JP2023069997A - Method, system and computer program for providing location-related recommendation according to tastes - Google Patents

Abstract

To provide: a location-related recommendation method for providing location-related recommendation according to tastes implemented in a computer system; a computer program; and a computer system.SOLUTION: The location-related recommendation method comprises: collecting user feedback related to a location S410; analyzing the user's taste by extracting characteristics of the location for which user feedback exists S420; and selecting and recommending reviewers who have tastes similar to the user's taste from among other users who use a location review service S430.SELECTED DRAWING: Figure 4

Description

There is an application for exception to loss of novelty

The following description relates to techniques for providing recommendation information associated with places.

Various evaluation systems such as star reviews, text reviews, and photo reviews are used as user reviews for places.

For example, Patent Literature 1 (published on January 6, 2020) discloses a technique for creating review information on sales floors using augmented reality.

In the place review service, after authenticating the place visited or used by the user by using the place reservation function, the receipt authentication function, etc., the authenticated place information is submitted together with the review.

User reviews of places are displayed in conjunction with other services that provide place information, such as search services and map services, as well as place review services.

Korean Patent Publication No. 10-2020-0000925

By analyzing the user's preferences based on the user feedback associated with the location, recommendation information corresponding to the user's preferences is provided.

Different recommendation logics can be applied by classifying preference levels according to the type of feedback.

Based on user's preferences, it provides person-centered recommendation information and place- and area-centered recommendation information.

A location-related 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 location-related recommendation method comprising: analyzing, by the at least one processor, user preferences using user feedback associated with a place; and, by the at least one processor, recommending information associated with a place based on features corresponding to the user preferences. A location-related recommendation method is provided, comprising providing a .

According to one aspect, analyzing the user's preference includes analyzing the user's preference by classifying a level of preference according to a type of feedback, and providing the recommendation information includes analyzing the user's preference. The method may include providing different recommended objects according to the user's tastes classified by.

According to another aspect, analyzing the user's preferences may include collecting the user's feedback based on likes and reactions to places.

According to another aspect, the step of analyzing user preferences includes at least one of log data related to location searches, log data related to receipt authentication, reservations or orders for locations, and user review analysis results for locations. The method may further include collecting the user feedback based on:

According to another aspect, the step of analyzing the user's preference includes classifying a like reaction to a place included in another user's review and a like reaction to a place visited or used by the user directly. Analyzing user preferences may be included.

According to yet another aspect, analyzing the user's preferences includes, for locations where the user feedback exists, extracting features from content associated with the location; The step of calculating a preference index for each of the features may be included.

According to another aspect, the step of providing the recommendation information may include selecting and recommending another user who has a preference for places similar to the user's preference.

According to another aspect, providing the recommendation information includes selecting and recommending other users having similar tastes based on at least one of a regional distribution according to the user's tastes and an industry category. may contain

According to another aspect, providing the recommendation information may include selecting and recommending a place having features corresponding to the user's taste.

According to another aspect, providing the recommendation information may include recommending a place commonly identified by a group of experts associated with the user's preferences.

According to yet another aspect, providing the recommended information may include providing information common to other users regarding the location based on the user's preferences.

A computer program is provided for causing a computer to execute the location-related recommendation method.

A computer system, comprising at least one processor configured to execute computer readable instructions contained in a memory, the at least one processor utilizing user feedback associated with a location to determine user preferences. and providing recommendations associated with places based on the user's preferences and corresponding characteristics.

According to an embodiment of the present invention, by analyzing the user's preference based on the user feedback associated with the location, recommendation information corresponding to the user's preference can be provided.

According to the embodiment of the present invention, it is possible to provide reviewer-centered recommendation information and location- or region-centered recommendation information based on user's preferences, and to classify the level of preference according to the type of feedback, thereby differentiating each other. It can be used for recommendation logic.

1 is a diagram showing an example of a network environment in one embodiment of the present invention; FIG. 1 is a block diagram illustrating an example of a computer system in one embodiment of the invention; FIG. FIG. 2 illustrates an example of components that a processor of a computer system may include in one embodiment of the present invention; 2 is a flowchart illustrating an example of a method that may be performed by a computer system in accordance with one embodiment of the present invention; FIG. 4 is an exemplary diagram illustrating a process of collecting preference feedback in an embodiment of the present invention; FIG. 4 is an exemplary diagram illustrating a process of collecting preference feedback in an embodiment of the present invention; FIG. 4 is an exemplary diagram illustrating a process of collecting preference feedback in an embodiment of the present invention; FIG. 4 is an exemplary diagram illustrating a process of collecting preference feedback in an embodiment of the present invention; FIG. 4 is an exemplary diagram illustrating a process of recommending a reviewer based on a user's preferences in one embodiment of the present invention; FIG. 4 is an exemplary diagram for explaining a process of recommending a place based on user's preferences in one 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.

Embodiments of the present invention relate to techniques for providing recommendation information associated with a place.

Embodiments including the matters specifically disclosed herein can provide recommendation information corresponding to the user's preferences by analyzing the user's preferences based on the user feedback associated with the location.

In this specification, the place means all objects that can be reviewed for user experience such as visiting and using, such as restaurants, sales floors, famous places, and popular places (hot places). you can Reviews for places may include reviews for online user experiences such as commerce, virtual performances/exhibitions, etc., as well as offline user experiences.

A place-related recommendation system according to an embodiment of the present invention may be implemented by at least one computer system, and a place-related recommendation method according to an embodiment of the present invention may be implemented by at least one computer system included in the place-related recommendation system. may be executed. At this time, a computer program according to an embodiment of the present invention may be installed and executed in the computer system, and the computer system may generate a location-related recommendation according to an embodiment of the present invention under control of the executed computer program. the method may be carried out. The computer program described above may be recorded in a computer-readable recording medium in order to combine with a computer system and cause the computer to execute the location-related recommendation method.

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 number of electronic devices and the number of servers are not limited as in FIG. Also, the network environment in FIG. 1 is merely an example of the environment applicable to this embodiment, and the environment applicable to this embodiment is not limited to the network environment in FIG. .

The plurality of electronic devices 110, 120, 130, 140 may be fixed terminals or mobile terminals implemented by computing devices. Examples of the plurality of electronic devices 110, 120, 130, and 140 include smartphones, mobile phones, navigation systems, PCs (personal computers), notebook PCs, 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 a smart phone as an example of the electronic device 110, but in embodiments of the present invention, the electronic device 110 substantially utilizes a wireless or wired communication scheme and communicates with other devices via the network 170. may refer to one of a variety of physical computer systems capable of communicating with the electronic devices 120, 130, 140 and/or the servers 150, 160.

The communication method is not limited, and not only the communication method using the communication network that can be included in the network 170 (eg, mobile communication network, wired Internet, wireless Internet, broadcasting network), but also the short distance between devices. Wireless communication may be included. 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). k), such as 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. will not be

Each of servers 150, 160 is implemented by one or more computing devices that communicate with a plurality of electronic devices 110, 120, 130, 140 over network 170 to provide instructions, code, files, content, services, etc. good. 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, location review service, etc.) 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 of distributing files for installing and executing the applications described above to the plurality of electronic devices 110 , 120 , 130 , 140 .

FIG. 2 is a block diagram illustrating an example computer system, in accordance with one embodiment of the present invention. Each of the plurality of electronic devices 110, 120, 130, 140 and each of the servers 150, 160 described above may be realized by the computer system 200 shown in FIG.

Such a computer system 200 may include memory 210, processor 220, communication interface 230, and input/output interface 240, as shown in FIG.

The memory 210 is a computer-readable storage medium and may include random access memory (RAM), read only memory (ROM), and permanent mass storage devices such as disk drives. Here, a permanent mass storage device such as a ROM or disk drive may be included in computer system 200 as a separate permanent storage device separate from memory 210 . Also stored in memory 210 may be an operating system and at least one program code. Such software components may be loaded into memory 210 from a computer-readable medium separate from memory 210 . Such other computer-readable recording media may include computer-readable recording media such as floppy drives, disks, tapes, DVD/CD-ROM drives, memory cards, and the like. In other embodiments, software components may be loaded into memory 210 through communication interface 230 that is not a computer-readable medium. For example, software components may be loaded into memory 210 of computer system 200 based on computer programs installed by files received over network 170 .

Processor 220 may be configured to process computer program instructions by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to processor 220 by memory 210 or communication interface 230 . For example, processor 220 may be configured to execute received instructions according to program code stored in a storage device, such as memory 210 .

Communication interface 230 may provide functionality for computer system 200 to communicate with other devices (eg, the recording devices described above) via network 170 . By way of example, processor 220 of computer system 200 may transmit requests, instructions, data, files, etc. generated according to program code stored in a storage device such as memory 210 to others via network 170 under the control of communication interface 230 . device. Conversely, signals, instructions, data, files, etc. from other devices may be received by computer system 200 through communication interface 230 of computer system 200 over network 170 . Signals, instructions, data, etc., received through communication interface 230 may be communicated to processor 220 and memory 210, and files, etc., may be stored on storage media (the permanent storage devices described above) that computer system 200 may further include. may be recorded.

Input/output interface 240 may be a means for interfacing with input/output device 250 . For example, input devices may include devices such as a microphone, keyboard, or mouse, and output devices may include devices such as displays, speakers, and the like. As another example, input/output interface 240 may be a means for interfacing with a device that integrates functionality for input and output, such as a touch screen. Input/output device 250 may be one device with computer system 200 .

Also, in other embodiments, computer system 200 may include fewer or more components than the components of FIG. However, most prior art components need not be explicitly shown in the figures. For example, computer system 200 may be implemented to include at least some of the input/output devices 250 described above, and may also include other components such as transceivers, databases, and the like.

Specific embodiments of methods and systems for providing location-related recommendations by preference are described below.

FIG. 3 is a block diagram illustrating exemplary components that a processor of a computer system may include in accordance with one embodiment of the present invention, and FIG. 4 illustrates the components executed by the computer system in accordance with one embodiment of the present invention. 2 is a flow chart showing an example of a possible place-related recommendation method;

The computer system 200 according to the present embodiment may provide location review services to clients through a dedicated application installed on the client or by connecting to a web/mobile site associated with the computer system 200 .

The computer system 200 provides a place review space through a user's personal service page. User-generated reviews may be matched and registered with authenticated location information.

Computer system 200 may provide a location-based search and recommendation environment for users. At this time, users may be allowed to share reviews of places by following each other.

As an example, the place review service includes a feed tab that displays reviews of users who are following for places in various industries such as restaurants and cafes, recommended places/reviewers that match the user's taste, etc. A Visits tab that displays a list of places that have been authenticated for visits or uses of the site, a Reviews tab that allows users to create reviews such as star reviews, keyword reviews, text reviews, photo reviews for places they have visited or used, visits or uses A Reservations/Orders tab that allows users to request reservations, orders, etc. for places for the purpose of , a Saved tab that displays a list of places that the user has saved as a subject of interest, and the like.

The processor 220 of the computer system 200 includes a feedback collection unit 310, a preference analysis unit 320, and a location-related recommendation unit 330 as shown in FIG. 3 as components for executing the location-related recommendation method described below. OK. Depending on the embodiment, components of processor 220 may be selectively included or excluded from processor 220 . Also, depending on the embodiment, the components of processor 220 may be separated or merged to represent the functionality of processor 220 .

Such processor 220 and components of processor 220 may control computer system 200 to perform the steps included in the location-related recommendation method described below. For example, processor 220 and components of processor 220 may be implemented to execute instructions according to the code of an operating system and the code of at least one program contained in memory 210 .

Here, the components of processor 220 may represent different functions performed by processor 220 according to instructions provided by program code recorded in computer system 200 . For example, feedback collector 310 may be utilized as a functional representation of processor 220 controlling computer system 200 according to the instructions described above so that computer system 200 collects user feedback associated with a place.

Processor 220 may read the necessary instructions from memory 210 loaded with instructions associated with controlling computer system 200 . In this case, the read instructions may include instructions for controlling processor 220 to perform the location-related recommendation method described below.

The steps included in the location-related recommendation method described below may be performed in a different order than shown in the figures, some of the steps may be omitted, or additional steps may be included. may

Referring to FIG. 4, at step S410, the feedback collector 310 may collect user feedback associated with the location. The feedback collector 310 may collect user feedback for each type of feedback. The feedback collection unit 310 may collect both the feedback type and the feedback target location information as the user feedback related to the location. The types of feedback may include interest feedback indicating user interest status, visit feedback indicating user visit/usage status, preference feedback indicating user preference status, preference feedback indicating user preference status, and the like. For example, the feedback collector 310 may collect interest feedback based on log data such as searches for places, clicks, saves, directions, calls, shares, and the like. The feedback collector 310 may collect visit feedback based on log data such as receipt verification, reservations, orders, payments, and the like. The feedback collection unit 310 may collect preference feedback based on sentiment analysis results of sentences written in text reviews among user reviews of places. In addition to text review, preference feedback may be collected using keywords and sentences reviewed by other review systems such as keyword review. The feedback collector 310 may collect preference feedback based on likes or recommendation reactions to places. In the case of taste feedback, it is possible to classify the level of taste and collect it. If it is used, it is classified as a light taste with relatively low importance, and if the like button is pressed in the process of creating a review after the user directly visits or uses it, it is relatively high in importance. You can classify according to taste.

In step S420, the preference analysis unit 320 may analyze the user's preference using user feedback related to the location. The preference analysis unit 320 may analyze the user's preference by extracting features of the location where user feedback exists. As an example, the preference analysis unit 320 may extract the feature information of the place from the basic information (type of business, description, photo, etc.) registered by the person concerned with the place. As another example, the preference analysis unit 320 may extract feature information automatically tagged to a location based on user-generated content that has visited or used the location. Location features may be classified and tagged from keywords and texts created as reviews by users, or classified and tagged by products in photos created as reviews by users, results of mood/sensory analysis, and the like. Feature information for user preference analysis may be extracted, centering on the location where the user has created a review of the same keyword or similar sentences.

The preference analysis unit 320 may analyze user preferences using at least one of interest feedback, visit feedback, preference feedback, and preference feedback. As an example, the preference analysis unit 320 may utilize interest feedback, visit feedback, preference feedback, preference feedback, and apply equal weights to analyze user preferences. As another example, the preference analysis unit 320 analyzes the user's preference using the preference feedback among the feedback types as the core feedback, and analyzes the remaining feedback types (interest feedback, visit feedback, preference feedback, clean feedback). ) may be reflected selectively or with different weight values. The preference analysis unit 320 may use the user feedback to calculate a preference index for each feature of the location. An index may be calculated. At least one feature having a high preference index equal to or higher than a certain level may be determined as the user's preference among the many features of the place.

When analyzing the user's preference using the preference feedback, the preference analysis unit 320 may calculate the user's preference index by distinguishing light preference and heavy preference. At this time, the light preference index and the heavy preference index may be used for different recommendation logics. For example, by analyzing photos registered by people involved in the place and photos registered by the user as reviews, the atmosphere and feeling of the place may be classified, and at this time, when the place with the atmosphere and feeling preferred by the user is to be recommended. A light preference index can be used for A heavy preference index may be used when a place having features that are the same as or similar to the feature corresponding to the user's preference is recommended.

In step S430, the location-related recommendation unit 330 may provide location-related recommendation information corresponding to the user's preference. As an example, the place-related recommendation unit 330 may select and recommend reviewers who have tastes similar to those of the user from among other users who use the place review service (hereinafter referred to as "reviewers"). At this time, the place-related recommendation unit 330 may select reviewers with similar tastes by using the regional distribution of places corresponding to the user's tastes or the business category. As another example, the place-related recommendation unit 330 may select and recommend a place having characteristics corresponding to the user's preference. The features of the place may be categorized by keywords or text written by the user as a review, or by products or atmosphere/sensory analysis results of photos written by the user as a review. Among these characteristics, locations having characteristics similar to the user's preferences may be selected, and the corresponding locations and reviews of the corresponding locations may be recommended. As another example, the place-related recommendation unit 330 selects and recommends a specific place that a reviewer identified as a service expert group or a reviewer followed by the user commonly visits or likes. good. A group of experts on a specific product (e.g., user preference menu) or a group of experts on a specific region (e.g., user preference area) commonly visited, rather than recommending by individual reviewer or location. Or you can recommend a particular place you like. As another example, the location-related recommender 330 may provide common information between the user and the reviewer. For example, when the user visits a reviewer's page (e.g., review home), the location-related recommendation unit 330 indicates locations that the reviewer and the user have reviewed in common, locations that have input like reactions, and the like. you can In addition, the place-related recommendation unit 330 may provide a filtering function using common points with the user when providing reviews from many reviewers. For example, various conditions such as age, town, taste, favorite place, place of like reaction, etc. may be provided as a filter, and reviews of reviewers whose conditions are similar to those of the user selected by the filter may be selected and provided. you can Therefore, the place-related recommendation unit 330 can provide recommendation information centered on a person as well as recommendation information centered on a place or a region based on user's preference.

In providing the review feed, the location-related recommendation unit 330 may provide a feed that shows reviews distributed in each location by collecting them for each of various criteria such as region and industry. The place-related recommendation unit 330 may provide a feed for each administrative area (for example, for each dong) based on the location set by the user. You may provide the news, etc. At this time, the place-related recommending unit 330 may select a place that is expected to be of interest to the user based on the user's preference among the places within the administrative district, and recommend a review of the corresponding place.

The location-related recommendation unit 330 provides a review list to the user in the process of location search and recommendation, and accumulates user feedback (e.g., feedback that the review was useful) on the review instead of the recency based on the review creation time. A review list may be provided ordered by number of times. The place-related recommendation unit 330 may display the review list in order of accumulated review usefulness, and may arrange the review list according to other criteria such as review credibility, review quality, and recency according to the user's request. can be shown. Reviews of locations that are predicted to be of interest to the user based on the user's preferences may be shown more than reviews of other criteria.

5-8 are exemplary diagrams illustrating the process of collecting preference feedback in one embodiment of the present invention.

Referring to FIG. 5, the location details screen 500 may display general information (eg, business name, location, phone number, photo, description, products, etc.) associated with the location. At this time, the location details screen 500 may include a “save” button 501 for saving the location as a bookmark.

Processor 220 may provide save location screen 510 when “save” button 501 is selected from location details screen 500 . The save location screen 510 may include an interface for selecting a location list for saving the location of interest.

The location save screen 510 may include a "Like" button 511 for entering a like reaction as preference feedback for the location to be saved. The processor 220 may collect user input via the "Like" button 511 on the Save Places screen 510 as preference feedback.

Referring to FIG. 6, processor 220 may provide review creation screen 610 for locations visited or used by the user. The review creation screen 610 includes an interface for creating star reviews, keyword reviews, text reviews, photo reviews, etc. as user reviews for places.

The review creation screen 610 may include a "Like" button 611 for entering a like reaction as preference feedback for the place. Processor 220 may collect user input via "Like" button 611 on review creation screen 610 as preference feedback.

Referring to FIG. 7, processor 220 may provide review feed screen 710 on the place review service page.

The review feed screen 710 displays a feed list of reviews created by reviewers the user follows, a feed list of reviews about recommended places that suit the user's taste, and the like, targeting places in various industries. you can

Review feed screen 710 may include a "Like" button 711 for each review included in the feed list for entering a like reaction with preference feedback for the location of that review. The processor 220 may collect user input via the "Like" button 711 on the review feed screen 710 as preference feedback.

Referring to FIG. 8, processor 220 may provide review details screen 810 when a particular review is selected, such as in reviews feed screen 710 . The review details screen 810 displays information about the reviewer who created the relevant review, location information of the object of the relevant review, star reviews created as user reviews, keyword reviews, text reviews, photo reviews, and other review contents. may be

The review details screen 810 may include a "Follow" button 801 for setting follow-up to the reviewer, a "Like" button 811 for inputting a nice reaction as preference feedback for the place, and the like. The processor 220 may collect user input via the "Like" button 811 on the review details screen 810 as preference feedback.

The routes described above are only some examples and should not be limiting, and user feedback on a location may be collected through a variety of other routes.

FIG. 9 is an exemplary diagram for explaining the process of recommending reviewers based on the user's preferences in one embodiment of the present invention.

Referring to FIG. 9, the processor 220 may provide a user-preferred reviewer recommendation list 910 on a review feed screen 710 . The reviewer recommendation list 910 may be composed of reviewers who have the same or similar preferences for places as the user.

For each reviewer included in the reviewer recommendation list 910, a "follow" button 901 for setting follow-up, a "recommendation exclusion" button 902 for excluding the reviewer from reviewer recommendation targets, and the like may be included.

The reviewer recommendation list 910 may include at least some photo reviews, place names, etc. as the review list 903 created by the recommended reviewers. The recommended reviewers may be classified and recommended according to various criteria, such as reviewers by region, reviewers by menu, and reviewers with similar tastes. As an example, the review list 903 may display, among the reviews created by the recommended reviewers, reviews of places having features identical or similar to features corresponding to the user's preferences. It is also possible to display reviews with a high number of reviews and reviews that have been created recently.

FIG. 10 is an exemplary diagram for explaining the process of recommending a place based on user's preferences in one embodiment of the present invention.

Referring to FIG. 10, the processor 220 may provide a user-preferred place recommendation list 1010 on a review feed screen 710 . The place recommendation list 1010 may be composed of places having features that are the same as or similar to features that correspond to the user's preferences.

For each place included in the place recommendation list 1010, place information 1002, photo review 1003 for the place, reviewer information 1004 for each photo review 1003, a "Follow" button 1001 for follow setting for the reviewer, and the like are included. good.

In other words, the place recommendation list 1010 can display reviews of places having characteristics that are the same or similar to those that correspond to the user's preferences.

Thus, according to the embodiment of the present invention, it is possible to provide recommendation information corresponding to the user's preference by analyzing the user's preference based on the user's feedback related to the location. Further, according to the embodiment of the present invention, it is possible to provide reviewer-centered recommendation information and location-centered recommendation information based on user's preferences, and classify the level of preference according to the type of feedback. It can be used for recommendation logic.

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. The processing unit may run an operating system (OS) and one or more software applications that run on the OS. The processor may also access, record, 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 may recognize that the processing device may include multiple processing elements and/or multiple types of processing elements. You can understand that. 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 stored and executed in a distributed fashion over computer systems linked by a network. Software and data may be recorded on one or more computer-readable recording 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. Here, the medium may record the computer-executable program continuously or temporarily record it for execution or download. In addition, the medium may be various recording means or storage means in the form of a combination of single or multiple hardware, and is not limited to a medium that is directly connected to a computer system, but is distributed over a network. It may exist in 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., and may be configured to store program instructions. Other examples of media include recording media or storage media managed by application stores that distribute applications, sites that supply or distribute various software, and servers.

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 than in 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.

220: Processor 310: Feedback Gatherer 320: Preference Analyzer 340: Place-Related Recommender

Claims (20)

A place-related 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 location-related recommendation method includes:
analyzing, by the at least one processor, user preferences using user feedback associated with locations; and, by the at least one processor, recommendation information associated with locations based on features corresponding to the user preferences. A place-related recommendation method, including providing The step of analyzing user preferences includes:
analyzing the user's preferences by classifying preference levels according to feedback types;
The step of providing the recommendation information includes:
The place-related recommendation method of claim 1, further comprising: providing different recommendation targets according to the user's taste classified by the weight level. The step of analyzing user preferences includes:
3. The method of claim 1 or 2, comprising collecting the user feedback based on like reactions to a place. The step of analyzing user preferences includes:
collecting the user feedback based on at least one of log data associated with location searches, log data associated with receipt authentication and reservations or orders for locations, and user review analysis results for locations. 3. The place-related recommendation method according to 3. The step of analyzing user preferences includes:
4. The method according to claim 3, further comprising the step of analyzing the user's preferences by classifying the user's favorable reaction to a place included in reviews of other users and the favorable reaction to a place that the user directly visited or used. Place-related nomination method. The step of analyzing user preferences includes:
extracting features from content associated with the location where the user feedback exists; and calculating a preference index for each of the features using the user feedback. 6. The place-related recommendation method according to any one of 5. The step of providing the recommendation information includes:
The place-related recommendation method according to any one of claims 1 to 6, comprising selecting and recommending other users who have tastes similar to the user's tastes for places. The step of providing the recommendation information includes:
Selecting and recommending other users who have similar tastes based on at least one of a regional distribution according to the user's tastes and an industry category. Described place-related nomination method. The step of providing the recommendation information includes:
The place-related recommendation method according to any one of claims 1 to 6, comprising selecting and recommending a place having features corresponding to the user's taste. The step of providing the recommendation information includes:
A location-related recommendation method according to any one of claims 1 to 6, comprising: recommending a location commonly identified by a group of experts associated with the user's preferences. The step of providing the recommendation information includes:
The place-related recommendation method according to any one of claims 1 to 6, comprising the step of: providing common information about places with other users based on the user's preferences. A computer program for causing a computer to execute the location-related recommendation method according to any one of claims 1 to 11. a computer system,
at least one processor configured to execute computer readable instructions contained in memory;
The at least one processor
A computer system for processing: analyzing user preferences utilizing user feedback associated with a place; and providing recommendation information associated with a place based on characteristics corresponding to said user preferences. The at least one processor
Analyzing the user's preference by classifying the preference level according to feedback type; and Providing different recommended objects according to the user's preference classified by the severity level. 14. The computer system according to 13. The at least one processor
a process of collecting said user feedback based on likes and reactions to places; and at least one of log data related to place searches, log data related to receipt authentication and reservations or orders to places, and user review analysis results for places. 15. A computer system according to claim 13 or 14, processing the step of collecting said user feedback based on: The at least one processor
16. The method of claim 15, further comprising a step of analyzing the user's preference by distinguishing the like reaction to the place included in other user's review from the like reaction to the place the user directly visited or used. computer system. The at least one processor
17. The computer system according to any one of claims 13 to 16, processing the process of selecting and recommending other users who have similar tastes to the user's tastes for places. The at least one processor
17. The process of selecting and recommending other users having similar tastes based on at least one of a regional distribution according to the user's tastes and an industry category. the computer system described in . The at least one processor
17. A computer system as claimed in any one of claims 13 to 16, processing the process of selecting and recommending locations having characteristics corresponding to the user's tastes. The at least one processor
A computer system as claimed in any one of claims 13 to 16, processing the process of recommending places commonly identified by a group of experts associated with the user's tastes.

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