirable to consider this in that there may be difficulties in moving the travel route. Objectivity may be slightly lower than the three factors described above in that it may vary flexibly by star and time zone, and if the user is located in an area adjacent to the travel route, it may not take much time to move, so it may be considered somewhat less. In this respect, a weight as high as 4th may be assigned to a certain number per unit time. In addition, it may be desirable to consider this in that the user can check the convenience of movement according to the traffic convenience of the area adjacent to the expected route, but this may also vary flexibly depending on circumstances such as by date and time. In general, when there is a visit history for an area adjacent to an expected route, there is a high probability of being nearby, so there is a high probability of not using traffic, and the expected route (from the point of departure to the destination) is an affiliate store whose distance is less than or equal to the preset level. Since this is recommended, a high weight can be given to the level of transportation convenience in the 5th order in that there can be a lot of walking, so it can be less affected by traffic. Therefore, since the device 100 determines the distance weight by making the weights given to each of the plurality of elements different, accuracy can be improved in determining the recommended affiliated store.

According to an embodiment, the device 100 may update the size of a weight given to each element in order to determine a distance weight according to circumstances. For example, the device 100 according to an embodiment, when there is no history of visits to areas adjacent to the user's travel route, provides a transportation convenience level, a certain number per unit time for the user, a deviation level, the user's age, and the user's age. Distance weights may be determined by assigning weights to gradually decrease in the order of gender. For example, if the user has no history of visits to the adjacent area of the travel route, the user is located at a distance greater than a predetermined distance, and the departure level is farther than the predetermined level, so the user preferentially moves to the adjacent area. Since it is most desirable to check convenience, the highest weight can be given to the level of transportation convenience, and if there are many schedules per unit time of the user, there may be difficulties in moving the route. If the user is not located and is located more than a preset distance, it may take a lot of time to move the travel route, so it can be determined that there is a high need to consider a certain number per unit time for the user. A second high weight may be assigned to a certain number. Therefore, when the user does not have a history of visits to areas adjacent to the travel route, the device 100 updates the transportation convenience level, which has been given a high weight in the previous 5th rank, to be given the highest weight, and the highest weighted value in the previous 4th rank. It can be updated so that a high weight of the second rank is given to a certain number per unit time that was assigned. In addition, according to the above-mentioned reason, high weights may be assigned to the 3rd to 5th ranks to the departure level, the user's age, and the user's gender, respectively. Therefore, since the device 100 determines the weight by updating the size of the weight given to each element according to the situation, it is possible to determine the recommended affiliate store with higher accuracy.

Referring to step S250, the device 100 according to an embodiment may provide recommendation information on some affiliated stores based on the emotion weight and the distance weight. In an embodiment, recommendation information on some affiliated stores may mean recommended affiliated stores, and recommended affiliated stores may be provided based on the emotional weight and distance weight determined according to the above description.

3 is a diagram for explaining an example of an affiliate store filter according to unique characteristics and preference characteristics of an affiliate store according to an exemplary embodiment. Referring to FIG. 3, as described in step S220, a plurality of member store types are displayed on the vertical axis, and a list of vegetarian, spicy, lactose, pork, vegan, crustacean, etc. corresponding to the unique characteristics of the member store is displayed on the horizontal axis. can In addition, depending on whether it is provided, if it is provided, it can be displayed as 1, and if it is not provided, it can be displayed as 0. Those skilled in the art will understand that the unique characteristics of affiliated stores are not limited thereto, and may be differently changed by a manager depending on circumstances. Accordingly, it is possible to provide information on recommended affiliated stores determined according to the user's preferred characteristics acquired from the user account. For example, when the preference characteristics of the user input from the user include a menu corresponding to spicy, a vegan menu, and a menu corresponding to excluding shellfish, the device 100 selects recommended affiliated stores as recommended restaurant A, recommended restaurant B, and recommended Restaurant E can be determined and the three franchisees can be provided as recommendation information for some franchisees.

4 is a diagram for explaining an example in which the device 100 determines a distance weight according to an exemplary embodiment. Referring to FIG. 4 , as described in step S240, the device 100 according to an embodiment may differently determine a degree of deviation from the movement path according to a preset distance. In addition, the device 100 may determine a deviation level according to a predetermined distance deviating from the predicted movement path and may assign a distance weight accordingly. The device 100 may determine the deviation level as A when the user's location is located within a radius of 100 m on the predicted movement path, and may determine the deviation level as B when the user's location is located within a radius of 100 m or more and 250 m on the predicted movement route. If the user's position is located within a radius of 250m or more and 500m in the predicted movement path, the deviation level can be determined as C. Accordingly, a distance weight may be assigned for each distance, and when the departure level is determined to be C or higher (located at 250 m or more), a negative weight may be assigned.

5 is a diagram showing the configuration of the device 100 according to an embodiment in more detail. The device 100 according to an embodiment may include an affiliate jumbo DB in which affiliate information on a plurality of affiliate stores is pre-stored, and may include a preference avoidance information DB in which preference and avoidance information is pre-stored as shown in FIG. can In addition, a distance weight calculation unit may be included to determine the distance weight. It may include a data shaping unit that performs cleanup by comparing and analyzing various information obtained accordingly. In addition, it may include a result providing unit that provides a plurality of results finally determined according to data formatting.

According to an embodiment, since a plurality of affiliated stores on which filtering has been performed according to the user's location is provided as recommendation information, user convenience can be improved in that it is provided to an affiliated store that is more suitable for the user, and based on user characteristics Since the determined plurality of affiliated stores are provided as recommendation information, user satisfaction can be improved, and efficiency can be improved in that there is an advertising effect for the plurality of affiliated stores.

Various embodiments of the present disclosure are implemented as software including one or more instructions stored in a storage medium (eg, memory) readable by a machine (eg, a display device or a computer). It can be. For example, the processor 120 (eg, processor 120) of the device may call at least one of one or more instructions stored from a storage medium and execute it. This enables the device to be operated to perform at least one function according to the invoked at least one instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' only means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and this term refers to the case where data is stored semi-permanently in the storage medium. It does not discriminate when it is temporarily stored.

According to one embodiment, the method according to various embodiments disclosed in the present disclosure may be included and provided in a computer program product. Computer program products may be traded between sellers and buyers as commodities. A computer program product is distributed in the form of a device-readable storage medium (e.g. compact disc read only memory (CD-ROM)), or through an application store (e.g. Play Store™) or on two user devices (e.g. It can be distributed (eg downloaded or uploaded) online, directly between smartphones. In the case of online distribution, at least part of the computer program product may be temporarily stored or temporarily created in a device-readable storage medium such as a manufacturer's server, an application store server, or a relay server's memory.

Although the present invention has been described with reference to the drawings exemplified, it is not limited by the disclosed embodiments and drawings, and those skilled in the art related to the present embodiment may modify within the range that does not deviate from the essential characteristics of the above description. It will be understood that it can be implemented in the form of Therefore, the disclosed methods are to be considered in an illustrative rather than a limiting sense. Although an embodiment is described and the operation and effect according to the configuration of the present invention are not explicitly described and described, effects predictable by the corresponding configuration can also be recognized. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the equivalent scope will be construed as being included in the present invention.

100: device
110: receiver 120: processor

Claims (7)

In the method of providing affiliated store information,
obtaining, by a reception unit, information about a predicted movement path and preferred characteristics of a user;
determining, by a processor, some affiliated stores from among the plurality of affiliated stores based on a first comparison result of the unique characteristics and the preferred characteristics of the plurality of affiliated stores;
determining, by the processor, emotional weights for some of the affiliated stores based on a second comparison result of the relative characteristics and the preferred characteristics of the partial affiliated stores;
determining, by the processor, distance weights for the partial affiliate stores based on a third comparison result of the locations of the partial affiliate stores and the predicted movement path; and
Providing, by the processor, recommendation information for the some affiliated stores based on the emotion weight and the distance weight;
The distance weight is
If the user has a history of visits to areas adjacent to the travel route,
A departure level indicating the degree of deviation from the predicted travel path, the user's age, the user's gender, a certain number per unit time for the user, and a transportation convenience level for the adjacent area of the expected route. determined according to weight,
If the user has no history of visits to neighboring areas of the travel route,
The transportation convenience level, a predetermined number per unit time for the user, the departure level, the age of the user, and the gender of the user are determined according to weights assigned to gradually decrease in order.
According to claim 1,
The unique characteristic is
A method comprising at least one of vegan support, spicy support, pork support, and shellfish exclusion. According to claim 1,
The relative characteristic is
A method including at least one of a specific payment method support availability, a star rating level, a search number level, and a sales level. According to claim 1,
The distance weight is
A positive or negative number is determined according to the departure level.
delete delete In a device that provides affiliated store information,
a receiving unit for obtaining information about a user's predicted movement path and preferred characteristics;
determining some affiliated stores among the plurality of affiliated stores based on a first comparison result of the unique characteristics and the preferred characteristics of the plurality of affiliated stores;
Determining an emotional weight for the some affiliated stores based on a second comparison result for the relative characteristics and the preferred characteristics of the partial affiliated stores;
determining distance weights for the partial affiliate stores based on a third comparison result for the locations of the partial affiliate stores and the predicted movement path;
A processor providing recommendation information for the some affiliated stores based on the emotion weight and the distance weight;
The distance weight is
If the user has a history of visits to areas adjacent to the travel route,
A departure level indicating the degree of deviation from the predicted travel path, the user's age, the user's gender, a certain number per unit time for the user, and a transportation convenience level for the adjacent area of the expected route. determined according to weight,
If the user has no history of visits to neighboring areas of the travel route,
The device, which is determined according to weights assigned to gradually decrease in the order of the transportation convenience level, a certain number per unit time for the user, the departure level, the age of the user, and the gender of the user.

Images