KR20190013277A - System and method for recommending mobile commerce information using big data - Google Patents

Abstract

The present invention relates to a mobile commerce service. More specifically, the present invention relates to a mobile commerce information recommendation system and a method thereof, capable of providing ranking information or recommendation information of a mobile commerce store or product in consideration of a user characteristic by a country. According to an embodiment of the present invention, the mobile commerce information recommendation system and the method thereof may analyze ranking of a commerce store or product based on big data and recommend a commerce store or product by reflecting a characteristic of a user, thereby increasing convenience of the user and activating a mobile commerce service. The mobile commerce method comprises the steps of: collecting big data related to mobile commerce through a communication network; generating ranking information with respect to a store or a product by analyzing a satisfaction level and influence from the collected big data; providing the ranking information to a mobile terminal of a user; receiving a purchase request, including product information which the user wants to purchase and store information, from the mobile terminal of the user; transmitting a mobile gift certificate or order information to the mobile terminal of the user in accordance with the purchase request; and processing a calculation on the store which has used the mobile gift certificate or the order information.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a system and method for recommending mobile commerce information using Big Data,

The present invention relates to a mobile commerce service, and more particularly, to a mobile commerce information recommendation system and method that can provide ranking information or recommendation information of a mobile commerce store or a product in consideration of characteristics of users by country.

Electronic commerce services are spreading as network technology such as IT technology and the Internet develops. E-commerce is generally defined as business to business (B2B), business to customer (B2C), customer to customer (C2C) Business to Government (B2G), which includes a series of actions such as purchasing, shipping, marketing, and providing services, as well as moving money through a wired or wirelessly connected network system. .

Especially, nowadays, with the spread of smart phones, there are a growing number of users who install and use various applications in mobile terminals. As a result, e-commerce (commerce) services using mobile terminals are widely available. However, in the case of Commerce using mobile terminals, there is a limitation in analyzing a large amount of big data for a store where payment is made. Especially, in case of mobile commerce service targeting users of multiple countries, There is a difficulty in providing efficient services due to the difference in the orientation, sensitivity, and the like.

For example, Patent Publication No. 10-0993818 (web crawling system and method) discloses a method of expanding an analysis target from a seed page to a general web site according to priority, There is a problem in that it is not possible to provide the efficiency of the big data processing which is essential for the service. In addition, although Patent Publication No. 10-0975502 (a method and system for determining ranking of blog posts) discloses a method of calculating a post ranking by assigning an authority score to a post of a blog, Or country-specific trends. In addition, Japanese Laid-Open Patent Application No. 10-2008-0002187 discloses a method of analyzing a customer's emotions using a user's answer to a specific question, , And can not provide a specific analysis method for mobile commerce stores. In addition, Patent Publication No. 10-0963996 (an apparatus and method for recommending personalized products based on emotion) discloses a method of recommending a product by predicting a user's sensibility according to weather or a region, There is a problem that it is difficult to accurately reflect the tendency of

Patent Registration No. 10-0993818 (Nov. 12, 2010) Patent Registration No. 10-0975502 (Aug. 11, 2010) Open Patent Publication No. 10-2008-0002187 (2008.01.04) Patent Registration No. 10-0963996 (Jun. 15, 2010)

SUMMARY OF THE INVENTION It is an object of the present invention to provide a mobile commerce information recommendation system and method capable of providing ranking information or recommendation information of a mobile commerce store or a product in consideration of characteristics of a user.

In order to achieve the above object, a mobile commerce information recommendation method of the present invention is a method for providing a mobile commerce service in a mobile terminal of a user and a commerce server interfaced with a POS terminal of a store, The method comprising the steps of: collecting related big data; calculating a recommended index by comparing the brand image of the mobile commerce store or product with the personal image of the user based on the big data; The method comprising the steps of: selecting a recommended store or a recommended product; providing information on the recommended store or recommended product to the user's mobile terminal; and receiving a purchase request including goods and store information to be purchased from the user's mobile terminal , Receiving said purchase According to the blue may include the step of proceeding the settlement targeting mobile voucher or sending the order information to the user of the mobile terminal and the mobile voucher or order information that the store use.

The step of collecting the big data may use at least one of a crawler, a focused crawler, a topical crawler, and a wrapper crawler.

The collecting of the big data includes a purification step of removing data not related to mobile commerce, a preprocessing step of removing characters, symbols, or numbers not used in analysis, and an extraction step of extracting words based on nouns .

The extracting step may extract at least one of a name, a product name, a region name, a recommendation number, an evaluation score, a sales amount, and a price of a mobile commerce store.

The brand image can be evaluated based on five factors: sophistication, sincerity, ruggedness, excitement, and competence.

The five elements may additionally include detailed attributes and characteristics below them.

The personal image may comprise a real image or an ideal image.

The step of calculating the recommendation index may be based on a sum of differences between the brand image and the actual image.

The step of calculating the recommendation index may be based on a sum of differences between the brand image and the ideal image.

The step of calculating the recommendation index may be calculated by giving a weight according to the user's country, region, or user's tendency.

In the step of selecting the recommended store or recommended product, the recommendation index can select the store or the product having the smallest value or the largest value.

The mobile commerce information recommendation method may further include generating rank information on a mobile commerce store or a product by analyzing satisfaction and influence from the collected big data.

The satisfaction level may be evaluated based on at least one of a quality of food, a quality of service, and a quality of atmosphere.

The influence may be assessed using at least one of: recentness that indicates how recently a user has purchased a mobile commerce product, frequency that indicates how often a product was purchased, and epidemics that indicate when the popularity of the product is maintained, Lt; / RTI >

The step of generating the ranking information may be calculated by adding up the sum of the influence of the mobile commerce store for the user and the average value of the satisfaction evaluated by the plurality of users.

The ranking information can be calculated by weighting the average value of the satisfaction.

The weights may be assigned differently depending on the language, custom, tendency, or emotion of the user's country or region.

Further, the mobile commerce information recommendation system of the present invention is a system for providing a mobile commerce service in cooperation with a mobile terminal of a user and a POS terminal of a shop, the system comprising a mobile terminal for collecting and processing big data related to mobile commerce through a communication network, A recommendation module for comparing the brand image of the mobile commerce store or product with the personal image of the user on the basis of the big data and selecting a recommended store or recommendation product; The mobile terminal receives the purchase request including the goods and shop information to be purchased from the mobile terminal of the user and transmits the mobile gift certificate or the order information to the user's mobile terminal according to the purchase request, Or, Targeting a used store may include a payment module to proceed with the settlement.

The recommendation module may calculate a recommendation index based on a sum of the difference between the brand image and the personal image of the user.

The brand image can be evaluated based on five factors: sophistication, sincerity, ruggedness, excitement, and competence.

The personal image may comprise a real image or an ideal image.

The recommendation index can be calculated by weighting according to the user's country, region, or user's tendency.

The mobile commerce information recommendation system may further include a content analysis module for analyzing satisfaction and influence from the collected big data to generate ranking information for a shop or a product.

The content analysis module may calculate the ranking information by adding the sum of the influence of the mobile commerce store to the user and the average value of the satisfaction evaluated by the plurality of users.

The ranking information can be calculated by weighting the average value of the satisfaction.

The weights may be assigned differently depending on the language, custom, tendency, or emotion of the user's country or region.

According to another aspect of the present invention, there is provided a method for providing a mobile commerce service in a commerce server interfaced with a mobile terminal of a user and a point-of-sale terminal of a store, the method comprising: collecting big data related to a mobile commerce through a communication network; Comparing the brand image of the mobile commerce store or product with the personal image of the user based on the big data to calculate a recommendation index; selecting a recommendation shop or recommendation product for a specific user based on the recommendation index; And providing information on the recommended store or recommendation product to the mobile terminal of the user.

According to another aspect of the present invention, there is provided a mobile commerce information recommendation program, comprising: collecting big data related to mobile commerce through a communication network in a server providing a mobile commerce service, in cooperation with a user's mobile terminal and a POS terminal of a shop; Comparing the brand image of the mobile commerce store or product with the personal image of the user based on the big data to calculate a recommendation index; selecting a recommended store or recommendation product for a specific user based on the recommendation index; And providing information on the recommended store or recommendation product to the mobile terminal of the user.

According to the mobile commerce information recommendation system and method according to the embodiment of the present invention, the ranking of the commerce store or commodity is analyzed on the basis of the big data, the commerce store or the commodity is recommended by reflecting the characteristics of the user, And the mobile commerce service can be activated.

1 is an overall configuration diagram of a mobile commerce system according to an embodiment of the present invention;
2 is a diagram illustrating an internal system of a commerce server according to an exemplary embodiment of the present invention,
3 is a flowchart illustrating a data processing procedure of a big data system in a mobile commerce method according to an embodiment of the present invention.
4 is a conceptual diagram of a contents system in a mobile commerce system according to an embodiment of the present invention,
5 is a diagram illustrating a process of mobile settlement through a third settlement system provided for each country in a mobile commerce method according to an embodiment of the present invention.
FIG. 6 is a diagram illustrating a process of directly making a mobile settlement through a commerce server in a mobile commerce method according to an embodiment of the present invention; FIG.
FIG. 7 is a block diagram illustrating an internal configuration of a recommendation system in a mobile commerce system according to an embodiment of the present invention.
8 is a view illustrating detailed information for evaluating a brand image in a mobile commerce system according to an embodiment of the present invention,
9 is a diagram illustrating software configuration of a mobile commerce server according to an embodiment of the present invention.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms as used herein include plural forms as long as the phrases do not expressly express the opposite meaning thereto. Means that a particular feature, region, integer, step, operation, element and / or component is specified, and that other specific features, regions, integers, steps, operations, elements, components, and / And the like.

Unless otherwise defined, all terms including technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Commonly used predefined terms are further interpreted as having a meaning consistent with the relevant technical literature and the present disclosure, and are not to be construed as ideal or very formal meanings unless defined otherwise.

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

FIG. 1 shows an overall configuration of a mobile commerce system according to an embodiment of the present invention. 1, the mobile commerce system of the present invention includes a mobile terminal 100 capable of facilitating the functions and features described herein, a communication network 200, a commerce server 300, -Of-Sale) terminal 400. Here, the store may include not only a restaurant selling food, but also various online or offline shops selling clothes or various other goods.

The mobile terminal 100, the commerce server 300 and the POS terminal 400 may be implemented as a processor, a controller, an integrated circuit, a programmable logic circuit, or any other combination of any number or combination thereof, And signal processing devices, as well as communication modules capable of communicating with one or more memories, transmitters and receivers, displays, and various devices. In addition, it includes a computer program that performs data operation through a specified algorithm and displays the result so that the result can be recognized from the outside through a display, and a user interface (UI) .

The computer program includes a plurality of code segments executable in the mobile terminal 100, the commerce server 300, and the POS terminal 400 to perform the respective steps in the electronic money transaction method of the present invention . At this time, each step of the mobile commerce method may be performed according to the order shown in the drawings, but each step may be performed in a different order, unless explicitly stated. Also, some of the steps may be performed simultaneously, and some of the steps may be performed selectively.

The mobile terminal 100, the commerce server 300, and the POS terminal 400 may include electronic devices capable of accessing one or more general computing resources such as Internet services, e-mail services, data transfer services, and the like. It may also include a user database that stores user specific information for accessing online resources via the communications network 200 and the database may store other information and data needed to implement the mobile commerce method of the present invention .

The mobile terminal 100, the commerce server 300, and the POS terminal 400 may include any device or equipment, including memory associated with the processor. A processor may implement an operating system and execute a computer program known as an instruction, software code, executable, application, app, or the like. The processor may include a microcontroller, a field programmable gate array (FPGA), or the like, or a combination thereof. The memory may store or hold computer programs and may store text, databases, graphics, audio, video, combinations thereof, and other data. The memory may be referred to as a computer-readable medium or a computer-readable storage medium and may be embodied in a variety of forms, including RAM (Random Access Memory), ROM (Read Only Memory), flash drive, floppy disk, hard disk, compact disk (Digital Video Disc), or the like, or a combination thereof. The mobile terminal 100 and the commerce server 300 and the POS terminal 400 can be used together with such memory to store a plurality of hard disks, a network attached storage, or a file storage and may further include a file store.

The mobile terminal 100, the commerce server 300 and the POS terminal 400 may be specifically configured for mobile communication devices (including wireless devices), workstations, desktop computers, laptop computers, palmtop computers, tablet computers, a digital assistant, a smart phone, a scanner, a kiosk, a printer, or a combination thereof.

Various embodiments of mobile terminal 100 may include voice or data communication devices such as cellular phones, cell phones, smart phones, laptops, and the like.

The mobile terminal 100, the commerce server 300 and the POS terminal 400 will have an electronic display such as a cathode ray tube, liquid crystal display, plasma, or touch screen that displays visual graphics, images, text, According to an embodiment, the computer program of the present invention facilitates interaction and communication via a Graphical User Interface (GUI) displayed through an electronic display. The GUI allows the user to interact with the electronic display by touching or pointing display areas to provide information of the user (i. E., A consumer, a commerce representative, or a manager) to the user control interface.

The mobile terminal 100 and the commerce server 300 and the POS terminal 400 may also include optical devices such as a digital camera, a video camera, an optical scanner, etc. so as to capture, store and transmit digital images or videos. have.

The mobile terminal 100, the commerce server 300, and the POS terminal 400 may include a user control interface to allow one or more users to share information and instructions. The user control interface may include one or more function inputs such as buttons, keyboards, switches, scroll wheels, voice recognition elements such as a microphone, and pointing devices such as a mouse, touchpad, tracking ball, and stylus . The user control interface may also include a speaker for providing audible instructions and feedback. The user control interface may also include a wired or wireless data transmission component, such as a communication component, a removable memory, a data transceiver, or a transmitter, to enable the user or other electronic device to interface remotely.

The communication network 200 may be wired or wireless and may include an electrically conductive cable or an optical cable, as well as a server, a router, a switch, a wireless receiver and a transmitter, and the like. The communication network 200 may also include a local area network (LAN), a metro area network (MAN), or a wide area network (WAN) as well as the Internet or other cloud networks. In addition, the communication network 200 may include a cellular or mobile telephone network, as well as a landline phone network, a public switched telephone network (PSTN), an optical fiber network, and the like.

The mobile terminal 100, the commerce server 300, and the POS terminal 400 may both be connected to the communication network 200. The mobile terminal 100, the commerce server 300 and the POS terminal 400 may communicate with the other mobile terminal 100 or the POS terminal 400 through the communication network 200. The commerce server 300 and the POS terminal 400 may be connected to a wired or wireless communication network 200. The mobile terminal 100, the commerce server 300 and the POS terminal 400 may comprise suitable components for establishing a wired or wireless connection.

The computer program of the present invention can be executed in the mobile terminal 100, the commerce server 300 and the POS terminal 400. [ As such, a first portion of the program, code, or instructions may be executed in the first mobile terminal 100, the commerce server 300 and the POS terminal 400, while the second portion of the program, code, May be executed in the second mobile terminal 100, the commerce server 300, and the POS terminal 400. [ In some embodiments, the program, code, or other portions of the instructions may be executed in another electronic device. For example, information specific to each user may be stored in the memory of the commerce server 300, so that information specific to each user may be provided to the user or manager using one or more mobile terminals 100, Lt; / RTI > In an embodiment of the present invention, a portion of the information embodying the invention may be stored in the commerce server 300, while other portions of information may be stored in one or more mobile terminals 100. The various operations, analyzes, and calculations described herein that are performed by a computer program or by using a computer program that actually executes portions of a computer program independently or in concert, May be performed by one or more computers, processors, or other devices, such as a commerce server 300 or a POS terminal 400. [

In an embodiment of the invention, the computer program may be embodied as a stand-alone program installed in a user's mobile terminal 100 or as a web accessible program accessible by the user's mobile terminal 100 via the communication network 200 . In the case of a standalone program, a downloadable version of the computer program may be stored, at least in part, in the commerce server 300. The user may download at least a portion of the computer program to the mobile terminal 100 via the communication network 200. [ In an embodiment of the invention, the computer program may be an application, such as an app for mobile terminal 100. [ After the computer program is downloaded, the program may be installed in the mobile terminal 100 in an executable format. The executable form of the program allows the user to access embodiments of the invention via electronic resources, such as mobile apps or websites. In the case of a web accessible computer program, the user can simply access the computer program via the communication network 200 using the mobile terminal 100.

2 is a diagram illustrating an internal system of a commerce server according to an embodiment of the present invention. 2, the commerce server 300 of the present invention may include a big data system 310, a content system 320, a billing system 330, a recommendation system 340, and a communications system 350 have.

Big data system 310 is responsible for collecting and processing atypical big data, structured big data, and sales big data related to mobile commerce. Specifically, atypical big data can be targeted to user's recommendation data about a product or service, collected through the web, refined and processed. Ordinary big data can be targeted to consumer purchase behavior data collected through mobile application. Revenue Big Data targets sales data of a store through mobile transactions. In particular, since the mobile commerce system of the present invention provides a mobile commerce service to users of a plurality of countries, it is necessary to collect and process big data scattered in online web pages of various countries.

Content system 320 is responsible for analyzing related content for ranking analysis of stores providing mobile commerce services of the present invention. In order to analyze the ranking of the stores, the user 's satisfaction with the stores and the influence figures of the stores and users can be utilized.

The billing system 330 allows a user to make transactions in a shop using the mobile terminal 100 and to make a mobile payment for a purchase item. Since the mobile commerce system of the present invention can be targeted to users in various countries, it is preferable to implement the mobile commerce system so that it can interoperate with a mobile settlement system or a mobile shopping mall that is used in each country.

The recommendation system 340 is responsible for recommending a shop or a commodity that provides mobile commerce to the user in accordance with the characteristics of the user. To do this, we can measure the brand image of the mobile commerce store, compare it with the user 's personal image, and select the recommended store or product by reflecting the difference. At this time, it will be possible to assign different weights depending on the language, custom, tendency, or emotion of the user's country or region.

The communication system 350 is a part that enables a user to conduct conversation or chat with a mobile commerce store or a product with another user using an application installed in the mobile terminal 100. Since the mobile commerce system of the present invention is intended for users of a plurality of countries, it is preferable to implement the mobile commerce system in cooperation with the SNS used in various countries. For example, a common software module server can be provided, and various SNSs can be linked through the common software module server.

3 is a flowchart illustrating a data processing procedure of a big data system in a mobile commerce method according to an embodiment of the present invention.

Referring to FIG. 3, the big data system 310 preferably collects data displayed in real time on SNSs such as Twitter and Facebook. Therefore, it is effective to formalize the natural language, to extract meaningful words related to the mobile commerce store, to extract it, and to database it. For example, in a case where a restaurant where a mobile payment is available, the user inputs a keyword (e.g., a restaurant, a restaurant name, a product name, a recommendation number, a score, a sales amount, a price, etc.) In that case, you will be able to select and display related questions and candidate answers.

On the other hand, it is also possible to analyze unstructured data created by a user on a web site or a blog in association with mobile commerce stores as well as stereotyped data, and extract and collect necessary data only. In order to collect such data, a web crawling method can be used (S311). Web crawling is used in Internet search engine, meaning that a web crawler finds a new web page or updated web page and downloads it to the information search engine. Web crawling prioritizes web pages according to the access probability of each web page, and preferentially downloads and designates the web pages sequentially from the high priority web page to the information search engine. These web crawlers can quickly and accurately collect web pages of a certain subject, such as general crawlers, focused crawlers, topical crawlers, and wrapper crawlers. . In the mobile commerce system of the present invention, a wrapper-based web crawler can be used to quickly and accurately collect web documents of a certain subject, such as a user's satisfaction evaluation. At this time, if the structure of the web site to be collected is analyzed in advance, the web crawler can access the data and collect desired data more accurately. In addition, since the mobile commerce system of the present invention is intended for users of a plurality of countries, it is possible to expand the data collection target country to a plurality of countries and collect unstructured data of a multilingual word from web sites, blogs, It is desirable to implement a web crawl for major languages such as English and Chinese.

The big data collected through the web crawler can remove data not related to mobile commerce through a data refinement process (S312). For example, in the case of collecting data related to food, texts with a high frequency of irrelevant words such as computer or clothing may be excluded from the collection target.

The text selected through the data refinement process is subjected to a preprocessing process to remove characters, symbols, numbers, and the like that are not used in the analysis (S313). For example, in the mobile commerce system of the present invention, it is efficient to conduct data analysis around a noun, and in this case, parts of speech, articles, prepositions, conjunctions, and exclamations may be removed through a preprocessing process . In addition, special characters or numbers within the text, such as parentheses ([]), double quotation marks (""), single quotation marks (" have. However, since the mobile commerce system of the present invention is intended for a plurality of countries, it is desirable to set the preprocessing rules by reflecting characteristics of languages such as Korean, English, Chinese, and Japanese.

Thereafter, words are extracted from the nouns in the data subjected to the pre-processing (S314). Since the mobile commerce system of the present invention recommends the mobile commerce store to the user based on the location, it is necessary to extract information on the name and location of the store from the refined and preprocessed data (S315, S316). Since the name and location of the store correspond to nouns, it is desirable to extract related data around nouns. In addition, the name of a product to be handled by a shop can also be extracted together with it.

In this manner, the name, location, and product data of the mobile commerce store extracted from the big data can be separately stored in the shop DB 312, and the data stored in the store DB 312 can be stored in the content system 320 It can be used to analyze the ranking. Meanwhile, data extracted from a user name (including a nickname, an alias, etc.) and an evaluation opinion that have started a comment on a specific shop through a web site, a blog, an SNS, or the like can be divided into a separate customer DB 314. Of course, since the data stored in the store DB 312 and the data stored in the customer DB 314 may have mutually related data or redundant data, they may be classified into separate DBs, but they may be integrated into one DB May be stored.

4 is a conceptual diagram of a contents system in a mobile commerce system according to an embodiment of the present invention. Referring to FIG. 4, content system 320 may perform ranking analysis using satisfaction and influence figures for a mobile commerce store.

User satisfaction with mobile commerce stores can be analyzed by analyzing attributes by focusing on three aspects: product, service, and mood. For example, if a restaurant is available for mobile payment, the factors for satisfaction analysis are the quality of food, the quality of service, , And Quality of Atmosphere. In this case, the quality of the food (commodity) is referred to as 'taste', and the service quality and the atmosphere quality can be measured by the evaluation results of the service satisfaction and the atmosphere inside the restaurant, respectively. The detailed evaluation criteria are, for example, very good (1), not very good (1.5), slightly lacking (2), just so (2.5), moderate (3), okay (3.5) , Very good (4.5), and best (5). Service quality and mood quality can be assessed on the same basis and the final rating for satisfaction is very poor (1), not very good (1.5), slightly lacking (2), just so (2.5) , It's okay (3.5), good (4), very good (4.5), best (5).

At this time, since the mobile commerce system of the present invention is targeted to users of a plurality of countries, it is possible to reflect attributes of users by country that are different by weighting the goods, service, or atmosphere for each country.

The impact of a mobile commerce store can be analyzed using the recentness that indicates how recently a user has purchased a mobile commerce product, the frequency of how often a product was purchased, and the pandemic that indicates when the popularity of the product is maintained have. This can be analyzed in the same way for mobile commerce stores as well as for commodities. For example, a last name can be measured by collecting reviews created after a user purchases a product from a store and calculating the difference between the date of creation of the most recent rating review and the current date. Frequency attribution can be measured by summing up the number of reviews created after a user purchases a product from a store. The pandemic can be measured based on the number of times the store has been mentioned in other blogs, websites, social networks, etc., after the user has written an evaluation review for the store.

Based on the above-mentioned satisfaction and influence, it is possible to analyze the ranking of mobile commerce stores. The ranking analysis algorithm can be calculated, for example, according to the following equation (1).

Here, PS is a value indicating the influence of the store to the user, and CS is a value indicating satisfaction with the mobile commerce store. Therefore, the ranking value of the j store can be calculated by adding the sum of the influences of the influenced users from the j store and the average value of the satisfaction evaluated by the plurality of users. In this case, the average value of the user satisfaction may be varied depending on the characteristics of the country or region or the characteristics of the product, .

Table 1 below shows an example of calculating the ranking score for stores 1 and 2 located in the same area. Stores 1 and 2 have the same effect on users, but there is a difference in the average value of the users' satisfaction, so that there is a difference in the ranking scores.

j

AVG (CS _j ) AVG (CS _j ) * ω RANK Store 1 51 3.38 3.38 * 15 = 50.63 101.63 Store 2 51 4.17 4.17 * 15 = 62.50 113.50

FIG. 5 is a flowchart illustrating a mobile settlement process using a third settlement system provided for each country in a mobile commerce information recommendation method according to an exemplary embodiment of the present invention. FIG. Fig.

The mobile commerce system of the present invention can be provided through a mobile shopping mall where goods can be purchased and paid for. The mobile shopping mall manages off-line commerce shops and products, supports ordering and payment of the commodities through the mobile terminal 100, and performs data collection and analysis of commerce stores and users. Internally, it can provide statistical functions such as registration of commodity products, inventory management and delivery management of commodity products, and sales management by date and period.

In the mobile commerce system of the present invention, when the third payment system is used in each country, settlement and settlement can be performed according to the flow of FIG. The user accesses the commerce server 300 using a mobile commerce application installed in the mobile terminal 100, and then selects a type of commodity or a voucher to request an order. At this time, the user can select the payment means for payment for the goods or gift certificates. The payment means may be a variety of payment systems provided by the relevant operators of the respective country. For example, Korea's Kakao Faye, China's Ali Faye, and Japan's Rhine Faye. The commerce server 300 transmits the product information ordered by the user to the payment system selected by the user. The payment system that receives the order information of the user transmits the payment information to the user's mobile terminal 100 and receives payment approval information from the user. The commerce server 300 receiving the payment approval information of the user transmits the mobile voucher information that can be used in the offline commerce store to the user's mobile terminal 100. The user can purchase a desired commodity at the commerce store using the mobile voucher stored in the mobile terminal 100. [ The POS terminal 400 of the commerce shop recognizes the mobile voucher stored in the mobile terminal 100 of the user and performs settlement processing and proceeds to the commerce server 300 for settlement of the used mobile voucher.

FIG. 6 illustrates a process in which a commerce server of the present invention performs a mobile settlement in cooperation with a commerce store. Referring to FIG. 6, a user accesses a commerce server 300 using a mobile commerce application installed in the mobile terminal 100, and then selects a commerce shop and a commodity to be purchased, and makes an order request. When the commerce server 300 transmits the order information of the user to the corresponding commerce shop 400, the commerce shop 400 confirms the inventory status of the ordered commodity and transmits the order reception status to the commerce server 300 do. The commerce server 300 transmits the information to the user's mobile terminal 100 when the order is completed. The user can bring the order information stored in the mobile terminal 100 and visit the designated commerce shop 400 to receive the ordered commodity. After the order is completed, the user can write a review such as a review on the purchase of the mobile product, and the commerce server 300 analyzes the review made by the user and transmits the result to the commerce shop 400, ) Can be used to establish a sales strategy.

FIG. 7 is a block diagram illustrating an internal structure of a recommendation system in a mobile commerce system according to an embodiment of the present invention. Referring to FIG. 7, the recommendation system 340 of the present invention measures a brand image of a mobile commerce store or product and compares the brand image with a personal image of the user. When the similarity with the actual image of the user is high, Select a recommended store or recommendation product based on the case where the similarity with the image is high.

In general, a user's personal image can be classified into an actual image reflecting his / her current state and an ideal image reflecting an ideal state. Since the actual image reflects the user's tendency, economic and social status at the present time, it can be regarded as a personal image of the user at the present point in time. On the other hand, since the ideal image represents the tendency or the state that the user pursues for the future or the future rather than the current point of view, the image is rather different from the personal image of the present point of view. From the user's point of view, since the present image (actual image) and the future desired image (ideal image) are both images reflecting themselves, they can have preference for a real image or a shop or product that approaches the ideal image. If the image is close to the actual image, the desire for self - consistency is satisfied, and if it is close to the ideal image (differently, it is different from the actual image), the desire for self - improvement can be satisfied.

In other words, the user feels a sense of self-definition in selecting a particular store or product, thereby communicating with others. As a result, they feel a sense of homogeneity and preference to brands that can reflect and express their own identities and tendencies, and the higher the sense of identity, the higher the brand loyalty.

Accordingly, when measuring the brand image of a mobile commerce store or product, sorting the brand image in detail, selecting a shop or a product that is close to the actual image of a specific user, or when the user approaches the ideal image And can be recommended to the user. If the brand image is close to the actual image of the user, it will correspond to a case where the self-correspondence with respect to the user's current state is high. On the other hand, when the brand image is close to the ideal image of the user, the difference is large compared to the current state of the user, but it can be said that it corresponds to a case in which the self-improvement of the user's ideal state is high.

The following formula (2) can be used to calculate the recommend index (RI) for a recommendation shop or a recommended product for each user by comparing the brand image of the commerce store or product with the image of the user.

Here, BI represents a brand image (brand image), and AI represents a user's actual image (Actual Image). When the above formula (2) is used, the recommendation index (RI) of the brand image close to the actual image of the user has a relatively low value and can be seen as a brand having high self-correspondence to the user. On the contrary, a brand image with a large difference from the actual image of the user will have a large RI value, and a shop or a product close to the ideal image of the user is a brand with a high self- .

If the ideal image is used instead of the actual image AI in the above equation 2, the brand having a small value of the recommendation index RI is a brand close to the user's ideal image, RI) value may be a brand close to the actual image of the user.

At this time, it may be possible to assign different weight to the brand image or the user's personal image depending on the language, custom, tendency, or emotion of the user's country or region.

FIG. 8 shows detailed information for evaluating a brand image in a mobile commerce system according to an embodiment of the present invention. Referring to FIG. 8, the brand image may include five attributes reflecting the characteristics of a commerce store or product, and 15 attributes. The five elements can be sophistication, sincerity, ruggedness, excitement, and competence. These five factors can be seen as a factor chosen with a focus on the symbolic function of the commerce store or commodity.

These five elements can include detailed attributes at the bottom, taking into account their respective functions. For example, the sophistication element can include two attributes, 'Upperclass' and' Charming ', and the Sincerity element includes' Wholesome', ' And may include four attributes corresponding to "honest", "down-to-earth", and "cheerful". In addition, the Ruggedness element has two attributes, 'Tough' and 'Outdoorsy'. Excitement elements include 'Up-to-date', 'Spirited' ), 'Imaginative', 'Daring', and 'Competence' elements include 'Successful', 'Reliable', and 'Intelligent' &Quot;, " and " respectively. These attributes may change some of the elements belonging to the commerce store or product category. Further, although not shown in the drawing, more specific detailed characteristics may be added to the lower portion of each attribute.

Since the brand image classified based on these factors and attributes is classified based on the characteristics that users exhibit when purchasing the mobile commerce store or product, the brand image classified for the commerce store or the product eventually becomes the user's various purchase Characteristics.

At this time, the brand image may be biased to any one of the above five elements, but it may represent various characteristics of various elements, or may represent a plurality of attributes simultaneously for the fifteen attributes. Therefore, in the case of comparing with a personal image (a real image or an ideal image) represented by a user, a personal image of a user and a brand image are compared for each element, and a recommendation index (RI) may be an effective recommendation method.

9 is a diagram illustrating software configuration of a mobile commerce server according to an embodiment of the present invention. Referring to FIG. 9, the application module 380 of the mobile commerce server 300 of the present invention includes a big data module 380 for collecting and processing atypical big data, fixed big data, and sales big data related to mobile commerce, A contents analyzing module 382 for analyzing related contents for ranking analysis of a shop providing a mobile commerce service, a user using a mobile terminal 100 to conduct transactions in a shop, A payment module 383 for making a mobile payment for a purchased product, and a recommendation module 384 for recommending a shop or a commodity to provide a mobile commerce service to a user by reflecting characteristics of each country.

The mobile commerce server 300 including the application module 380 can use various OSs as an OS of the system. The OS provides high-level commands to an API (Application Program Interface) 361 to control the operation of each application module 380. The mobile commerce server 300 according to the present invention includes a high level command processing unit 310 for identifying each application module 380 corresponding to a high level command provided from the API 361, (362). The application module control unit 370 controls the operation of each application module 380 according to a command provided from the high-level command processing unit 362. That is, the high-level command processing unit 362 identifies whether the corresponding application module 380 exists according to the high-level command provided through the API 361. If the corresponding application module 380 exists, 373, 375, and 377, or controls the transmission of the message, by decrypting the decoded command with an instruction recognizable by the application module 380. Accordingly, the application module control unit 370 is connected to the mapping unit 371, 373, 375, and 377 for the big data module 381, the content analysis module 382, the payment module 383, and the recommendation module 384, 372, 374, 376, and 378, respectively.

The big data module mapping unit 371 receives high-level commands for refining and processing the big data collected from the blog or SNS from the high-level command processing unit 362, Device level, and provides it to the big data module 381 through the big data module interface unit 372.

The content analysis module mapping unit 373 and the content analysis module interface unit 374 analyze the satisfaction and influence of the commerce store on the basis of the collected big data and ultimately perform the ranking analysis of the commerce store. Accordingly, the content analysis module mapping unit 373 receives a high-level command for using the content analysis module 382 from the high-level command processing unit 362, maps it to a device level command, 374 to the content analysis module 382.

The payment module 383 is a part responsible for mobile settlement based on the goods order information transmitted through the mobile terminal 100 of the user. The payment module mapping unit 375 receives a high-level command through the high-level command processing unit 362 for payment of a commodity at the request of the user, . The device level commands are provided to the payment module 383 via the payment module interface 376.

The recommendation module 384 is a part for recommending a commerce store or a commodity to reflect a user's nationality or purchasing propensity. Accordingly, the recommendation module mapping unit 375 receives the high-level command through the high-level command processing unit 362 to select recommendation information according to the user, Maps to the command. The device level commands are provided to the recommendation module 384 via the recommended module interface 378.

A detailed function of the API 361 configured to perform this function will be described below. The open API (Open API) is used to provide information on the mobile shop or product, ranking information and recommendation information according to the request of the user, and to open a session of the application module 380 for payment according to purchase. The Close API is used to terminate the session for the application module used. The Retrieve API (Retrieve API) is used to retrieve information that users need, such as commerce stores, merchandise, ranking information, recommendation information, and location information. A status API (Status API) is used to determine the operation status of each application module 380. The initialization API (Initialize API) has a function of initializing the application module 380 so that it can be called. The List API (List API) is used to check a list of commands that can be executed for a commerce store or commodity, ranking information, recommendation information, review information, and the like. The record API (Register API) has a function of recording information such as a purchase history of a user or a review. The API (Unregister API) is used to delete information such as a user's request or execution operation. Eventually, an individual API 361 is executed according to the application module 380 or the message transmission mode to be used, thereby executing the application module 380 for user recognition, position determination, destination guidance, and air purification functions .

However, the above-mentioned application module and its functions may be all performed in the commerce server 300, but may be processed by sharing a part of the functions by distributing the user's mobile terminal 100 and the module.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, . Therefore, it should be understood that the above-described embodiments are illustrative in all aspects and not restrictive. The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: mobile terminal 200: communication network
300: Commerce Server 400: POS terminal
310: Big Data System 312: Store DB
314: Customer DB 320: Content system
330: Payment system 340: Recommendation system
350: Communication system 361: API
362: High level command processing unit
370: Application module control unit 380: Application module
371: Big data module mapping unit
372: Big data module interface part
373: content analysis module mapping section
374: Content analysis module interface section
375: Payment module mapping section
376: Payment module interface unit
376: Recommended module mapping section
377: Recommended module interface section
381: Big data module 382: Content analysis module
383: Payment module 384: Recommendation module

Claims (28)

A method of providing a mobile commerce service in a commerce server interlocked with a POS terminal of a user's mobile terminal and a store,
Collecting big data related to mobile commerce through a communication network;
Comparing the brand image of the mobile commerce store or product with the personal image of the user based on the big data and calculating a recommendation index;
Selecting a recommendation shop or recommendation product for a specific user based on the recommendation index;
Providing information on the recommended store or recommended product to the mobile terminal of the user;
Receiving a purchase request including goods and shop information to be purchased from the mobile terminal of the user;
Transmitting the mobile gift certificate or the order information to the user's mobile terminal according to the purchase request; And
And proceeding with settlement to a shop using the mobile voucher or order information.
The method according to claim 1,
The method of claim 1, wherein collecting the big data comprises using at least one of a crawler, a focused crawler, a topical crawler, and a wrapper crawler.
The method according to claim 1,
The step of collecting the big data
A purifying step of removing data not related to mobile commerce;
A preprocessing step of removing characters, symbols, or numbers that are not used in the analysis; And
And extracting words based on the nouns.
The method of claim 3,
Wherein the extracting step extracts at least one of a name of a mobile commerce store, a product name, an area name, a recommendation number, an evaluation score, a sales amount, and a price of the mobile commerce store.
The method according to claim 1,
The brand image is evaluated based on five factors of sophistication, sincerity, ruggedness, excitement, and competence.
6. The method of claim 5,
Wherein said five elements further include detailed attributes and characteristics below said mobile commerce information recommendation method.
The method according to claim 1,
Wherein the personal image includes an actual image or an ideal image.
The method according to claim 1,
Wherein the step of calculating the recommendation index is based on a sum of differences between the brand image and the actual image.
9. The method of claim 8,
Wherein the step of calculating the recommendation index is based on a sum of differences between the brand image and the ideal image.
The method according to claim 1,
Wherein the step of calculating the recommendation index is performed by weighting according to a user's country, region, or user's tendency.
The method according to claim 1,
Wherein the step of selecting the recommended store or recommended product selects a store or a product having the smallest or largest value of the recommendation index.
The method according to claim 1,
And analyzing satisfaction and influence from the collected big data to generate ranking information for a mobile commerce store or a commodity.
13. The method of claim 12,
Wherein the satisfaction is evaluated based on at least one of a quality of food, a quality of service, and a quality of atmosphere.
13. The method of claim 12,
The influence may be assessed using at least one of: recentness that indicates how recently a user has purchased a mobile commerce product, frequency that indicates how often a product was purchased, and epidemics that indicate when the popularity of the product is maintained, Mobile commerce information recommendation method.
13. The method of claim 12,
Wherein the step of generating the ranking information includes calculating a sum of an influence of a mobile commerce store for a user and an average value of satisfaction evaluated by a plurality of users.
13. The method of claim 12,
And wherein the ranking information is weighted by calculating an average value of the satisfaction levels to calculate a mobile commerce information recommendation method.
17. The method of claim 16,
Wherein the weight is differently assigned according to the language, custom, tendency, or emotion of the user's country or region.
A system for providing a mobile commerce service in cooperation with a mobile terminal of a user and a POS terminal of a shop,
A big data module for collecting and processing big data related to mobile commerce through a communication network;
A recommendation module for comparing the brand image of the mobile commerce store or product with the personal image of the user based on the big data to select a recommended store or a recommended product; And
The method includes providing information on the recommended store or a recommended product to a user's mobile terminal, receiving a purchase request including goods and shop information to be purchased from the user's mobile terminal, receiving a mobile gift certificate or order information according to the purchase request, And a settlement module that transmits the settlement information to the user's mobile terminal and performs settlement of settlement with respect to the store in which the mobile voucher or order information is used.
19. The method of claim 18,
Wherein the recommendation module calculates a recommendation index based on a sum of the brand image and the personal image difference of the user.
20. The method of claim 19,
The brand image is evaluated based on five factors of sophistication, sincerity, ruggedness, excitement, and competence.
20. The method of claim 19,
Wherein the personal image includes a real image or an ideal image.
20. The method of claim 19,
The recommendation index is calculated by assigning weights according to the user's country, region, or user's propensity.
19. The method of claim 18,
And a content analyzing module for analyzing satisfaction and influence from the collected big data to generate ranking information for a shop or a product.
24. The method of claim 23,
Wherein the content analysis module calculates ranking information by adding the sum of the influence of the mobile commerce store to the user and the average value of the satisfaction evaluated by the plurality of users.
25. The method of claim 24,
Wherein the ranking information is calculated by weighting an average value of the satisfaction degrees.
26. The method of claim 25,
Wherein the weights are differently assigned according to the language, custom, tendency, or emotion of the user's country or region.
A method of providing a mobile commerce service in a commerce server interlocked with a POS terminal of a user's mobile terminal and a store,
Collecting big data related to mobile commerce through a communication network;
Comparing the brand image of the mobile commerce store or product with the personal image of the user based on the big data and calculating a recommendation index;
Selecting a recommendation shop or recommendation product for a specific user based on the recommendation index; And
And providing information on the recommended store or recommended product to the mobile terminal of the user.
In a server providing a mobile commerce service in cooperation with a user's mobile terminal and a POS terminal of a shop,
Collecting big data related to mobile commerce through a communication network;
Comparing the brand image of the mobile commerce store or product with the personal image of the user based on the big data and calculating a recommendation index;
Selecting a recommendation shop or recommendation product for a specific user based on the recommendation index; And
And providing information on the recommendation shop or recommendation product to the mobile terminal of the user.

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