JP7252748B2 - Information recommendation method and computer system - Google Patents

Description

The following description relates to techniques for providing recommendation information on social networks.

Product recommendation on the Internet is intended to select and recommend products optimized for individuals. The logic for product recommendation is to select users who have purchase histories similar to those of the target user to whom the product is to be recommended, and to recommend products that the similar users have purchased but the recommended user has not yet purchased. Filtering (Collaborative Filtering) and Algorithms such as Association Rule Mining that recommends products with a high possibility of simultaneous purchase or simultaneous click for each product using the purchase records of many users is utilized.

For example, in Patent Document 1 (registered on February 2, 2009), product information is automatically recommended using a product code, product name or model recognized by an information recommendation agent installed in a user terminal. A technique for doing so is disclosed.

However, it cannot be said that the product suitable for the target user can be recommended accurately, quickly, and satisfactorily.

Korean Patent No. 10-0882716

A platform for recommending popular information among acquaintances on a social network can be realized.

It is possible to recommend product information and store information with increased transactions based on payment information of acquaintances on social networks.

1. An information recommendation method performed in a computer system, said computer system including at least one processor configured to execute computer readable instructions contained in a memory, said information recommendation method comprising: said at least one processor constructing a DB (database) containing purchase information for each user on the social network; searching the DB for purchase information of users belonging to the user group to generate purchase recommendation information for the user group; Provided is an information recommendation method including the step of providing the purchase recommendation information to a non-purchase user whose information is not generated.

According to one aspect, the configuring may include obtaining the user's purchase information from the user's electronic device in a message received by the electronic device.

According to another aspect, the configuring may include obtaining the user's purchase information from another server with which the user's identity on the social network is linked.

According to another aspect, the acquired purchase information includes at least one of merchandise information and store information, and the step of configuring includes standardizing the notation of the merchandise and the store acquired as the purchase information. and storing it in the DB.

According to another aspect, the generating includes generating purchase recommendation information for at least one of products and stores purchased by a predetermined number of users belonging to the user group. good.

According to another aspect, the step of generating includes generating purchase recommendation information for at least one of a product and a store purchased by at least a certain number of users of the user group within a predetermined time. may contain

According to another aspect, the generating step includes periodically generating, as purchase recommendation information, a list of at least one of products and shops purchased by users belonging to the user group during a corresponding period. may contain

According to another aspect, the step of generating includes selecting one of at least one user group to which the arbitrary user belongs on the social network when purchase information of the arbitrary user is stored in the DB. acquiring, and searching the DB for the purchase information of users belonging to the acquired user group, and if a predetermined number or more of purchase information corresponding to the purchase information of the arbitrary user are found, the corresponding purchase information; as the purchase recommendation information.

According to another aspect, the non-purchasing user may be a user for whom purchase information corresponding to the product or store corresponding to the purchase recommendation information has not been generated in the user group.

According to another aspect, the purchase information for each user may include purchase price information, and the providing step may include providing the purchase recommendation information based on the purchase price information.

According to another aspect, the information recommendation method may further include, by the at least one processor, setting information recommendation criteria for providing the purchase recommendation information for each user.

According to another aspect, in the step of providing, purchase information corresponding to the two or more user groups is generated as the purchase recommendation information for non-purchasing users belonging to the two or more user groups. If so, a step of limiting the number of times the corresponding purchase recommendation information is provided may be included.

According to yet another aspect, the step of providing includes, for non-purchased users belonging to two or more user groups, if the same product is generated as the purchase recommendation information for the two or more user groups, A step of limiting the number of times the purchase recommendation information is provided for the corresponding product may be included.

A non-transitory computer-readable recording medium is provided in which a program for causing a computer to execute the information recommendation method is recorded.

1. A computer system comprising a memory and at least one processor coupled to the memory and configured to execute computer readable instructions contained in the memory, the at least one processor comprising: a DB configuration unit configuring a DB (database) containing purchase information for each user on the network, searching the DB for purchase information of users belonging to a user group in which relationships are established between users on the social network, and an information generating unit for generating purchase recommendation information for a user group; and information for providing the purchase recommendation information to a non-purchasing user, among the users belonging to the user group, for whom purchase information corresponding to the purchase recommendation information has not been generated. A computer system is provided that includes a recommender.

1 is a diagram showing an example of a network environment in one embodiment of the present invention; FIG. 1 is a block diagram for explaining internal configurations of an electronic device and a server in one embodiment of the present invention; FIG. 1 is a diagram showing an example of an information recommendation service environment in one embodiment of the present invention; FIG. 1 is a block diagram illustrating exemplary components that a processor of a server may include in one embodiment of the invention; FIG. Figure 4 is a flowchart illustrating an example of a method that a server may perform in one embodiment of the invention; It is the figure which showed the example of the process of acquiring purchase information in one Embodiment of this invention. FIG. 4 is an exemplary diagram for explaining a purchase information DB in one embodiment of the present invention; FIG. FIG. 4 is a diagram for explaining the process of configuring a purchase information DB with standardized notation in one embodiment of the present invention; FIG. 4 is an exemplary diagram for explaining an information recommendation process according to an embodiment of the present invention; FIG. 4 is an exemplary diagram for explaining an information recommendation process according to an embodiment of the present invention;

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

TECHNICAL FIELD Embodiments of the present invention relate to technology for providing recommendation information on a social network, and more particularly to technology for providing popular information among acquaintances on the social network.

Embodiments, including those specifically disclosed herein, can provide popular information among social network acquaintances, thereby increasing efficiency, market retention, service expansion, convenience, cost. Considerable advantages can be achieved in aspects such as savings.

FIG. 1 is a diagram showing an example of a network environment in one embodiment of the present invention. The network environment of FIG. 1 illustrates an example including multiple electronic devices 110 , 120 , 130 , 140 , multiple servers 150 , 160 , and a network 170 . Such FIG. 1 is merely an example for explaining the invention, and the electronic device and server are not limited to the forms shown in FIG.

The plurality of electronic devices 110, 120, 130, 140 may be fixed terminals or mobile terminals implemented by a computer system. Examples of the plurality of electronic devices 110, 120, 130, and 140 include smart phones, mobile phones, navigation systems, PCs (personal computers), notebook pan computers, digital broadcasting terminals, PDAs (Personal Digital Assistants), and PMPs (Portable Multimedia Players). ), tablets, game consoles, wearable devices, IoT (Internet of things) devices, VR (virtual reality) devices, AR (augmented reality) devices, and the like. As an example, FIG. 1 shows the shape of a smartphone as an example of the electronic device 1 (110). , and may refer to one of a variety of physical computer systems capable of communicating with other electronic devices 120 , 130 , 140 and/or servers 150 , 160 via network 170 .

The communication method is not limited, and not only the communication method that utilizes the communication network that the network 170 can include (eg, mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) It may also include short-range wireless communication between For example, the network 170 includes a PAN (personal area network), a LAN (local area network), a CAN (campus area network), a MAN (metropolitan area network), a WAN (wide area network), a BBN (broadband network), and the Internet. Any one or more of the networks may be included. Additionally, network 170 may include any one or more of network topologies including, but not limited to, bus networks, star networks, ring networks, mesh networks, star bus networks, tree or hierarchical networks, and the like. is not limited to

Each of servers 150, 160 is implemented by a computer device or a plurality of computer devices that communicate with a plurality of electronic devices 110, 120, 130, 140 via network 170 to provide instructions, code, files, content, services, etc. you can For example, the server 150 may be a system that provides a first service to a plurality of electronic devices 110, 120, 130, 140 connected via the network 170, and the server 160 may also be a system that provides a plurality of electronic devices connected via the network 170. It may be a system that provides the second service to the electronic devices 110 , 120 , 130 , 140 . As a more specific example, the server 150 provides a service (for example, a social service, etc.) intended by the application through an application as a computer program that is installed and driven in the plurality of electronic devices 110, 120, 130, and 140. ) may be provided to a plurality of electronic devices 110 , 120 , 130 , 140 as a first service. As another example, the server 160 may provide, as a second service, a service for distributing files for installing and running the above-described applications to the plurality of electronic devices 110, 120, 130, and 140. FIG.

FIG. 2 is a block diagram for explaining internal configurations of an electronic device and a server in one embodiment of the present invention. In FIG. 2, the internal configuration of the electronic device 1 (110) and the internal configuration of the server 150 will be described as an example of the electronic device. Other electronic devices 120, 130, 140 and server 160 may also have the same or similar internal configurations as electronic device 1 (110) or server 150 described above.

Electronic device 1 (110) and server 150 may include memories 211, 221, processors 212, 222, communication modules 213, 223, and input/output interfaces 214, 224. The memories 211 and 221 are non-temporary computer-readable recording media such as RAM (random access memory), ROM (read only memory), disk drive, solid state drive (SSD), and flash memory. ), and the like. Here, permanent mass storage devices such as ROMs, SSDs, flash memories, and disk drives are included in the electronic device 1 (110) and server 150 as separate permanent storage devices separate from the memories 211 and 221. good too. The memories 211 and 221 also store an operating system and at least one program code (for example, a browser installed and driven in the electric device 1 (110) or the electronic device 1 (110) for providing a specific service). may store code for applications installed in the . Such software components may be loaded from a computer-readable recording medium separate from the memories 211,221. Such another computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, disk, tape, DVD/CD-ROM drive, memory card, and the like. In other embodiments, the software components may be loaded into memory 211, 221 through communication modules 213, 223 that are not computer readable media. For example, at least one program is a computer program (as an example, the above-mentioned may be loaded into the memory 211, 221 based on the application).

Processors 212, 222 may be configured to process computer program instructions by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to processors 212 , 222 by memory 211 , 221 or communication modules 213 , 223 . For example, processors 212 , 222 may be configured to execute received instructions according to program code stored in a storage device, such as memory 211 , 221 .

Communication modules 213 and 223 may provide functions for electronic device 1 (110) and server 150 to communicate with each other via network 170, and electronic device 1 (110) and/or server 150 may communicate with each other. may provide functionality for communicating with other electronic devices (eg, electronic device 2 (120)) or other servers (eg, server 160). As an example, a request generated by the processor 212 of the electronic device 1 (110) according to program code stored in a recording device such as the memory 211 is transmitted to the server 150 via the network 170 under the control of the communication module 213. you can Conversely, control signals, commands, contents, files, etc., provided under the control of the processor 222 of the server 150 are transmitted to the electronic device via the communication module 213 of the electronic device 1 (110) via the communication module 223 and the network 170. 1 (110). For example, control signals, commands, contents, files, etc. of the server 150 received through the communication module 213 may be transmitted to the processor 212 and the memory 211, and the contents, files, etc. may be further included in the electronic device 1 (110). may be stored in a storage medium (permanent storage as described above) that can store

Input/output interface 214 may be a means for interfacing with input/output devices 215 . For example, input devices may include devices such as keyboards, mice, microphones, cameras, and output devices may include devices such as displays, speakers, haptic feedback devices, and the like. As another example, input/output interface 214 may be a means for interfacing with a device that integrates functionality for input and output, such as a touch screen. The input/output device 215 may consist of one device with electronic device 1 (110). Input/output interface 224 of server 150 may also be a means for interfacing with devices (not shown) for input or output that are coupled to server 150 or that server 150 may include. As a more specific example, when the processor 212 of the electronic device 1 (110) processes the instructions of the computer program loaded into the memory 211, data provided by the server 150 and the electronic device 2 (120) are used. The service screens and content displayed may be displayed on the display through the input/output interface 214 .

Also, in other embodiments, electronic device 1 (110) and server 150 may include more components than those in FIG. However, most prior art components need not be explicitly shown in the figures. For example, electronic device 1 (110) may be implemented to include at least some of the input/output devices 215 described above, a transceiver, a GPS (Global Positioning System) module, a camera, various sensors, a database It may further include other components such as As a more specific example, when the electronic device 1 (110) is a smartphone, the acceleration sensor, gyro sensor, camera module, various physical buttons, buttons using a touch panel, input/ Various components such as an output port, a vibrator for vibration, etc. may be implemented to be further included in the electronic device 1 (110).

Specific embodiments of methods and systems capable of recommending popular information among acquaintances on social networks are described below.

In this specification, a representative example of a social network is described as a messenger, but it is not limited to this. The present invention is applicable to all social media that can exchange messages and data and share various types of information.

Mainly, when a user directly visits a shopping site or a shopping page in messenger, or purchases an item based on information shared by other users on a bulletin board, etc., the user actively selects the desired item. It is possible to search, recognize and purchase in a short time.

In the present invention, after obtaining product purchase information of messenger users and constructing a DB (database), information popular among acquaintances can be grasped and automatically recommended based on such DB.

FIG. 3 is a diagram showing an example of an information recommendation service environment in one embodiment of the present invention.

Referring to FIG. 3, the server 150 may serve as a service platform capable of identifying and recommending products and shops that are popular among acquaintances. For the sake of simple explanation, the case where the transaction object is a product will be explained, but the present invention may be similarly applied not only to the product but also to services that can be the transaction object. An example of the flow of processing according to the embodiment will be described below.

1. When a user purchases a product from a shopping site or a shopping page in messenger, the user may receive a message including corresponding purchase information through a sort message service (SMS), messenger notification account, or the like.

2. The DB server 301 may acquire purchase information received through SMS or a messenger notification account for a messenger user by linking with the messenger application installed in the user's electronic device 1 (110). . A DB may be constructed by matching and storing such purchase information with user information.

3. The DB server 301 may request acquaintance information by transmitting corresponding user information to the messenger server 302 for the user for whom the purchase information is generated. Acquaintance information may refer to a list of strangers who have relationships (eg, friends, neighbors, subscriptions, groups, etc.) with the user on messenger.

4. The messenger server 302 may transmit the acquaintance information of the user requested by the DB server 301 to the DB server 301 .

5. Based on the acquaintance information acquired from the messenger server 302, the DB server 301 may transmit information about products and shops purchased by N or more acquaintances to the messenger server 302 as recommendation information about the messenger user. At this time, the DB server 301 may also transmit acquaintance information without purchase information for the product or store included in the recommended information.

6. The messenger server 302 may provide notification of recommendation information on products or stores purchased by N or more acquaintances to acquaintances who do not have purchase information on the products or stores.

The server 150 includes the DB server 301 and the messenger server 302, but as an example, the DB server 301 and the messenger server 302 may be implemented in one platform. However, it is not limited to this, and it is also possible to implement a form in which the DB server 301 and the messenger server 302 are constructed as separate systems and provide an information recommendation service through interlocking.

Therefore, the server 150 builds a DB of purchase information of messenger users, and based on this DB, can identify products and shops that are frequently traded among acquaintances and recommend them to users who have no purchase history yet. .

FIG. 4 is a block diagram illustrating exemplary components that a processor of a server may include in one embodiment of the present invention, and FIG. 5 is a block diagram of what the server performs in one embodiment of the present invention. 4 is a flow chart showing an example of a possible method;

The server 150 according to the present embodiment serves as a platform that provides an information recommendation service to a plurality of electronic devices 110, 120, 130, and 140, which are clients. Server 150 may provide information recommendation services in conjunction with applications installed on electronic devices 110 , 120 , 130 , and 140 .

The processor 222 of the server 150 may include a DB configuration unit 410, an information generation unit 420, and an information recommendation unit 430 as shown in FIG. 4 as components for executing the information recommendation method according to FIG. Depending on the embodiment, one or more components of processor 222 may be selectively included or excluded from processor 222 . Also, in some embodiments, the components of processor 222 may be separated or combined to perform the functionality of processor 222 .

Such processor 222 and components of processor 222 may control server 150 to perform steps 510-540 included in the information recommendation method of FIG. For example, processor 222 and components of processor 222 may be implemented to execute instructions according to the code of an operating system and the code of at least one program that memory 221 contains.

Here, the components of processor 222 may be representations of different functions of processor 222 that are executed by processor 222 according to instructions provided by program code stored on server 150 . For example, the DB configuration unit 410 may be used as a functional representation of the processor 222 that controls the server 150 according to the above-described instructions so that the server 150 configures the messenger user's purchase information as a DB. Note that "functions" may also be referred to as "modules", and functions and modules may be implemented by software and/or hardware.

At step 510 , processor 222 may read the necessary instructions from memory 221 loaded with instructions associated with controlling server 150 . In this case, the read instructions may include instructions for controlling processor 222 to perform steps 520-540 described below.

In step 520, the DB configuration unit 410 may acquire purchase information of a messenger user and configure a DB including purchase information for each user. Referring to FIG. 6, the DB configuration unit 410 acquires purchase information received from the electronic device 1 (110) through SMS or a notification account in conjunction with a messenger application installed in the electronic device 1 (110) of the user ( S601), a DB may be constructed by matching such purchase information with user information and storing them. At this time, the user information means information for identifying a messenger user, and may include, for example, a messenger ID. In addition to acquiring the user's purchase information from the electronic device 1 (110) in conjunction with the messenger application, the DB configuration unit 410 acquires other servers 160 (for example, a shopping server, a payment server, etc.) with which the user's messenger ID is linked. ), it is also possible to acquire the user's purchase information from another server 160 (S602). For example, when a user accesses a shopping mall using a messenger ID and purchases a product, purchase information of the user may be obtained directly from the shopping mall. The DB configuration unit 410 may acquire the purchase information of the messenger user in real time when the purchase activity is established or when the purchase information is received through the SMS and the notification account, but the purchase information during the period is collectively collected at regular intervals. It is also possible to obtain with

As shown in FIG. 7, the DB configuration unit 410 matches and stores user information 710 and purchase information 720 for each purchase activity of a user (User1, 2, 3, . may be constructed. At this time, the purchase information 720 includes information (i.e., shop information) regarding the shop (shops 1, 2, 3, . Item 1, 2, 3, . Store information and product information mean information for identifying objects, and may include, for example, names, unique numbers, identification codes, and the like.

In the case of shop information and product information, different notations are used depending on the entity (for example, card company, member store, etc.) that creates the purchase information, so it is necessary to build the purchase information DB 700 with standardized notations. Referring to FIG. 8, product DB 801 and store DB 802 are constructed by collecting the descriptions of products and stores used by each subject from each entity that creates purchase information and storing the corresponding descriptions together with standardized descriptions. you can For example, in the case of product information, the same product may be described in different languages (English, Korean, etc.) or with different names. , the notation may be converted into a standardized notation of the product and stored in the purchase information DB 700 . For example, if product A is described as "Item aa" in shopping mall a and "Item aaa" in shopping mall b, the purchase information DB 700 stores "Item aa" and "Item aaa" as "Item1 ” to be unified and stored. Similarly, store information can be written in different ways, so the purchase information DB 700 refers to the store DB 802 for the acquired store description, and converts it to the standardized description of the corresponding store. can be stored. Therefore, the DB construction unit 410 can construct the purchase information DB 700 by matching various descriptions of product information and store information acquired from purchase information to one unified description.

Referring to FIG. 5 again, in step 530, the information generating unit 420 searches the purchase information DB 700 for a user group (hereinafter referred to as an "acquaintance group") in which a relationship is set between users on messenger, and Recommendation information for products and/or stores purchased by a certain number of people (N people) or more may be generated. As an example, when the purchase information of any user is stored in the purchase information DB 700 (that is, when the purchase occurs), the information generation unit 420 acquires the acquaintance group of the user who made the purchase. After that, for each acquaintance group acquired, it is determined whether or not there is an acquaintance group with N or more acquaintances who have purchased the same product or store, and the corresponding acquaintance group(s) exists. If so, recommendation information for the product or store may be generated. When the purchase information of an arbitrary user is stored in the purchase information DB 700, the information generation unit 420 acquires one of at least one acquaintance group to which the arbitrary user belongs on the social network, and assigns the acquired acquaintance group As a result of searching the purchase information DB 700 for the purchase information of the belonging user, if there is more than a certain number of purchase information corresponding to the purchase information of an arbitrary user, the recommendation information for the corresponding purchase information is sent to the corresponding acquaintance group. It may be generated as information.

When an arbitrary user and another user purchase the same product or products belonging to the same product group at the same store, or when an arbitrary user and another user purchase the same product or products belonging to the same product group at different stores In that case, it may be determined that the purchase information of any user and the purchase information of other users correspond to each other. At this time, if it is determined that N or more acquaintances have purchased the same product or store within a predetermined time (for example, one week) for the acquaintance group, recommendation information for the corresponding product or store can be generated. In other words, the information generating unit 420 can identify products or stores that have been purchased more than a predetermined number of times within a certain period of time, or that have been purchased more than the previous period.

As another example, the information generation unit 420 may use the purchase information DB 700 to generate, as recommendation information, a list of products and stores purchased by acquaintances in the acquaintance group during each predetermined cycle. At this time, the acquaintance group is composed of others who have a relationship with the user who made the purchase, or is a social group created according to criteria such as a dialogue group or interest, and the user is a social group. Contained groups may be meant. For example, for user A who made a purchase, user A's friends and neighbors, other people with whom user A has a relationship such as subscription, dialogue partners in a group chat room in which user A participates, and common interests with user A A social group to which the user belongs may correspond to the acquaintance group.

Therefore, the information generation unit 420 searches the purchase information DB 700 for whether or not there are N or more pieces of purchase information for the same product or store for the acquaintance group in which the user A is set or the user A is included. If it exists, information about the corresponding product or store may be generated as recommendation information. It goes without saying that each time a purchase is made, an individual acquaintance group may be acquired based on the user of the purchase, but the acquaintances to be serviced, such as the common part of acquaintances between users and the union of acquaintances of users A group may be predetermined.

In operation 540, the information recommending unit 430 may provide purchase recommendation information to users who do not have purchase information corresponding to the purchase recommendation information among the users belonging to the acquaintance group. At this time, a non-purchasing user is a user who belongs to an acquaintance group and does not have purchase information matching the recommended product or store generated as the purchase recommendation information (that is, a user recommended in the purchase information DB 700). It may mean a user who has no purchase history for the target product or store). The information recommendation unit 430 may provide recommendation information on products and/or stores purchased by a certain number (N) or more of the acquaintance group to users who do not have the corresponding purchase information. In other words, the information recommending unit 430 may provide recommendation information on products or shops purchased by N or more acquaintances by push notification of messenger to acquaintances who do not have purchase information on the corresponding products or shops. At this time, the information recommendation unit 430 may provide recommendation information including price information using the purchase information DB 700 . For example, the information recommendation unit 430 may recommend purchase information including information on the lowest price for products or shops purchased by N or more acquaintances. As another example, the information recommendation unit 430 may provide a push notification when there is a change in price information of a product or store purchased by N or more acquaintances, for example, when the price drops. Furthermore, the information recommendation unit 430 may use the purchase information DB 700 to provide, as recommendation information, the products purchased by acquaintances during the cycle and the list of stores at regular intervals.

Information recommendation criteria may include the number of people, frequency, etc., which may be defaulted to be commonly applied to all messenger users, or may be set individually for each messenger user. is also possible. For example, the number of people (N) for information recommendation is set to 5 so that when 5 acquaintances among users of each messenger purchase the same product or store, they can receive a recommendation for the corresponding product or store. Alternatively, the cycle for information recommendation may be set to one day (1 day) so that the product purchased by an acquaintance the day before and the recommendation of the store list are received every day.

9 and 10 are diagrams showing an example of the information recommendation process in one embodiment of the present invention. In the following, it is assumed that the number of persons (N) for information recommendation is three.

Referring to FIG. 9, when the number of users 901 who have purchased the same product in the acquaintance group 900 is three or more, the information recommendation unit 430 may recommend the corresponding product information to the users 902 who have not purchased the product. When the number of purchasing users 901 reaches three, which is the number of users set as the information recommendation criteria, product information similarly purchased by the relevant personnel may be recommended to non-purchasing users 902 .

As shown in FIG. 10, when there are three purchasing users 1001 in both the first acquaintance group 1010 and the second acquaintance group 1020, the information recommending unit 430 selects the item information that the relevant person has purchased as unpurchased. A recommendation may be made to the user 1002 . At this time, in the case of some users 1003 who belong to both the first acquaintance group 1010 and the second acquaintance group 1020 among the non-purchasing users 1002, they correspond to the first acquaintance group 1010 and the second acquaintance group 1020. If purchase information is generated as recommendation information, there is a concern that it will be recommended redundantly. In such a case, the number of times the corresponding recommendation information is provided may be limited. In other words, when the same product is generated as recommendation information for the first acquaintance group 1010 and the second acquaintance group 1020, the information recommending unit 430 may limit the number of times the corresponding product is recommended. Therefore, in the case of a user belonging to two or more acquaintance groups, the number of recommendation notifications for the same product is limited to a certain number or less in order to prevent redundant reception of recommendation notifications for the same product. good. As another example, it is also possible to repeatedly provide recommendation notices for the same product step by step. For example, when the number of users 1001 reaches 3, 6, 9, etc., as a constant multiple of the set number of users, a recommendation notification for products purchased by acquaintances is sent in stages. may provide. Furthermore, the recommendation notification may include the number of purchasers. The number of recommendation notifications for the same product may be set by default, or may be set directly by each messenger user.

As described above, according to the embodiment of the present invention, by providing recommendation information on a social network, information popular among acquaintances can be grasped and recommended based on acquaintance's purchase information.

The apparatus described above may be realized by hardware components, software components, and/or a combination of hardware and software components. For example, the devices and components described in the embodiments include processors, controllers, arithmetic logic units (ALUs), digital signal processors, microcomputers, field programmable gate arrays (FPGAs), programmable logic units (PLUs), microprocessors, Or may be implemented using one or more general purpose or special purpose computers, such as various devices capable of executing and responding to instructions. A processing unit may run an operating system (OS) and one or more software applications that run on the OS. The processing unit may also access, store, manipulate, process, and generate data in response to executing software. For convenience of understanding, one processing device may be described as being used, but those skilled in the art will appreciate that the processing device may include multiple processing elements and/or multiple types of processing elements. You can understand. For example, a processing unit may include multiple processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include computer programs, code, instructions, or a combination of one or more of these, to configure a processor to operate at its discretion or to independently or collectively instruct a processor. You can Software and/or data may be embodied in any kind of machine, component, physical device, computer storage medium, or device for interpretation by, or for providing instructions or data to, a processing device. good. The software may be distributed over computer systems connected by a network so that they are stored and executed in a distributed fashion. Software and data may be stored on one or more computer-readable media.

The method according to the embodiments may be embodied in the form of program instructions executable by various computer means and recorded on a computer readable medium. At this time, the medium may continuously store the computer-executable program or temporarily store the program for execution or download. In addition, the medium may be various recording means or storage means in the form of single or multiple hardware combined, and should not be limited to media directly connected to a computer system, distributed over a network. may exist. Examples of media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and ROM, RAM, flash memory, etc., configured to store program instructions. Another example of the medium may be an application store that distributes applications, a site that supplies or distributes various software, or a recording medium or storage medium that is managed by a server.

As described above, the embodiments have been described based on the limited embodiments and drawings, but those skilled in the art will be able to make various modifications and variations based on the above description. For example, the techniques described may be performed in a different order than in the manner described and/or components such as systems, structures, devices, circuits, etc. described may be performed in a manner different from the manner described. Appropriate results may be achieved when combined or combined, opposed or substituted by other elements or equivalents.

Accordingly, different embodiments that are equivalent to the claims should still fall within the scope of the appended claims.

222: Processor 410: DB configuration unit 420: Information generation unit 430: Information recommendation unit

Claims (22)

An information recommendation method implemented in a computer system, comprising:
The computer system includes at least one processor configured to execute computer readable instructions contained in memory;
The information recommendation method includes:
the at least one processor constructing a DB containing purchase information for each user on a social network , matching the purchase information with user information and storing the purchase information in the DB;
said at least one processor searching said DB for purchase information of users belonging to a user group with which users have established relationships on said social network, and generating purchase recommendation information for said user group; one processor providing the purchase recommendation information to a non-purchase user for whom purchase information corresponding to the purchase recommendation information has not been generated among the users belonging to the user group; Restricting the number of times the corresponding purchase recommendation information is provided to a user belonging to a group who has not purchased the purchase recommendation information when purchase information corresponding to the two or more user groups is generated as the purchase recommendation information.
information recommendation methods, including; An information recommendation method implemented in a computer system, comprising:
The computer system includes at least one processor configured to execute computer readable instructions contained in memory;
The information recommendation method includes:
the at least one processor constructing a DB containing purchase information for each user on a social network, matching the purchase information with user information and storing the purchase information in the DB;
the at least one processor searching the DB for purchase information of users belonging to a user group with which users have relationships established on the social network, and generating purchase recommendation information for the user group;
the at least one processor providing the purchase recommendation information to a non-purchase user for whom purchase information corresponding to the purchase recommendation information has not been generated among the users belonging to the user group; limiting the number of times the purchase recommendation information for the corresponding product is provided to a user who has not purchased the product, if the same product is generated as the purchase recommendation information for the two or more user groups.
information recommendation methods, including; The configuring step includes:
3. The information recommendation method according to claim 1 or 2 , comprising obtaining purchase information of the user from the electronic device of the user in a message received by the electronic device. The configuring step includes:
3. The information recommendation method according to claim 1 or 2 , comprising obtaining the purchase information of the user from another server with which the identification information of the user on the social network is linked. the acquired purchase information includes at least one of product information and store information;
The configuring step includes:
5. The information recommendation method according to claim 3 , further comprising the step of converting the notation of the product or store acquired as the purchase information into a standardized notation and storing the standardized notation in the DB. The generating step includes:
3. The information recommendation method according to claim 1 or 2 , comprising generating purchase recommendation information for at least one of products and shops purchased by a predetermined number of users among the users belonging to the user group. The generating step includes:
3. The information recommendation according to claim 1 or 2 , comprising generating purchase recommendation information for at least one of a product and a store purchased by at least a certain number of users of the user group within a predetermined time. Method. The generating step includes:
The information recommendation according to claim 1 or 2 , comprising generating a list of at least one of products and stores purchased by users belonging to the user group during the corresponding period as purchase recommendation information at regular intervals. Method. The generating step includes:
obtaining one of at least one user group to which the arbitrary user belongs on the social network when purchase information of an arbitrary user is stored in the DB; and a user belonging to the obtained user group. searching the DB for the purchase information, and if more than a certain number of purchase information corresponding to the purchase information of the arbitrary user is found, generating recommendation information for the corresponding purchase information as the purchase recommendation information. 3. The information recommendation method according to claim 1 or 2 . 3. The information recommendation method according to claim 1 , wherein said non-purchase user is a user for whom purchase information corresponding to a product or store corresponding to said purchase recommendation information has not been generated in said user group. the purchase information for each user includes purchase price information;
The providing step includes:
3. The information recommendation method according to claim 1 or 2 , comprising providing the purchase recommendation information based on the purchase price information. The information recommendation method includes:
The at least one processor further comprises setting information recommendation criteria for providing the purchase recommendation information for each of the users, wherein the information recommendation criteria are the number of users who have made purchases and the period of recommendation. 3. The information recommendation method according to claim 1 or 2 , specified by at least one of . A computer program that causes a computer to execute the information recommendation method according to any one of claims 1 to 12 . a computer system,
a memory; and at least one processor coupled with the memory and configured to execute computer readable instructions contained in the memory;
The at least one processor
a DB configuration unit that configures a DB containing purchase information for each user on a social network, the DB configuration unit that matches the purchase information with user information and stores the purchase information in the DB;
an information generating unit that searches the DB for purchase information of users belonging to a user group in which relationships are established between users on the social network, and generates purchase recommendation information for the user group; and the users belonging to the user group. an information recommendation unit for providing the purchase recommendation information to a non-purchase user for whom purchase information corresponding to the purchase recommendation information has not been generated, wherein the non-purchase user belonging to two or more user groups is provided with the An information recommending unit that limits the number of times that corresponding purchase recommendation information is provided when purchase information corresponding to each other for two or more user groups is generated as the purchase recommendation information.
A computer system comprising: a computer system,
memory, and
at least one processor coupled to the memory and configured to execute computer readable instructions contained in the memory
including
The at least one processor
a DB configuration unit that configures a DB containing purchase information for each user on a social network, the DB configuration unit that matches the purchase information with user information and stores the purchase information in the DB;
an information generating unit that searches the DB for purchase information of users belonging to a user group in which relationships are established between users on the social network, and generates purchase recommendation information for the user group;
An information recommending unit that provides the purchase recommendation information to a non-purchase user for whom purchase information corresponding to the purchase recommendation information has not been generated among the users belonging to the user group, the information recommendation unit belonging to two or more user groups. An information recommendation unit that limits the number of times the purchase recommendation information for the corresponding product is provided to a user who has not purchased the product when the same product is generated as the purchase recommendation information for the two or more user groups.
A computer system comprising: The DB configuration unit
16. The computer system of claim 14 or 15, wherein the computer system obtains the user's purchase information from the user's electronic device in a message received by the electronic device. The DB configuration unit
16. The computer system according to claim 14 or 15, wherein said user's purchase information is obtained from another server with which said user's identification information on said social network is linked. the acquired purchase information includes at least one of product information and store information;
The DB configuration unit
18. The computer system according to claim 16 or 17, wherein the notation of the product or store acquired as the purchase information is converted into a standardized notation and stored in the DB. The information generation unit
16. The computer system according to claim 14 or 15, wherein purchase recommendation information for at least one of products and shops purchased by a certain number of users among the users belonging to the user group is generated. The information generation unit
16. The computer system according to claim 14 or 15, wherein a list of at least one of products and stores purchased by users belonging to said user group during a given period is generated as purchase recommendation information at regular intervals. The information generation unit
When the purchase information of an arbitrary user is stored in the DB, after acquiring one of at least one user group to which the arbitrary user belongs on the social network, the user belonging to the acquired user group Searching the DB for purchase information, and if a predetermined number or more of purchase information corresponding to the purchase information of the arbitrary user is found, generating recommendation information for the corresponding purchase information as the purchase recommendation information. 16. The computer system according to 14 or 15. the purchase information for each user includes purchase price information;
The information recommendation unit
16. A computer system according to claim 14 or 15, wherein said purchase recommendation information is provided based on said purchase price information.

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