KR20200011725A - Apparatus of recommending restaurants based on types and drinking volumes of alcohols and method thereof - Google Patents

Abstract

The present invention relates to a device and a method for recommending a restaurant according to the type and amount of alcohol, which receives a liquor desired by a user and a drink amount for the liquor of the user and recommends a restaurant suitable for the type and input amount of the liquor that the user wants. The present invention provides device and a method for recommending a restaurant according to the type and amount of alcohol, which comprises: a display unit including a region selection part which receives a region, a liquor type selection part which receives a type of liquor, a drink amount selection unit which receives a drink amount for the received type of liquor, and a preference filter selection part which displays a preference filter associated with the type of liquor or the drink amount and receives the preference filter; and a control unit controlling the display unit and transmitting the input region and preference filter to an information providing device.

Description

Apparatus and method for recommending restaurants according to type and quantity {APPARATUS OF RECOMMENDING RESTAURANTS BASED ON TYPES AND DRINKING VOLUMES OF ALCOHOLS AND METHOD THEREOF}

The present invention relates to an apparatus and method for recommending a restaurant according to alcohol and alcohol, and more particularly, an apparatus for recommending a restaurant suitable for a type and amount of alcohol received by receiving a user's liquor for a desired alcohol and a corresponding alcohol. And to a method.

According to a conventional search method, a user may search for and find a desired web document by inputting a search keyword in a search window. For example, a user may enter the movie title "Interstellar" into the search box to retrieve information about the movie "Interstellar". However, if the user does not remember the title of the movie he is looking for, it is required to provide information in a different manner. For example, the user may attempt to search by entering an actor who appeared in a movie he is looking for, a director, a producer, or the like of the movie as a keyword. Movie information sites or movie review sites are often provided with the cast information along with the movie information, so if not lucky, the user may be able to find a desired movie by keyword, such as actor, director, producer.

However, if the information is provided based on an atypical language, for example, an emotional language, rather than the standardized information, the conventional search method cannot be used. For example, a traditional search engine's response to a search query such as "funny movie" or "movie when watching sad" will search for documents that someone has written containing the keyword "fun movie" or "movie when sad." It will only be. However, atypical language requires a different approach than formalized information such as actors, the (general) movie genre, and the year of release. Even if someone didn't include the keyword "funny movies" or "movies when sad," there may be movies that many people may find "fun" or "good when sad." Furthermore, other areas besides film may require different approaches to requests for information provision using unstructured languages.

When the area is gathered at a dinner or a meeting with colleagues or friends, the restaurant or bar is searched by searching the restaurants in the area on a portal site or an internet cafe, and referring to comments on the restaurant or blog reviews. .

In this case, not only does it take a long time to read the review, but also depends on the nature of the meeting or the progress of the meeting, such as the first and second meetings. In some cases.

The present invention is designed to solve the above problems, and to provide an apparatus and method for recommending a restaurant that is suitable for the type and amount of alcohol received by the user to receive the desired amount of alcohol and the user's liquor for the sake. .

According to an embodiment of the present disclosure, a restaurant recommendation terminal device based on a main alcohol and a main quantity includes a region selection unit for receiving an area, a main species selection unit for receiving a main species, a main quantity selection unit for receiving a main quantity for the main species, and the main species Or a display unit including a taste filter selection unit which displays a taste filter associated with a main quantity and receives the taste filter; And a control unit which controls the display unit and transmits the input area and taste filter to an information providing device.

The display unit may further include a recommendation result display unit configured to receive and display, from the information providing server, a restaurant having a high correlation with the selected taste filter and the restaurant- taste filter in the region.

The display unit may further include a taste filter display unit for displaying the selected taste filter, and a recommendation result display unit for receiving and displaying a restaurant having high association with the selected taste filter in the region from the information providing server.

If some of the taste filters are deleted, a recommended restaurant is displayed according to the remaining taste filters.

A taste filter associated with the master species is selected based on the distance between the master species and the taste filter, the frequency at which the master species and the taste filter are simultaneously described, or word2vec.

Based on the article information, a taste filter and a menu associated with the liquor are selected.

Restaurant recommendation server according to the master and master according to an embodiment of the present specification,

Receiving a region, a master and a liquor selected by the user; Selecting a taste filter by calculating a master / flavor filter association degree or a liquor-flavor filter association degree with respect to the master or master quantity; And transmitting a predetermined number of taste filters among the selected taste filters to the terminal.

And receiving a taste filter selected from the transmitted taste filters.

The dominant-flavor filter association degree is selected based on the distance between the dominant species and the taste filter, the frequency at which the dominant species and the taste filter are simultaneously described, or word2vec.

The dosing-flavor filter association degree and the menu associated with the dosing amount is based on article information.

Calculating a restaurant- taste filter association.

And searching for and transmitting the restaurant associated with the region, the taste filter, and the menu to the terminal.

The restaurants are arranged in the order of high restaurant- taste filter association.

The restaurant- taste filter correlation is calculated based on the distance between the restaurant and the taste filter or the frequency of the taste filter.

The restaurant-flavor filter association degree is extracted using sub-keywords for the taste filter and the association between the sub-keywords and the restaurant.

According to an embodiment of the present disclosure, an apparatus and method for recommending a restaurant based on alcohol and alcohol may search for and display a restaurant that matches the type of alcohol selected by the user and the amount of alcohol of the user.

1 is a network diagram of an information providing system using an attribute language according to an embodiment of the present specification.
2 is a block diagram of a terminal 200 according to an embodiment of the present disclosure.
3 is a block diagram of an information providing apparatus 300 according to an exemplary embodiment.
4 is a flowchart illustrating an information providing process through an information providing interface according to an exemplary embodiment of the present specification.
5 is a detailed flowchart of step 910 according to an embodiment of the present disclosure.
6 is a detailed flowchart of step 510 according to an embodiment of the present disclosure.
7 is a detailed flowchart of step 530 according to an embodiment of the present disclosure.
8 is a flowchart illustrating an information providing process according to another exemplary embodiment of the present specification.
9 is a flowchart illustrating an information providing process according to an exemplary embodiment of the present specification.
10 is an illustration of a stored object-keyword association diagram according to one embodiment of the present disclosure.
11 is an illustration of a basic reserved word-keyword association according to one embodiment of the present specification.
12 is a detailed flowchart of step 940 according to one embodiment of the present specification.
13 is a flowchart illustrating an information providing process according to another exemplary embodiment of the present specification.
14 is a flowchart illustrating an information providing process according to another embodiment of the present specification.
15 is a detailed flowchart of step 1340 according to the first embodiment of the present specification.
16 is a detailed flowchart of step 1340 according to another embodiment of the present specification.
17 is a detailed flowchart of step 1340 according to another embodiment of the present disclosure.
18 is a detailed flowchart of step 1320 according to a modification of the present specification.
19 is an example of an interface 1900 generated according to the interface information provided in step 1840.
20 is a diagram illustrating a term hierarchy according to an embodiment of the present specification.
21 is a flowchart illustrating a virtual input interface program installation process according to an embodiment of the present invention.
22A to 22B are diagrams illustrating an interface page of a package for performing a restaurant recommendation method according to alcohol and alcohol according to one embodiment of the present specification.
FIG. 23 is a diagram illustrating an interface page of a package for performing a restaurant recommendation method according to alcohol and alcohol according to another embodiment of the present specification.

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

In describing the embodiments, descriptions of technical contents that are well known in the art to which the present disclosure belongs and are not directly related to the present specification will be omitted. This is to more clearly communicate without obscure the subject matter of the present specification by omitting unnecessary description.

For the same reason, in the accompanying drawings, some components are exaggerated, omitted or schematically illustrated. In addition, the size of each component does not reflect the actual size entirely. The same or corresponding elements in each drawing are given the same reference numerals.

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

According to some embodiments of the present disclosure, the reserved word may mean a string that can be expressed or defined through the degree of relevance information with other keywords, or a property, usage, or property thereof can be expressed and defined. However, it is also possible to apply a reserved word defined / expressed according to another method, for example, by an administrator's manual input. The detailed configuration of the association information will be described later.

1 is a network diagram of an information providing system using an attribute language according to an embodiment of the present specification.

Referring to FIG. 1, an information providing system according to an exemplary embodiment of the present specification may include a terminal 200, an information providing apparatus 300, and a communication network 150.

The terminal 200 may be implemented as, for example, an electronic device or a similar device that performs a smartphone, a PDA, a tablet PC, a notebook computer, a laptop computer, a personal computer, or other communication, receives a user input, and outputs a screen. Can be.

The information providing apparatus 300 may be implemented as, for example, a workstation, a server, a general purpose computer, an electronic device capable of performing other communication, or a similar device.

The terminal 200 and the information providing apparatus 300 are connected through the communication network 150 and communicate with each other through the communication network 150.

The communication network 150 may be, for example, Long Term Evolution (LTE), LTE-Advanced (LTE-A), WI-FI, Local Area Network (LAN), Wide Area Network (WAN), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Wireless Broadband (WiBro), Global System for Mobile Communications (GSM), and the like may be implemented using at least some of the past, present and future communication schemes. Hereinafter, for convenience, the terminal 200 and the information providing apparatus 300 will be described as if they directly communicate without referring to the communication network 150.

Detailed operations and configurations of the terminal 200 and the information providing apparatus 300 will be described later with reference to FIGS. 2 to 8.

2 is a block diagram of a terminal 200 according to an embodiment of the present disclosure.

Referring to FIG. 2, the terminal 200 according to an exemplary embodiment of the present specification may include an input unit 210, a display unit 220, a communication unit 230, a storage unit 240, and a controller 250.

The input unit 210 converts a user's input operation into an input signal and transmits the input signal to the controller 250. The input unit 210 may be implemented as, for example, a keyboard, a mouse, a touch sensor on a touch screen, a touch pad, a keypad, a voice input, and other input processing devices that may be available in the present, the past, or the future. The input unit 210 may receive, for example, a user's information providing request input and transmit the received information to the controller 250.

The display unit 220 outputs a screen under the control of the controller 250. The display unit 220 may be embodied as, for example, LCDs (liquid crystal displays), LEDs (light emitting diodes), OLEDs (organic light emitting diodes), projectors, and other display devices that may be available now, in the past, or in the future. The display unit 220 may display, for example, an interface page for providing information or an information providing result page. According to an exemplary embodiment, the display unit 220 may be used instead of the display unit 220.

The communication unit 230 exchanges data with the information providing device 300 and / or other external device. The communicator 230 transmits the data received from the information providing apparatus 300 to the controller 250. In addition, the communication unit 230 transmits data to the information providing apparatus 300 under the control of the control unit 250. The communication technology used by the communication unit 230 may vary depending on the type or other circumstances of the communication network 150.

The storage unit 240 stores data under the control of the controller 250 and transmits the requested data to the controller 250.

The controller 250 controls the overall operation and each component of the terminal 200. As described later, the controller 250 transmits an information providing request and other data to the information providing apparatus 300 according to the information input from the input unit 210, and according to the page information received from the information providing apparatus 300. The result page and / or the interface page are displayed on the display unit 220.

Operations performed by the controller 250 may be distributed by various computing devices that are physically separated. Some of the operations performed by the controller 250 may be performed by the first server, and other operations may be performed by the second server. In this case, the controller 250 may be implemented as a total of physically separated computing devices.

The storage unit 240 may be implemented as a total of storage devices that are physically separated.

When the control unit 250 or the storage unit 240 is implemented as a total of several physically separated devices, communication between the various devices may be required. For simplicity, the description will be given on the assumption that the storage unit 240 or the controller 250 is implemented as a single object.

When the terminal 200 transmits / receives data, the communication unit 230 may transmit or receive data according to the control of the control unit 250 according to a viewpoint, or the control unit 250 controls the communication unit 230 to transmit / receive data. You can also express it.

A detailed operation of each component of the terminal 200 will be described later with reference to FIGS. 4 to 8.

3 is a block diagram of an information providing apparatus 300 according to an exemplary embodiment.

Referring to FIG. 3, the information providing apparatus 300 according to an exemplary embodiment of the present specification may include a communication unit 310, a control unit 320, and a storage unit 330.

The communication unit 310 exchanges data with the terminal 200 and / or other external device. The communicator 310 transmits data received from the terminal 200 to the controller 320. In addition, the communication unit 310 transmits data to the terminal 200 under the control of the control unit 320. The communication technology used by the communication unit 310 may vary depending on the type or other circumstances of the communication network 150.

The storage unit 330 stores data under the control of the controller 320 and transmits the data requested from the controller 320 to the controller 320.

The control unit 320 controls the overall operation and each component of the information providing apparatus 300. When the controller 320 receives an interface page request, an information providing result page request, and other data through the communication unit 310 as described below, the controller 320 loads necessary data from the storage unit 330 and generates page information. The page information is transmitted to the terminal 200 through the communication unit 310.

When the information providing device 300 transmits / receives data, the communication unit 310 may express that the data is transmitted / received according to the control of the control unit 320 according to a viewpoint, or the control unit 320 controls the communication unit 310 to transmit the data. It can also be expressed as sending and receiving.

On the other hand, the storage unit 330 is installed in the terminal to store the program code for performing the restaurant recommendation method according to the main species and quantity, and / or an installation package (installation program code) for installing the program. Hereinafter, the program code for performing the restaurant recommendation method according to the master and master quantity, and / or the installation package (installation program code) for installing the program is called a virtual input interface program.

A developer or administrator who develops the information providing apparatus 300 may store a virtual input interface program in the storage 330. When the information providing device 300 receives the request message for requesting the virtual input interface program through the communication unit 310, the controller 320 transmits the virtual input interface program to the terminal 200 through the communication unit 310.

A detailed operation of each component of the information providing apparatus 300 will be described later with reference to FIGS. 4 to 8.

According to another exemplary embodiment, data for providing information in a voice or other manner may be transmitted and received instead of a page for visually providing information.

4 is a flowchart illustrating an information providing process through an information providing interface according to an exemplary embodiment of the present specification.

In operation 410, the controller 320 of the information providing apparatus 300 generates interface page information. The interface page information is information necessary for generating an interface page. The interface page is a page for inducing a user's input, receiving the user's input, and delivering the input to the information providing apparatus 300. For example, the interface page information may be in the form of an HTML document or other markup language document. In another embodiment, the terminal 200 may have the form information of the interface page in advance, and only the content corresponding to the content may be transmitted from the information providing apparatus 300 to the terminal 200. In the following description, it is assumed that interface page information or other page information is transmitted in an HTML document format for convenience. However, the scope of the present specification is not limited thereto.

In operation 420, the communication unit 310 of the information providing apparatus 300 transmits interface page information to the terminal 200.

In step 430, the controller 250 of the terminal 200 configures the interface page using the interface page information. For example, the controller 250 may configure an interface page in the form of a web page by interpreting an HTML document by driving a web browser. A separate application may be used instead of a web browser.

In operation 440, the display unit 220 of the terminal 200 displays an interface page to the user 400. The interface page may include, for example, an interface through which the user 400 may request information provision, input and / or select keywords for information provision, and set up other information provision.

In operation 450, the input unit 210 of the terminal 200 receives a selection input of the user 400 input through the interface page and transmits the selection input to the controller 250.

In operation 460, the communication unit 230 of the terminal 200 transmits input information for identifying a user's selection input to the information providing apparatus 300 under the control of the controller 250.

In operation 470, the controller 320 of the information providing apparatus 300 generates result page information by using a user's input (eg, a keyword and / or other information provision setting). A preparation process for generating result page information and a process for generating result page information will be described later with reference to FIGS. 5 to 8. The result page information may be configured, for example, in HTML document format and / or image format.

In operation 480, the communication unit 310 of the information providing apparatus 300 transmits the result page information to the terminal 200.

In operation 490, the control unit 250 of the terminal 200 configures a result page using the result page information received by the communication unit 230. For example, the controller 250 may configure the result page by interpreting the result page information in the HTML format.

In operation 495, the display unit 220 of the terminal 200 provides a result page to the user 400.

In the embodiment of FIG. 4, it is assumed that a page in a visual form is provided to the user. However, it is also possible to provide an interface or result information through voice. In this case, the voice output unit may be used instead of the display unit 220. Instead of a visual / acoustic approach, other currently available or future ways of interface may be used in the context of the user 400. In this case, the information providing apparatus 300 may provide the terminal 200 with information converted in another manner instead of page information so as to conform to the interface method.

In an embodiment after FIG. 5, the user 400 wants to be provided with information about an object of a particular interest field of interest. However, the scope of the present specification is not limited thereto.

The field of interest can be, for example, the type of object. For example, if the area of interest is 'celebrity', the objects corresponding to the area of interest may include 'Yu Simin', 'Yoo Jae-seok', 'Taylor Swift'. For example, if your area of interest is "movies," your objects of interest may include "Dunkerk," "Spider-Man: Homecoming," and "Superbad 3." For example, if the area of interest is a 'broadcast program', objects corresponding to that area of interest may include 'infinite challenge', 'american idol', and 'game of thrones'.

In the following embodiments, a document is collected to evaluate the relationship (association / weighting, etc.) between keywords. The collected documents may all be valued as having an equal value, and newer documents may be rated as having higher value. That is, the correlation between the age of the evaluation point reference document and the keywords appearing in the document may have a negative correlation.

In the process subsequent to FIG. 5 to be described later, the value may be evaluated differently according to the freshness of the document. For example, two keywords may appear together in a document one day after the evaluation point, and 10 times higher correlation may be evaluated than two keywords appear together in the document ten days after the evaluation point. The age of the document may be evaluated in units of seconds / minutes / hours or in units of days / months / years, for example. The control unit 320 extracts the partial association degree reflecting the document age by dividing the partial association value by the age of the document based on the corresponding document evaluated before the age of the document is reflected, and accumulating the partial association degree between the two keywords. The association of can be extracted.

When a document is generated to check the age of the document, for example, the publication time and / or metadata included in the document may be determined, or periodically crawled to discover a new document not found in a previous crawl. In this case, new documents may be added at the time of the new crawl.

9 is a flowchart illustrating an information providing process according to an exemplary embodiment of the present specification.

In operation 910, the controller 320 stores the object-keyword association degree corresponding to the object item and the representative attribute keyword pair in the storage unit 330.

10 is an illustration of a stored object-keyword association diagram according to one embodiment of the present disclosure.

In the embodiment of FIG. 10, there are m object items (i ₁ to i _m ), and there are n object attributes keywords (k ₁ to k _n ).

For example, the object-keyword association of the object item i ₅ and the representative attribute keyword k ₃ is w _5,3 .

The process of step 910 may be performed, for example, according to some of the embodiments of FIGS. 5 to 8, or a similar process, or a corresponding process. According to another embodiment of the present disclosure, the process of step 910 may be performed by an input of an administrator or by receiving an object-keyword association degree determined by an external system through a network or a storage medium.

5 is a detailed flowchart of step 910 according to an embodiment of the present disclosure.

Referring to FIG. 5, in step 510, the controller 320 extracts a representative attribute keyword candidate set from the first set documents. The controller 320 may collect, for example, keywords that frequently appear in a document corresponding to the field of interest in the first set of documents as a representative attribute keyword candidate set.

6 is a detailed flowchart of step 510 according to an embodiment of the present disclosure.

The control unit 320 selects keywords appearing in a document, such as an object keyword representing an object item belonging to a specific field, and keywords appearing in a document, such as a field keyword indicating a specific field, of the first attribute keyword candidate set and the second attribute keyword candidate. Can be set as a set.

For example, if the field of interest is a celebrity, the field keyword may include 'celebrity', 'celebrity', 'movie actor', 'star', 'celeb', 'celeb' and the like. The field keyword may be set by the administrator or recommended and set by the controller 320. The controller 320 may recommend / set a similar keyword, which is obtained by obtaining some field keywords, and analyzes a similar keyword whose correlation with the field keywords is greater than or equal to a preset value.

If the field of interest is a celebrity for providing an information service, the object keyword may be an individual person belonging to the field of interest. For example, "Yoo Jae-seok", "Taylor Swift", "Stephan Curry" may be an object keyword corresponding to a celebrity of interest.

To describe the relationship between the field keyword and the object keyword, for example, the field keyword may correspond to an attribute or type of the object keyword. If the field keyword represents a set, the object keyword may be an element representing the set.

The object keyword may be set by an administrator or selected in a manner similar to the field keyword. According to another embodiment, the controller 320 may analyze the context of the collected documents and select keywords determined as elements belonging to the set represented by the field keyword as object keywords.

The popular object keyword and the less popular object keyword may be classified according to the search / collection amount of the corresponding object keyword. The controller 320 may search / collect documents containing each object keyword, set an object keyword having a collection amount more than a specific threshold value as a popular object keyword, and set the remaining object keywords as non-popular object keywords.

Popular field keywords and non-popular field keywords may be classified according to search / collection amounts of corresponding field keywords. The controller 320 may search / collect documents including the respective field keywords, set a field keyword having a collection amount more than a specific threshold value as a popular field keyword, and set the remaining field keywords as non-popular field keywords. However, a threshold for distinguishing a popular object keyword and a non-popular object keyword and a threshold for distinguishing a popular field keyword from an unpopular field keyword may be different values. Hereinafter, for convenience, the popular object keyword and the popular field keyword are collectively referred to as popular field object keywords. Also, for convenience, both non-popular object keywords and non-popular field keywords are referred to as non-popular field object keywords.

In a modified embodiment, only the popular field keyword or the popular object keyword may be used instead of the popular field object keyword. In a modified embodiment, only the non-popular field keyword or the non-popular object keyword may be used instead of the non-popular field object keyword.

In operation 610, the controller 320 sets keywords appearing together in a document such as a popular field object keyword as a first attribute keyword candidate set.

The controller 320 may search / collect documents including the popular field object keyword, and set keywords included in the collected document as the first attribute keyword candidate set. According to another embodiment, the controller 320 may exclude the field keyword and the object keyword among the keywords included in the collected document from the first attribute keyword candidate set. In addition, the controller 320 may exclude a meaningless keyword, for example, a survey / article, that is set in advance from the first attribute keyword candidate set. According to another embodiment, the controller 320 may include, in the first attribute keyword candidate set, a keyword registered in a preset dictionary among keywords included in the collected document.

According to another embodiment, the controller 320 searches / collects a document including a popular field object keyword, and searches keywords collected within a predetermined distance from the popular field object keyword or a sentence including the keyword in the collected document. It can also be included in the first attribute keyword candidate set. According to another exemplary embodiment, the controller 320 may search / collect a document including a popular field object keyword, analyze a context, and select keywords used for the purpose of modifying / explaining a popular field object keyword as the first attribute keyword candidate. It can also be included in a set.

The distance between keywords or the distance between a keyword and a sentence may be, for example, the number of sentences placed between two keywords or keywords and sentences, the number of words placed between two keywords or keywords and sentences, and the distance between two keywords or keywords and sentences. It may be determined based on any one or more of the number of words located, two keywords, or the number of characters between the keyword and the sentence.

The controller 320 may first perform morphological analysis for keyword analysis.

In operation 620, the controller 320 sets keywords appearing together in a document such as an unpopular field object keyword as a second attribute keyword candidate set.

The controller 320 may search / collect a document including a non-popular field object keyword, and set keywords included in the collected document as a second attribute keyword candidate set. According to another embodiment, the controller 320 may exclude the field keyword and the object keyword among the keywords included in the collected document from the second attribute keyword candidate set. In addition, the controller 320 may exclude a predetermined meaningless keyword, for example, a survey / article, from the second attribute keyword candidate set. According to another exemplary embodiment, the controller 320 may include, in the second attribute keyword candidate set, a keyword registered in a preset dictionary among keywords included in the collected document.

According to another embodiment, the controller 320 searches / collects a document including a non-popular field object keyword, and searches keywords collected within a predetermined distance from the non-popular field object keyword or a sentence including the keyword in the collected document. It may also be included in the second attribute keyword candidate set. According to another exemplary embodiment, the controller 320 may search / collect a document including a non-popular field object keyword, and analyze the context to identify keywords used to modify / explain the non-popular field object keyword as the second attribute keyword candidate. It can also be included in a set.

The distance between keywords or the distance between a keyword and a sentence may be, for example, the number of sentences placed between two keywords or keywords and sentences, the number of words placed between two keywords or keywords and sentences, and the distance between two keywords or keywords and sentences. It may be determined based on any one or more of the number of words located, two keywords, or the number of characters between the keyword and the sentence.

The controller 320 may first perform morphological analysis for keyword analysis.

In operation 630, the controller 320 may set keywords belonging to both the first attribute keyword candidate set and the second attribute keyword candidate set as the representative attribute keyword candidate set. That is, keywords used as expressions for modifying popular field object keywords and used as expressions for modifying non-popular field object keywords may be collected in the representative attribute keyword candidate set.

According to another embodiment, in operation 510, the controller 320 may include keywords appearing with the object keyword and / or the field keyword in the representative attribute keyword candidate set regardless of popularity / unpopularity.

5, in operation 520, the controller 320 extracts two or more sub keywords associated with each representative attribute keyword included in the representative attribute keyword candidate set from the second set of documents.

The second set document used for extracting the lower keyword in step 520 and the first set document used for extracting the representative attribute keyword candidate set in step 510 may be different document sets or the same document set. For example, the first aggregated document may be a collection including all collectable documents, and the second aggregated document may be a collection including only documents in which a specific field of interest for which the information providing service is to be provided as a key keyword. . The controller 320 may analyze the collectable documents and analyze whether each document is a document used as a key keyword of a particular field of interest to which the information providing service is provided, based on frequently appearing keywords. According to another embodiment, both the first and second aggregated documents may be a collective including all of the related documents that can be collected. According to another embodiment, the first aggregated document may be a collection including all collectable related documents, and the second aggregated document may be a collection including only documents related to a specific area of interest for which an information providing service is to be provided. According to another exemplary embodiment, the second aggregated document may be a collection including all collectable related documents, and the first aggregated document may be a collection including only documents related to a specific interest to provide an information providing service.

For step 520, the control unit 320 may include, for example, documents including a field keyword indicating the field of interest itself so as to generate a set including only documents related to a specific field of interest for which the information providing service is to be provided; Alternatively, documents including object keywords belonging to a corresponding interest may be collected, and documents having a specific gravity or more of the field keywords / object keywords among them may be extracted to generate a set including only documents related to a specific interest. The weight of the field keyword / object keyword may be determined based on the frequency of occurrence of the field keyword / object keyword, the appearance position, and the context. For example, a document in which a field keyword / object keyword frequently appears, a field keyword / object keyword is used as a title of the document, or displayed in large letters or a typeface for emphasis may be classified as a document related to a particular interest.

In operation 520, the controller 320 may analyze, for example, at least a portion of the second set of documents to extract a predetermined number of sub keywords most relevant to each representative attribute keyword, and to extract two or more subordinate items associated with each representative attribute keyword. You can extract keywords.

For example, the controller 320 may determine the degree of association between the representative attribute keyword and the lower keyword in consideration of the frequency of the lower keyword appearing in the same or similar context as the representative attribute keyword. For example, words appearing around keyword A in a particular sentence may appear around words associated with keyword A in other documents.

"I went on a trip with a big heart, but I suffered because the weather was so hot in July."

"I went on a trip to eat a big heart, but the weather was too humid for July."

Looking at the above two sentences, the word "hot" is replaced with "wet" in the same context. The controller 320 may infer that "hot" and "wet" are words associated with each other.

"I went on a trip with a big heart, but I suffered because the weather was so hot in July."

"I went on vacation with a big heart, but I suffered because the weather was so hot in July."

Similarly, the control unit 320 may infer that the words "travel" and "vacation" are related words through the above two sentences.

"I went on a trip with a big heart, but I suffered because the weather was so hot in July."

"I went on a trip with a big heart, but I suffered because the weather was so hot in August."

Similarly, the controller 320 may infer that the words "July" and "August" are related through the above two sentences.

The controller 320 indicates that words "hot" and "wet" are related words, "July" and "August" are related words, and "travel" and "vacation" are words related to each other through previously collected documents. You can save it.

"I went on vacation with a big heart, but the weather was too humid for July."

"I went on a trip with a big heart but it was hard because the weather was so hot in August."

If the two sentences are not in the same context but know that "hot" and "wet" are related words, "July" and "August" are related words, and "travel" and "vacation" are words related to each other, the control unit 320 ) Can be learned from the above sentence that "toil" and "tough" are also related words.

Keyword pairs with high frequency appearing in the same / similar context may be determined to be highly related to each other. In addition, it may be determined that the higher the similarity of the context in which the two keywords appear, the higher the correlation between the two keywords. The controller 320 proceeds with learning using the collected documents to set the degree of association between keywords, and sets the degree of association between keywords appearing in the sentence by using the set relation between the keywords and the context of the sentence. The correlation between the two can increase the accuracy of the judgment.

Similar learning methods are known as the Neural Net Language Model (NNLM), Recurrent Neural Net Language Model (RNNNLM), word2vec, skipgram, and Continuous Bag-of-Words (CBOW). In particular, when using word2vec, word2vec learns from documents and maps each keyword to a vector, and the similarity between two keywords can be determined by calculating the cosine similarity of the two vectors.

In this manner or in a similar manner, the controller 320 may analyze at least a portion of the second set of documents to extract a predetermined number of sub keywords most relevant to each representative attribute keyword.

In operation 530, the controller 320 may extract an association weight corresponding to each representative attribute keyword and a lower keyword pair in the representative attribute keyword candidate set from the second set of documents.

7 is a detailed flowchart of step 530 according to an embodiment of the present disclosure.

In operation 710, the controller 320 may analyze at least some of the second aggregated documents to extract the degree of correlation between the sub-keywords. For example, assume that 50 collected sub keywords B1 ₁ to B1 ₅₀ are collected as associated sub keywords for the representative attribute keyword A1. In this case, the controller 320 may extract the correlation between the two sub keywords using the frequency of the two sub keywords appearing together in the same document. B1 is also interconnected between the _first B1 and B1 ₂ and B1 is determined in accordance with the _first frequency to the _second appearing in the same document. According to another embodiment B1 ₁ and B1 ₂ is The distance between the addition to the frequency appearing in the same document that affects the degree interrelated, B1 ₁ and B1 if the _two appeared in the same document both keywords B1 ₁ and B1 distance between _two (or a statement that both keywords appear ), The higher the correlation, the higher the correlation. In a similar manner, correlations between subkeywords can be extracted. The distance between keywords or the distance between a keyword and a sentence may be, for example, the number of sentences placed between two keywords or keywords and sentences, the number of words placed between two keywords or keywords and sentences, and the distance between two keywords or keywords and sentences. It may be determined based on any one or more of the number of words located, two keywords, or the number of words located between the keyword and the sentence.

In operation 720, the controller 320 may extract an association weight between each representative attribute keyword and the lower keyword based on the degree of correlation between the lower keywords. For example, the control unit 320 may include a correlation between the specific sub-keyword in the sub-keyword set and other sub-keywords in the sub-keyword set with respect to a sub-keyword set corresponding to each representative attribute keyword. An association weight between the specific sub-keyword and the representative attribute keyword may be set such that the association weight between the representative attribute keywords has a positive correlation with each other.

For example, as the correlation degree between the lower keyword B1 ₁ of the representative attribute keyword A1 and the other lower keywords B1 ₂ to B1 ₅₀ of A1 is higher, an association weight between A1 and B1 ₁ may be set higher. For example, keywords of the other sub-B1 ₁ and A1 (B1 B1 ₂ to ₅₀₎ is also the arithmetic mean (or total) correlation between this association may be a weight between ₁ and B1 A1. The geometric mean / harmonic mean may be used instead of the simple arithmetic mean. A cutting average may be used except for the highest two (example) and the lowest two (example) of the correlation between the sub keywords B1 ₁ and the other sub keywords B1 ₂ to B1 ₅₀ . have. The median may be used instead of the arithmetic mean of the correlation.

According to some embodiments, “B1 ₁ and B1 _{2, which} are used to calculate the associative weight of B1 ₁ for A1, are Frequency of appearance in the same document "simply means that B1 ₁ and B1 ₂ B1 ₁ and B1 ₂ are not dependent on the number of documents that appear together (or appear in the same sentence, or appear in close proximity). The number of documents appearing together (or appearing in the same sentence, or appearing in close proximity) may be obtained by dividing the number of documents in which B1 ₁ appears and / or the number of documents in which B1 ₂ appears. In a similar way, "B1 ₁ and B1 ₂ Frequency in the same document "means that B1 ₁ and B1 ₂ Appeared to as (to appear or are in the same sentence, or close to the emergence of) correlation of the number and the notes of the document to the number and the positive correlation between the B1 ₁ appeared number of documents and / or to have B1 ₂ appeared in the document Can be set to have a relationship. It is a kind of normalization to prevent a simple commonly used word from having a high associative weight on the representative attribute keyword A1.

Returning to FIG. 5, in operation 540, the controller 320 may extract a lower association degree between an object item and a lower keyword from the first set of documents.

Among the first set of documents, a document such as an object keyword indicating an object item (for example, "Taylor Swift"), or sub keywords frequently appearing in the same sentence or a close sentence may be determined to be associated with the object item. The controller 320 may collect a document in which an object keyword of the corresponding object item appears, and extract a lower association degree between the lower keyword and the object keyword according to the frequency of appearing together in the documents. In particular, when the sub-keyword appears in the same sentence as the object keyword, the controller 320 may set a higher degree of association between the sub-keyword and the object item than when the sub-keyword appears in another sentence.

The control unit 320 may set that the sentence in which the sub-keyword appears is closer to the sentence in which the object keyword appears, the higher the correlation between the sub-keyword and the object item of the corresponding object keyword. For example, the proximity of two sentences may include, for example, the number of sentences between two sentences, the number of words between two sentences, the number of words between two sentences, and the number of characters located between two sentences. It may be determined based on one or more.

The control unit 320 may set the higher the correlation between the lower keyword and the object item of the corresponding object keyword as the position where the lower keyword appears is closer to the position where the object keyword appears. Proximity between the subkeyword and the object keyword is, for example, the number of sentences located between the subkeyword and the object keyword, the number of words between the subkeyword and the object keyword, and the word between the subkeyword and the object keyword. It may be determined based on any one or more of the number, the number of characters located between the lower keyword and the object keyword.

In operation 550, the controller 320 may extract an object-keyword association between the object item and each representative attribute keyword using the sub-association of operation 540 and the association weight of operation 530.

For example, the object-keyword association between object entry C and representative attribute keyword A1 uses the subassociation between C and A1 subkeywords (eg B1 ₁ through B1 ₅₀ ) and the association weight of each subkeyword. Can be extracted. For example, the object-keyword association degree between the object item C and the representative attribute keyword A1 may be set higher as the lower degree of association between the object item C and B1 ₁ to B1 ₅₀ is higher.

In addition, if the sub-keyword having a higher association weight has a higher subassociation with the object item C in relation to A1, the object C and the representative attribute keyword A1 are lower than the case where the subkey with a low association weight has a high subassociation. The object-keyword association between can be set higher. For example, in the case of Table ₁ , the lower association degree of the side with higher association weight (B1 ₁ ) is higher than that of Table 2, so the case of Table 1 is the object-object between the object C and the representative attribute keyword A1 than the case of Table 2. Keyword relevance may be set high.

Association weight with A1 Subassociation with C B1 ₁ 0.5 0.5 B1 ₂ 0.2 0.2

Association weight with A1 Subassociation with C B1 ₁ 0.2 0.5 B1 ₂ 0.5 0.2

According to an embodiment of the present disclosure, an object-keyword correlation between the object C and the representative attribute keyword A1 may be obtained as the sum (or using the sum) of the product of the association weight corresponding to each sub-keyword and the sub-association. In Table 1, 0.5 × 0.5 + 0.2 × 0.2 = 0.29, and in Table 2, 0.2 × 0.5 + 0.5 × 0.2 = 0.20, so that Table 1 is more between object C and representative attribute keyword A1 than in Table 2. The object-keyword association of may be set high. The above-described object-keyword association calculation method is merely exemplary, provided that the sub-association with C in step 540 and the association weight with A1 in step 530 are positively correlated with the object-keyword association between C and A1. Other methods may be used.

Thereafter, when the communication unit 310 receives an information providing request associated with a specific representative attribute keyword, the control unit 320 may provide a result item through the communication unit 310 based on the object-keyword association degree extracted in step 550. For example, when receiving a request for providing an information including one representative attribute keyword, the controller 320 may provide information about an object item in the order of highest object-keyword association in relation to the representative attribute keyword. have.

In another embodiment, when receiving an information providing request including two or more representative attribute keywords and corresponding weights, the controller 320 associates object-keywords with the representative attribute keywords included in the information providing request for each object item. The sum (or average) of the values multiplied by the weight (or similarly weighted) may provide information about the object item in the highest order.

8 is a flowchart illustrating an information providing process according to another exemplary embodiment of the present specification.

The embodiment of FIG. 8 further includes two steps 523 and 526 between steps 520 and 530 in addition to the same processes as the embodiment of FIG. 5. Here, only the steps 523 and 526 will be described without the repeated explanation.

In operation 523, the controller 320 determines whether each of the lower keywords extracted in operation 520 corresponds to an emotional language (emotional language). To this end, the storage unit 330 or an external server may hold an emotional dictionary. An emotional word dictionary is a tool for determining whether a word (keyword) is an emotional word, and may hold, for example, a list of emotional words. The keywords included in the list of appraisers may be referred to as appraisals, and keywords not included in the appraiser list may not be appraiser. However, these judgments are based on dictionary meanings, and may not reflect the use of words by the public. Accordingly, the controller 320 determines whether to use the representative attribute keyword based on whether the representative keywords of the sub-keywords associated with the representative attribute keyword are used, without determining whether the representative attribute keyword itself is an emotional word.

According to another embodiment, the controller 320 may add another word learned to the appraisal dictionary having a high degree of relevance (more than a preset value) registered with the appraisal dictionary as an appraiser.

In operation 526, the controller 320 may remove only the representative attribute keyword candidate set in the representative attribute keyword candidate set in the order of the high emotional language ratio (or number) of the associated lower keywords, and remove the rest. This process prevents keywords that are far from the emotional language to be treated like the emotional language.

9, in operation 920, the controller 320 stores the basic reserved word-keyword association degree corresponding to the reserved word and the representative attribute keyword pair in the storage unit 330.

The reserved word may include expressions that may be expressed as weights of the representative attribute keywords. For example, "bored" can be a reserved word, and "pretty" can be a reserved word.

Representative keyword with a high degree of basic reserved word-keyword correlation for the reserved word "bored" may include representative attribute keywords that can solve the boring situation, such as "fun", "exciting", "for time out". .

Regarding the Reserved Word "Pretty" A representative attribute keyword with a high basic reserved word-keyword association is a representative attribute that describes, for example, "pretty" or similar to "pretty" such as "beautiful", "cute", "eye-catching". May include keywords.

For example, the process of step 920 may be performed by input of an administrator or by receiving a basic reserved word-keyword association degree determined by an external system through a network or a storage medium. According to another exemplary embodiment, the process of step 920 may be performed in a manner similar to that of FIGS. 5 to 8 by analyzing collectable documents such as the Internet, SNS, and news. In addition, the process of step 920 may include a process of reflecting user feedback as described below.

The process of step 920 may be performed in a manner described below with reference to any one of FIGS. 15 to 17.

11 is an illustration of a basic reserved word-keyword association according to one embodiment of the present specification.

In the example of FIG. 11, there are q reserved words (C ₁ to C _q ), and there are n representative attribute keywords (k ₁ to k _n ).

For example, the basic reserved word-keyword association degree between the reserved word C ₅ and the representative attribute keyword k ₃ is v _3,5 .

In operation 930, the communicator 310 receives and acquires a reserved reserved word from the terminal 200, and transmits the received reserved word to the controller 320.

The reserved reserved word is a reserved word received by the terminal 200 from the search user. The terminal 200 may convert the voice input into an electrical signal (voice signal) and transmit the converted voice signal to the search apparatus 300. The controller 320 of the search apparatus 300 may analyze the voice signal, convert the voice signal into text, and match the converted text with the reserved word. The controller 320 may also analyze the voice signal and analyze the intonation, height, speed, respiration state, etc. of the voice and use it as context information.

According to another exemplary embodiment, the terminal 200 may convert the voice input into text and transmit the converted text to the search apparatus 300. The terminal 200 may analyze the intonation, the height, the speed, the breathing state, etc. of the received voice input and transmit the analysis information to the search apparatus 300. The search apparatus 300 may utilize the analysis information as a kind of context information.

In operation 940, the controller 320 obtains a reserved word-object association corresponding to the received reserved word and each object item pair by using an object-keyword association and a basic reserved word-keyword association.

12 is a detailed flowchart of step 940 according to one embodiment of the present specification.

Referring to FIG. 12, in step 1210, the controller 320 obtains a coordinated object-keyword association corresponding to each object item and representative attribute keyword pair for a reserved reserved word.

According to an embodiment of the present disclosure, the controller 320 may correspond to the received reserved word and the representative attribute keyword pair in an object-keyword association degree corresponding to the object item and the representative attribute keyword pair for each object item and the representative attribute keyword pair. By applying a basic reserved word-keyword association, the adjusted object-keyword association corresponding to each object item and representative attribute keyword pair for the received reserved word may be obtained.

In particular, the control unit 320 is a basic reserved word-keyword corresponding to the received reserved word and the representative attribute keyword pair in an object-keyword association degree corresponding to the object item and the representative attribute keyword pair for each object item and the representative attribute keyword pair. The adjusted object-keyword association degree corresponding to each object item and representative attribute keyword pair for the received reserved word may be obtained using a value multiplied by the degree of association.

In addition, the control unit 320 has a positive correlation with the object-keyword association corresponding to the object item and the representative attribute keyword pair with respect to each object item and the representative attribute keyword pair, and is applied to the received reserved word and the representative attribute keyword pair. Coordinated object-keyword associations may also be set for the received reserved words, corresponding to each object item and representative attribute keyword pair, to have a positive correlation with the corresponding basic reserved word-keyword association.

In this specification, the values representing the object-keyword association, the basic reserved word-keyword association, the adjusted object-keyword association, the basic reserved word-subkeyword association, and other associations are all assumed to be closer to each other. . In other embodiments, a small correlation value may have a close correlation, while another association value may have a higher correlation value, so that a positive correlation-negative correlation may be appropriate. It may be used as an alternative.

For example, controller 320 receives reserved words is C ₂ be when the object item i ₄ and shorthand keyword k corresponding adjustment to the _three pairs of object-object entry to obtain a degree keywords associated i ₄ and shorthand keyword k _three pairs by applying the FIG v _3,4 keyword associated adjustment object-default reserved word corresponding to the received reserved Relevancy C ₂ and k ₃ represent attribute keyword pairs to _4,3 w - equivalent to the object that is able to obtain the keyword associated .

In particular, the method of applying association may be a method of multiplying object-association with basic reserved word-keyword association. For example, controller 320 receives reserved words is C ₂ be when the object item i ₄ and shorthand keyword k corresponding adjustment to the _three pairs of object-object entry to obtain a degree keywords associated i ₄ and shorthand keyword k _three pairs (W _4,3 Хv _{3, 2} ) _, which is the product of the object-relationship corresponding to w _4,3 multiplied by the default reserved word-keyword association degree v _3,2 corresponding to the received reserved word C ₂ and the representative attribute keyword k ₃ pairs. Can be used to obtain a coordination object-keyword association. In another embodiment, the control unit 320 may apply a function f (w _4,3 ) to which the adjustment object-keyword correlation has a positive correlation with w _4,3 and v _3,2 instead of multiplying. , v _{3, 2} ) may be used to obtain a coordination object-keyword association. In addition, (w _4,3 Хv _3,2 ) can be used as a coordinating object-keyword association, and (w _4,3 Хv _{3, 2} ) can be used as a coordinating object-keyword association by applying corrections based on other factors. Both methods can be used.

를 이용하여 획득될 수 있다. f(w_4,j,v_j,2)는 객체 항목 i₄ 및 수신 예약어 C₂, 키워드 k_j에 대응하는 조정 객체-키워드 연관도이다.In operation 1220, the controller 320 may obtain a reserved word-object association degree by using the accumulated value of the adjusted object-keyword association degree for a specific object item. For example, the controller 320 may generate the reserved word-object association degree corresponding to the received reserved word and the specific object item pair so as to have a positive correlation with a cumulative value of the adjusted object-keyword association degree for a specific object item. Can be set. Reserved word-object associations corresponding to the object item i ₄ and incoming reserved word C ₂ pairs are, for example,

It can be obtained using. f (w _{4, j} , v _{j, 2} ) is a coordinated object-keyword association corresponding to the object item i ₄ and the reception reserved word C ₂ , the keyword k _j .

이 될 수 있다. 다른 예에서 객체 항목 i₄ 및 수신 예약어 C₂ 쌍에 대응하는 예약어-객체 연관도는

에 대해 기타 요인에 의한 보정을 가한 값이 될 수 있다.For example, the reserved word-object associations that correspond to object item i ₄ and incoming reserved word C ₂ pairs are

This can be In another example, the reserved word-object associations that correspond to object entry i ₄ and incoming reserved word C ₂ pairs are

It can be a value that is corrected by other factors for.

9, in operation 950, the controller 320 may provide an object item according to a reserved word-object association corresponding to the received reserved word. For example, if the reserved word-object association degree corresponding to the reserved reserved word C ₂ is as shown in Table 3 below, the controller 320 may provide the object items in the order of Table 4 below.

Object item Reserved word-object association with incoming reserved words i ₁ 0.23 i ₂ 0.33 i ₃ 0.99 i ₄ 0.84

order Object item Reserved word-object association with incoming reserved words One i ₃ 0.99 2 i ₄ 0.84 3 i ₂ 0.33 4 i ₁ 0.23

That is, the controller 320 may provide the object items in the order of the high relevance of the reserved word corresponding to the received reserved word. The terminal 200 provided with the object item may provide information about the object item i ₃ to the user through the display unit 220. The terminal 200 may also provide information on other object items in a lower order as necessary. The terminal 200 may provide the user with information about the object item i ₃ by using a speaker instead of the display unit 220.

13 is a flowchart illustrating an information providing process according to another exemplary embodiment of the present specification.

13 to 17 may be performed by partially using the processes of FIGS. 5 to 12 or by partially changing the processes of FIGS. 5 to 12. 13 to 17, the description of FIGS. 5 to 12 may be used when necessary.

Referring to FIG. 13, in step 1310, the controller 320 extracts the representative attribute keyword candidate set from the first set documents. The controller 320 may collect, for example, keywords that frequently appear in a document corresponding to the field of interest in the first set of documents as a representative attribute keyword candidate set. The process of step 1310 may be performed the same as or similar to the process of step 510 of FIG. 5, for example. The process of step 1310 may be performed through the same or similar process as that of FIG. 6. The description of the process of FIG. 6 is not repeated again.

In step 1320, the controller 320 sets a set of reserved words. For example, a set of reserved words may be set by an administrator manually. According to a modification, the controller 320 may provide an interface for setting a word / phrase suitable for a reserved word as a reserved word candidate and setting one or more of the reserved word candidates as a reserved word.

18 is a detailed flowchart of step 1320 according to a modification of the present specification.

In operation 1810, the controller 320 acquires the number of appearances in the document set of one language unit or two or more consecutive language units. The document set may be the same document set as the document set used in step 510 of FIG. 5 or may be another document set.

The language unit may be any one of words, words, morphemes, syllables, and letters. The unit divided by various criteria for classifying other sentences may be a language unit in the present embodiment.

The controller 320 may divide the documents included in each document of the document set into word units before storing in the form of an array or a list before step 1810. According to an exemplary embodiment, the controller 320 deletes words that are meaningless in each word, for example, some search words in Korean, words that are not necessary for an indication adjective such as 'yi', 'low', or other analysis words, or in an array / list. Can be removed In addition, according to an embodiment, when a word consists of one word, the controller 320 may convert the word into a basic form (or a preset form).

According to a modified example, the control unit 320 may divide the documents included in each document of the document set in word units and store them in an array or a list before step 1810. According to an embodiment, the controller 320 may convert each word into a basic form (or a preset format). According to an exemplary embodiment, the controller 320 may remove meaningless words, for example, some words in Korean, words which are not necessary for an indication adjective such as 'yi', 'low', or other analysis from the array / list.

Modifications are also possible in which the control unit 320 divides a document into morpheme units, syllable units, or character units.

In the following embodiments, it is assumed that the controller 320 divides a document into word units for convenience, and each word becomes a language unit.

A single language unit can be a reserved word. According to a variant embodiment, two or more consecutive language units may also be reserved words. For example, "neat" (single language unit) can be a reserved word, and "good mood" (two consecutive language units) can be a reserved word. However, since two or more consecutive language units are less likely to appear than a single language unit, in order to ensure that two or more consecutive language units are selected as reserved words, weights or additional scores for two or more consecutive language units when selecting reserved words Can be given. According to a modification, when selecting a reserved word, a reference value for selecting a reserved word may be more lenient for two or more consecutive language units. For example, if a single language unit has to appear at least a times before it can be recommended as a reserved word candidate, two consecutive language units can be set to be recommended as reserved word candidates even if they appear only a few times a times. The language unit may be set to be recommended as a reserved word candidate even if only c times appear less than b. In the following description, two or more consecutive language units are referred to as continuous language units.

In addition, when consecutive language units are recommended / selected as reserved words, consecutive language units included in the reserved words (candidates) or included in the reserved words (candidates) and shorter than the reserved words (candidates) should not be recommended as reserved words or recommended as reserved words. Penalties may be deducted when the score is calculated. This is to prevent similar reserved words from being selected or recommended. In the following description, the description of the continuous language unit is omitted to simplify the description, but the description of the single language unit may be equally or similarly applied to the continuous language unit.

In operation 1810, the number of appearance of the language unit may be, for example, the number of documents in which the language unit appears. Even if the language unit appears several times in a document, only one occurrence is allowed. According to another embodiment, the case in which the language unit appears several times in a document is recognized as the number of appearances, and the number of appearances may be the number of appearances of the language unit.

According to another embodiment, when the language unit is repeatedly appeared more than once in a document, the second and subsequent appearances may be regarded as lower appearances than the first appearance. Also, the more repeated the language unit appears in a document, the later appearance can be recognized as a lower score. As the number of appearances increases, the score increases, but the slope gradually decreases. For example, a 1 / r square of the number of appearances (r is a real number greater than 1) may be used as the appearance score of the language unit in the document. For example, (log number of appearances) +1 (where 0 is the number of appearances is 0) may be used. In addition, the appearance score of a language unit in a document may be limited not to exceed a preset upper limit. The cumulative score of the language units for all documents may be an appearance score according to the number of appearances of the language unit. This appearance score can then be utilized in step 1830.

In the following description, for the sake of convenience, the number of appearances of a language unit is assumed to be the number of documents in which the language unit appears.

In operation 1820, the controller 320 obtains a number of times the emotional word is located within a predetermined distance from the language unit. The distance between a language unit and an emotional word is, for example, the number of words located between the language unit and the emotional word, the number of words located between the language unit and the emotional word, and the number of characters located between the language unit and the emotional word. It may be determined based on any one or more.

In addition, when the language unit and the emotional word belong to different sentences, the controller 320 may determine that the emotional word is not located within a predetermined distance from the language unit regardless of the number of words / phrases / characters located between the language unit and the emotional word. It may be. According to another variation, when the language unit and the emotional word belong to different sentences, the controller 320 may calculate the distance by adding a predetermined number to the distance calculated by excluding the judgment on the sentence. This is because it is preferable to evaluate the distance more than the number of words / phrases / letters located between the emotional words because the probability that the language unit and the emotional word belong to different sentences is relatively high.

Whether or not a specific word (word) is an emotional word can be checked by checking in advance a registered emotional word dictionary.

The number of times an emotional word is located within a certain distance from the language unit may be, for example, the number of documents in which the language unit and the emotional word are located together within a certain distance. Even if the language unit and the appraiser appear together within a certain distance in a document several times, the number of appearances is recognized only once. According to another embodiment of the present disclosure, when multiple language units are located within a certain distance of a corresponding language unit of a document, all of the various language units may be recognized as the number of times the emotional words are located within a certain distance from the language unit. Hereinafter, the language unit in which the emotional words are located within a predetermined distance is referred to as the emotional language location language unit.

According to another embodiment, when an emotional language location language unit appears repeatedly two or more times in a document, the second and subsequent appearances may be regarded as lower appearances than the first appearance. In addition, the more repeated the appearance of the corresponding language of language position in the document, the later appearance can be recognized as a lower score. As the number of appearances increases, the score increases, but the slope gradually decreases. For example, a 1 / r square of the number of appearances (r is a real number greater than 1) may be used as the appearance score of the language unit of the location of the corresponding word in the document. For example, (log number of appearances) +1 (where 0 is the number of appearances is 0) may be used. In addition, the appearance score of the language unit of the appraiser position in a document may be limited not to exceed a preset upper limit. A cumulative value of appearance scores of the corresponding language unit for all documents may be an appearance score according to the number of appearances of the corresponding language unit. This appearance score can then be utilized in step 1830.

According to another embodiment, as the number of emotional words located within a certain distance with respect to one language unit increases, the appearance score may be recognized higher. In addition, if there is an appraisal within a short distance for one language unit, the appearance score may be higher. In addition, the appearance score (number of appearances) may be recognized only when there are two or more preset numbers of emotion words for one language unit.

In the following description, it is assumed that the number of appearances of an emotional language location language unit is a number of documents in which an emotional word appears within a predetermined distance from the corresponding language unit.

In operation 1830, the controller 320 sets a reserved word candidate in consideration of the number of appearances of the corresponding language unit and the number of times the emotional word is located within a predetermined distance from the language unit.

For example, the controller 320 may multiply the number of appearances of the language unit and the number of times the emotional words are located within a predetermined distance (or according to an operation method having a positive correlation with two other variables) to convert the emotional word scores. The predetermined number may be set as the reserved word candidates in order of increasing score. Alternatively, a language unit corresponding to a predetermined score or more may be set as a reserved word candidate.

Language unit Appearance Appraiser Position Count score First language unit 3003 1122 3369366 Second language unit 2001 1820 3641820 Third language unit 3121 1300 4057300 4th language unit 200 110 22000

In the example of Table 5, candidates for reserved words may be selected in the order of third language unit-> second language unit-> first language unit-> fourth language unit. If the controller 320 recommends two reserved word candidates, the third language unit and the second language unit may be recommended. If the controller 320 recommends a language unit having a score of 3 million points or more as a reserved word candidate, the third language unit, the second language unit, and the first language unit are sequentially recommended as reserved word candidates. According to another embodiment, the controller 320 extracts the first number of language units in order of the number of appearance of the language units, and then based on the number of times the emotion words appear within a certain distance of the extracted language units (or score of the emotion words). A predetermined number of reserved word candidates can be extracted in one order. In the example of Table 5, if three language units are extracted in the order of appearance, the first to third language units may be extracted. Based on the number of appearances of the emotional words, the reserved word candidates may be recommended in the order of the second language unit, the third language unit, and the first language unit.

According to another embodiment of the present disclosure, the controller 320 extracts the first number of language units in order of the number of appearance of the language units, and then based on the number of times that the emotional words appear within a certain distance of the extracted language units (or the score of the emotional words). The reserved word candidates of the second number (where the second number is less than the first number) may be extracted in the order. In the example of Table 5, if three language units are extracted in the order of appearance, the first to third language units may be extracted. If two language units are extracted based on the number of appearances of the emotional words, reserved word candidates may be recommended in the order of the second language unit and the third language unit.

The controller 320 may be similar to or slightly different from the above-described method, in which the score for selecting the reserved word candidate is positively correlated with the number of occurrences of the language unit in step 1810 and the emotional words are located within the preset distance in step 1820. A score for selecting a reserved word candidate may be set to have a positive correlation with the number of appearances of the language unit, and the reserved word candidate may be recommended using this score.

In addition, the controller 320 may process a language unit that is already included in the reserved word set so as not to be added as a reserved word candidate. In addition, the controller 320 may process a language unit that is substantially the same as a reserved word included in the reserved word set to not be added as a reserved word candidate.

In operation 1840, the information providing apparatus 300 provides the terminal 200 with interface information for generating a reserved word selection interface including reserved word candidate information. The interface information can be a document in html format, for example. According to another embodiment, the interface information includes only dynamic information (recommended reserved word candidates, etc.) necessary for generating an interface, and the terminal 200 reflects the dynamic information in a page form previously stored in the terminal 200. To provide users with a page with an interface.

The controller 320 may generate page information for generating a page including a reserved word selection interface including reserved word candidate information, and the communication unit 310 may provide the page information to the terminal 200. The terminal 200 may render and display a page including the corresponding interface to the user. According to a modified embodiment, instead of an interface included in a visual page, an interface by sound or an interface by a technology known or known in the future may be provided. In the following description, it is assumed that an interface included in a visual page is provided for convenience.

19 is an example of an interface 1900 generated according to the interface information provided in step 1840.

Referring to FIG. 19, the interface 1900 includes a table consisting of a checkbox column 1910, a reserved word candidate column 1920, and a detail view column 1930. In addition, the interface 1900 may include a reserved word add button 1940, a candidate delete button 1950, and a put-in box 1960. The user selects the desired reserved word candidate (s) in the checkbox column 1910 and selects one of the add reserved word button 1940, the delete candidate button 1950, and the put in the archive button 1960 to process the reserved word candidate. Can be. When any one button is selected, the terminal 200 may transmit input information obtained by converting a user's input to the information providing apparatus 300.

The information providing apparatus 300 may process the reserved word candidate according to the input information received from the terminal 200. For example, when a user selects a check box 1910 of some reserved word candidates (hereinafter, 'selected candidate') and selects a reserved word add button 1940, the controller of the information providing apparatus 300 that has received input information related thereto ( 320 may add the selected candidates to the reserved word set and delete the selected candidates from the reserved word candidate set. The controller 320 controls not to recommend language unit (s) included in the reserved word set and language unit (s) substantially the same as the language unit (s) included in the reserved word set as a reserved word candidate.

According to another example, when a user selects a check box 1910 of a reserved word candidate and selects a reserved word candidate delete button 1950, the controller 320 of the information providing apparatus 300 that receives the input information related thereto is excluded from the reserved word. Selected candidates may be added to the set and selected candidates may be deleted from the reserved word candidate set. The controller 320 controls not to recommend language unit (s) included in the reserved word exclusion set and language unit (s) substantially the same as the language unit (s) included in the reserved word exclusion set in the future as a reserved word candidate recommendation. do.

According to another example, when a user selects a checkbox 1910 of a candidate candidate for a reserved word and selects a put button 1960, the controller 320 of the information providing apparatus 300 that receives input information related to the reserved word is a reserved word. Selected candidates may be added to the candidate storage set and selected candidates may be deleted from the reserved word candidate set. The controller 320 may not recommend language unit (s) included in the reserved word candidate storage set and language unit (s) substantially the same as the language unit (s) included in the reserved word candidate storage set when the reserved word candidate is recommended in the future. To prevent it.

Instead of the buttons 1940, 1950, 1960, another interface similar to the button or other interface may be used instead of the button.

In addition, the controller 320 may provide an interface for deleting some of the reserved words from the set of reserved words. The controller 320 may provide an interface for removing a language unit from the reserved word exclusion set such that some language units of the reserved word exclusion set are not excluded from the recommendation. The controller 320 may provide a language unit of the reserved word candidate storage set in the form of a list interface similar to that of FIG. 19, and allow the user to add some language units of the reserved word candidate storage set to the reserved word. In addition, the user may include some language units of the reserved word candidate set in the reserved word exclusion set and delete the reserved word candidate set through the list interface. In this case, the corresponding language unit is no longer provided through the list interface of the reserved word candidate storage set and is not recommended as the reserved word candidate through the interface 1900 of FIG. 19. In addition, the user may simply delete some language units of the reserved word candidate storing set from the reserved word candidate storing set through the list interface. In this case, the corresponding language unit is no longer provided through the list interface of the reserved word candidate storage set, but may be recommended as the reserved word candidate through the interface 1900 of FIG. 19.

In addition, the interface 1900 may include a previous page button 1970 and / or a next page button 1980 for moving pages in case the reserved word candidates are not all displayed on one page. The previous page button 1970 and / or next page button 1980 may optionally be provided according to the number of actual candidates and the current page position. In addition, an interface that is expanded through scrolling may be provided instead of the previous page button 1970 and / or the next page button 1980. In some interfaces, only tables that include 1910,1920, 1930 items may be scrolled and buttons 1940, 1950, 1960, 1970, 1980 may be excluded from scrolling.

The user may select the detailed view 1930 to inquire in detail about the background or related information for which the reserved word candidate is recommended. When the detailed view 1930 is selected, the interface provided by the controller 320 includes information about the reserved word candidate and an interface for adding the reserved word candidate to the reserved word, adding to the reserved word candidate storage set, or adding the reserved word exclusion set. can do.

An interface for managing other reserved word candidates may be provided to the user through the terminal 200 by the controller 320.

Returning to FIG. 18, in operation 1850, the controller 320 may add the selected reserved word candidate to the set of reserved words according to an input for selecting the reserved word.

13, in operation 1330, the controller 320 stores an object-keyword association corresponding to the object item and the representative attribute keyword pair.

10 is an illustration of a stored object-keyword association diagram according to one embodiment of the present disclosure.

In the embodiment of FIG. 10, there are m object items (i ₁ to i _m ), and there are n object attributes keywords (k ₁ to k _n ).

For example, the object-keyword association of the object item i ₅ and the representative attribute keyword k ₃ is w _5,3 .

The process of step 1330 may be performed, for example, according to some of the embodiments of FIGS. 5 to 8, or a similar process, or a corresponding process. According to another embodiment of the present disclosure, the process of step 1330 may be performed by an administrator's input or by receiving an object-keyword association degree determined by an external system through a network or a storage medium.

Since the embodiments of FIGS. 5 to 8 have been described above, repeated descriptions thereof will be omitted. However, the process performed in step 510 of FIGS. 5 and 8 is substantially the same as the process of step 1310 of FIG. 13. Therefore, even when the embodiments of FIGS. 5 to 8 are used in performing the process of step 1330, the process of step 510 is not performed again and the result of step 1310 may be recycled.

In operation 1340, the control unit 320 uses the associated weight corresponding to the representative attribute keyword and the lower keyword pair, and the basic reserved word corresponding to the reserved word and the representative attribute keyword pair by using the basic reserved word-sub keyword association degree corresponding to the reserved word and the lower keyword pair. Store the keyword association in the storage unit 330. For example, the process of step 1340 may be performed according to an input of an administrator or according to one or more embodiments of FIGS. 15 to 17.

Before the process of step 1340 or during the process of step 1340, the sub-keyword is determined, the association weight corresponding to the representative attribute keyword and the sub-keyword pair is determined, and the basic reserved word-sub-keyword association degree corresponding to the reserved word and the sub-keyword pair. Should be determined.

The lower keyword used in the process of step 1340 may be determined while step 520 is performed during the process of step 1330. In this case, the subkeyword of step 520 may be used in step 1340. If the lower keyword is not determined in operation 1330, the lower keyword may be determined through the same or similar process as that of the operation 520 and the previous process of FIG. 5.

The association weight used in the process of step 1340 may be determined while step 530 is performed during the process of step 1330. In this case, the association weight of step 530 may be used in step 1340. If the association weight is not determined in operation 1330, the association weight may be determined through a process that is the same as or similar to that of operation 530 and the process of FIG. 5.

The basic reserved word-sub keyword association may be calculated by considering, for example, the frequency of the reserved word and the lower keyword appearing in the same or similar context.

Hereinafter, an example of obtaining the basic reserved word-keyword association degree v _3,2 between the reserved word C ₂ and the representative attribute keyword k ₃ will be described in the description of FIGS. 15 to 17. For example, it is assumed that sub keywords of the representative attribute keyword k ₃ are B3 ₁ to B3 ₅₀ . For a reserved word, a representative attribute keyword, and a basic reserved word-keyword association, refer to the example described above with reference to FIG. 11. The basic reserved word-sub keyword association corresponding to the reserved word C _j and the lower keyword Bg _h pairs is expressed as x _{j, h} . The association weight corresponding to the lower keyword Bg _h and the representative attribute keyword k _g pair is expressed as y _{g, h} . The sub-keyword association degree is expressed as x _{j, g, h} - reserved word C _j, k _g represent attribute keywords and keyword sub Bg _h combination adjustment reserved word corresponding to.

15 is a detailed flowchart of step 1340 according to the first embodiment of the present specification.

Referring to FIG. 15, in step 1510, the controller 320 obtains an adjusted reserved word-sub keyword association by applying an association weight to a basic reserved word-sub keyword association.

In operation 1510, the control unit 320 applies an association weight corresponding to the sub-keyword and the representative attribute keyword pair to a basic reserved word-sub-keyword association degree corresponding to the reserved word and the sub-keyword pair for each reserved word and the sub-keyword pair. The adjusted reserved word-sub keyword association degree corresponding to each reserved word and sub keyword pair for the representative attribute keyword may be obtained.

For example, when the reserved word is C ₂ , the controller 320 may include C ₂ − − corresponding to the lower keyword B _{3 4} and the representative attribute keyword k ₃ pair. The sub-keyword Relevancy sub keyword B3 ₄ and representing the property keywords x _2,4 k ₃ ssang-B3 ₄ between adjustment reserved - to obtain a lower degree keywords associated reserved words C ₂ and sub-keyword B3 ₄ ssang corresponding default reserved for the The adjusted reserved word- _subkeyword association degree x _2,3,4 may be obtained by applying the corresponding association weight y _3,4 .

In particular, the method of applying the association weight may be a method of multiplying the basic reserved word-sub keyword association degree x _2,4 by the association weight y _3,4 corresponding to the lower keyword B3 ₄ and the representative attribute keyword k ₃ pairs. For example, when the reserved word is C ₂ , the controller 320 may include C ₂ − − corresponding to the lower keyword B _{3 4} and the representative attribute keyword k ₃ pair. B3 _Four -adjusted reserved word-subkey word association x _2,3,4 in order to obtain reserved keyword C ₂ and subkeyword B3 ₄ pairs of base reserved word-subkey word association x _2,4 corresponding to _subkeyword B3 ₄ and representative The adjusted reserved word-sub keyword association degree x _2,3,4 may be obtained using (x _2,4 Хy _{3, 4} ), which is a value multiplied by the association weight y _3,4 corresponding to the attribute keyword k ₃ pairs. In another embodiment, the control unit 320 may apply another operation / use scheme in which the adjusted reserved word-sub keyword association degree x _2,3,4 has a positive correlation with x _2,4 and y _3,4 instead of multiplying. The adjusted reserved word-sub keyword association degree x _2,3,4 may be obtained using the function f (x _{2, 4} , y _{3 , 4} ). We can also use (x _2,4 Хy _{3, 4} ) as an adjusted reserved word-subkey association x _{2,3,4 ,} and add a correction based on other factors to (x _2,4 Хy _{3, 4} ) _-Any sub-keyword association x _2,3,4 can be used.

가 될 수 있다. 즉, 하위 키워드 B3₁ 내지 B3₅₀에 대해 예약어 C₂와의 기본 예약어-하위키워드 연관도 x_2,f를 구한 뒤 각각에 대해 해당 하위 키워드에 대한 연관 가중치 y_3,f를 반영하여 x_{2, 3,f}를 구하고 x_{2, 3,f}를 누적하여 예약어 C₂ 및 대표 속성 키워드 k₃ 간의 기본 예약어-키워드 연관도를 구할 수 있다. 다른 실시 예에 따르면, 예약어 C₂ 및 대표 속성 키워드 k₃ 간의 기본 예약어-키워드 연관도는

에 기타 요인에 의한 보정을 가한 값이 될 수 있다. 다른 실시 예에 따르면, 예약어 C₂ 및 대표 속성 키워드 k₃ 간의 기본 예약어-키워드 연관도는

과 양의 상관 관계를 가지는 값이 될 수 있다. 여기서는 하나의 대표속성 키워드에 연결돼 있는 하위 키워드가 50개인 것으로 가정하였으나, 대표속성 키워드에 연결돼 있는 하위 키워드의 개수가 달라지면 수식에서 f의 누적 범위가 50이 아닌 다른 값이 될 수 있을 것이다.In operation 1520, the controller 320 may set a basic reserved word-keyword association degree between the reserved word C ₂ and the representative attribute keyword k ₃ by using the cumulative value of the adjusted reserved word-sub keyword association degree x _{2, 3, f} . That is, the default reserved word-keyword association between the reserved word C ₂ and the representative attribute keyword k ₃ is

Can be That is, the sub-keyword B3 ₁ to base the reserved word with the reserved word C ₂ for B3 ₅₀ - lower keyword Relevancy x _2, after obtaining a _f for each reflecting the associated weight y _{3, f} for its sub-keyword x _{2, 3 ,} to obtain a ₂ x _{f, 3, f} by accumulating the reserved words C ₂ and the representative keyword attribute default reserved word between k ₃ - can be obtained also associated keywords. According to another embodiment, the default reserved word-keyword association between the reserved word C ₂ and the representative attribute keyword k ₃ is

This can be a value added by correction by other factors. According to another embodiment, the default reserved word-keyword association between the reserved word C ₂ and the representative attribute keyword k ₃ is

Can be a positively correlated value. Here, it is assumed that there are 50 sub keywords connected to one representative attribute keyword. However, if the number of sub keywords connected to the representative attribute keyword is different, the cumulative range of f in the formula may be a value other than 50.

16 is a detailed flowchart of step 1340 according to another embodiment of the present specification. Processes of steps 1510 and 1520 of FIG. 16 are the same as those described above with reference to FIG. 15 and thus will not be repeated.

Referring to FIG. 16, in operation 1530, the controller 320 may include a representative keyword having a basic reserved word-keyword association degree of the reserved word and the corresponding representative keyword pair among representative keywords corresponding to a specific reserved word. You can delete the default reserved word-keyword association for. Representative keywords corresponding to a specific reserved word indicate keywords in which a basic reserved word-keyword association degree is set in relation to the specific reserved word. The reference default reserved word-keyword association may be preset. According to another embodiment, the reference basic reserved word-keyword association is set by using an average value of the basic reserved word-keyword association corresponding to a specific reserved word or when the basic reserved word-keyword association corresponding to a specific reserved word is arranged in size order. The default reserved word-keyword association degree of the rank may be set. A specific value having a positive correlation with basic reserved word-keyword associations corresponding to other specific reserved words may be a reference basic reserved word-keyword association. The reference basic reserved word-keyword association degree may be set differently according to the reserved word, or may be set identically for all reserved words. If the default reserved word-keyword association is deleted, it is set as no association between the reserved word and the representative keyword. The control unit may set the association degree to 0, and the base reserved word-keyword association degree of the reserved word and the corresponding representative keyword pair in a list indicating a basic reserved word-keyword association degree (which may be replaced by an array or other data structure). The default reserved word-keyword association may be deleted by deleting the related information. A method of adding information indicating that the other basic reserved word-keyword association has been deleted (or the association has been deleted) may be used.

In step 1530, the default reserved word-keyword association with a relatively fine association is deleted (i.e., set as no association) to perform an overly complex operation or to make the association practically meaningless to the user / administrator. It can be suppressed.

17 is a detailed flowchart of step 1340 according to another embodiment of the present disclosure.

Processes of steps 1510 and 1520 of FIG. 17 are the same as those described above with reference to FIG. 15, and thus descriptions thereof will not be repeated. The process of step 1530 of FIG. 17 is the same as the process described above with reference to FIG.

In operation 1540, the controller 320 may normalize the basic reserved word-keyword association that remains without being deleted. For example, in order to include the average value of the default reserved word-keyword associations stored in association with a specific representative attribute keyword in a specific range, the default reserved word-keyword associations stored in connection with a specific representative attribute keyword are multiplied by a constant coefficient or increased. Can be reduced. In another example, the base reserved word-keyword association associated with a specific representative keyword is stored by multiplying by a predetermined coefficient so that the sum of the basic reserved word-keyword associations stored and linked to the specific representative attribute keyword is included in a specific range. Can be reduced or reduced. That is, in order to prevent a case in which a specific representative attribute keyword has a high default reserved word-keyword association, and no reserved word is selected, only that specific representative attribute keyword is recommended / used or a specific representative attribute keyword is rarely used. Appropriate adjustments can be made.

According to another embodiment, in step 1540, instead of multiplying the basic reserved word-keyword association by a constant coefficient, the controller 320 may add a constant coefficient or combine operations such as logarithmic and squared to perform normalization. According to another embodiment, the normalization may be performed by partially reducing only basic reserved word-keyword associations above a certain reference value or partially increasing only basic reserved word-keyword associations below a certain reference value.

According to another embodiment, in operation 1540, the controller 320 may perform normalization to include an average value (or sum) of basic reserved word-keyword associations stored in connection with a specific reserved word in a specific range.

17 is also a modified example in which step 1530 is omitted and a basic reserved word-keyword association degree normalization process is performed.

In operation 1350, the communication unit 310 receives and acquires a reserved reserved word from the terminal 200, and transmits the reserved reserved word to the control unit 320.

The reserved reserved word is a reserved word received by the terminal 200 from the search user. The terminal 200 may convert the voice input into an electrical signal (voice signal) and transmit the converted voice signal to the search apparatus 300. The controller 320 of the search apparatus 300 may analyze the voice signal, convert the voice signal into text, and match the converted text with the reserved word. The controller 320 may also analyze the voice signal and analyze the intonation, height, speed, respiration state, etc. of the voice and use it as context information.

According to another exemplary embodiment, the terminal 200 may convert the voice input into text and transmit the converted text to the search apparatus 300. The terminal 200 may analyze the intonation, the height, the speed, the breathing state, etc. of the received voice input and transmit the analysis information to the search apparatus 300. The search apparatus 300 may utilize the analysis information as a kind of context information.

In operation 1360, the controller 320 obtains the reserved word-object association corresponding to the received reserved word and each object item pair by using the object-keyword association and the basic reserved word-keyword association. The process of step 1360 may be performed according to the method of step 940 of FIG. 9 or the method of FIG. 12. The description of the same content is omitted.

In operation 1370, the controller 320 may provide an object item according to a reserved word-object association degree corresponding to the received reserved word. The process of step 1370 may be performed in the same manner as the process of step 950. The same description is omitted.

14 is a flowchart illustrating an information providing process according to another embodiment of the present specification.

Since steps 1310, 1320, 1330, 1340, 1350, 1360, and 1370 of FIG. 14 are the same as steps 1310, 1320, 1330, 1340, 1350, 1360, and 1370 of FIG. 13, the same description is not repeated.

Step 1333 added in FIG. 14 may be performed at any time after the representative attribute keyword and the lower keyword are determined. For example, step 1333 may be performed concurrently / in parallel with step 1330 and the process of step 1333 may be performed during step 1330.

In operation 1333, the controller 320 stores an association weight between the representative attribute keyword and the lower keyword. If the process of setting the association weight is not performed in step 1330, the association weight between the representative attribute keyword and the lower keyword may be set through a process similar to the process of step 530 of FIG. 5. According to another embodiment, the controller 320 may store the association weight between the representative attribute keyword and the lower keyword in a manner of calling the association weight set in operation 1330.

In operation 1337, the controller 320 obtains a basic reserved word-subkeyword association degree between the lower keyword and the reserved word.

For example, the controller 320 may determine the degree of association between the reserved word and the lower keyword in consideration of the frequency of the lower keyword appearing in the same or similar context as the reserved word. For example, words appearing around keyword A in a particular sentence may appear around words associated with keyword A in other documents.

"I went on a trip with a big heart, but I suffered because the weather was so hot in July."

"I went on a trip to eat a big heart, but the weather was too humid for July."

Looking at the above two sentences, the word "hot" is replaced with "wet" in the same context. The controller 320 may infer that "hot" and "wet" are words associated with each other.

"I went on a trip with a big heart, but I suffered because the weather was so hot in July."

"I went on vacation with a big heart, but I suffered because the weather was so hot in July."

Similarly, the control unit 320 may infer that the words "travel" and "vacation" are related words through the above two sentences.

"I went on a trip with a big heart, but I suffered because the weather was so hot in July."

"I went on a trip with a big heart, but I suffered because the weather was so hot in August."

Similarly, the controller 320 may infer that the words "July" and "August" are related through the above two sentences.

The controller 320 indicates that words "hot" and "wet" are related words, "July" and "August" are related words, and "travel" and "vacation" are words related to each other through previously collected documents. You can save it.

"I went on vacation with a big heart, but the weather was too humid for July."

"I went on a trip with a big heart but it was hard because the weather was so hot in August."

If the two sentences are not in the same context but know that "hot" and "wet" are related words, "July" and "August" are related words, and "travel" and "vacation" are words related to each other, the control unit 320 ) Can be learned from the above sentence that "toil" and "tough" are also related words.

Keyword pairs with high frequency appearing in the same / similar context may be determined to be highly related to each other. In addition, it may be determined that the higher the similarity of the context in which the two keywords appear, the higher the correlation between the two keywords. The controller 320 proceeds with learning using the collected documents to set the degree of association between keywords, and sets the degree of association between keywords appearing in the sentence by using the set relation between the keywords and the context of the sentence. The correlation between the two can increase the accuracy of the judgment.

Similar learning methods are known as the Neural Net Language Model (NNLM), Recurrent Neural Net Language Model (RNNNLM), word2vec, skipgram, and Continuous Bag-of-Words (CBOW). In particular, when using word2vec, word2vec learns from documents and maps each keyword to a vector, and the similarity between two keywords can be determined by calculating the cosine similarity of the two vectors.

20 is a diagram illustrating a term hierarchy according to an embodiment of the present specification.

After completing the process of FIG. 13 or FIG. 14, the hierarchical relationship between the reserved words C ₁ to C _q , the representative attribute keywords k ₁ to k _n , and the sub keywords BX ₁ to BX ₅₀ is set. .

A basic reserved word-keyword association is set between the reserved word and the representative attribute keyword, and an association weight is set between the representative attribute keyword and the sub-keyword. Using such a hierarchical relationship, the controller 320 may recommend an appropriate object or select a new reserved word candidate according to the reserved word. In addition, the hierarchical relationship of FIG. 20 may reflect new materials, and thus the hierarchical relationship may be modified or supplemented through learning while repeating the processes of FIGS. 13 and 14.

21 is a flowchart illustrating a process of installing a virtual input interface program according to an embodiment of the present invention.

In step 2110, the terminal 200 transmits a package request message to the information providing apparatus 300. The package request message is a message for requesting a virtual input interface program. In operation 2120, the information providing apparatus 300 delivers a virtual input interface program (package) to the terminal 200.

In step 2130, the terminal 200 installs the received virtual input interface program (package). If the installation is unnecessary and the received virtual input interface program can be executed as it is, step 2130 may be omitted. The terminal 200 executes a virtual input interface program according to a user's input and performs a restaurant recommendation method according to master and master.

22A to 22B are diagrams illustrating an interface page of a package for performing a restaurant recommendation method according to alcohol and alcohol according to an embodiment of the present specification.

4, 21, 22a to 22b, the interface page of the package for performing the restaurant recommendation method according to the amount of alcohol according to an embodiment of the present invention is the package installation 2130 in the terminal of FIG. According to this may be an interface page of a package installed in the terminal.

22A to 22B, the interface page 2200 for performing a restaurant recommendation method according to an amount of alcohol and alcohol according to an embodiment of the present invention includes a title display unit 2210, an area selection unit 2220, and an alcohol selection unit. 2230, a liquor selector 2240, a taste filter selector 2250, a recommendation result display unit 2260, and an all view button 2270.

The display unit 220 of the terminal 200 displays the interface page 2200 as the package installed in the terminal 200 is installed and driven.

The interface page 2200 may be a whole page or a partial page of a program in which a package is installed and driven, or may be a page displayed by another web server connected to a program in which a package installed in a terminal is installed and driven.

Hereinafter, the control unit 250 of the terminal 200 controls the display unit 220 to output the contents or the communication unit 230 to communicate with the information providing apparatus 300 or to control the input unit 210. It is described that the terminal 200 operates to receive the contents by controlling or to control and store the storage 240. The display unit 220 of the terminal 200 may be integrally combined with the input unit 210 to perform both the input unit 210 and the display unit 220. Hereinafter, the display unit 220 may perform the function of the input unit 210. It is also described as performing.

The terminal 200 displays the title of the current page or the title of the current program on the title display unit 2210. For example, the terminal 200 may display “Recommendation of restaurants by liquor / liquor” on the title display unit 2210 to display that the current page is a page recommending restaurants by liquor or liquor.

The terminal 200 displays a region on the region selector 2220, and a user may select the displayed region or input a desired region. Regions may be selected according to combo boxes, radio boxes, or lists, and the scope of the present invention is not limited to these embodiments. In addition, the terminal 200 may determine the location of the user through GPS without the user needing to input the region.

The terminal 200 displays an interface for selecting a type of liquor suitable for the type of liquor selected by the master or slave selection unit 2230. The terminal 200 displays a figure having the same width for each drink and a master species in the figure in the master slave selection unit 2230. The figure may be a tab of a rectangular shape or a figure such as a circle or an ellipse.

The user may select which type of alcohol is recommended for a restaurant by touching a figure or a tab. The user's selection of the main character may be changed and underlined so that the color of the name of the main character is distinguished from other main models. Alternatively, the color or the indicator of the figure or the tab itself may be changed so that the figure or the tab on which the name of the master is recorded is different from the figure or the tab on which the main slave is recorded, but embodiments of the present invention are not limited thereto.

The terminal 200 displays the amount of alcohol according to each type in the amount selection unit 2240. In the bottle amount selection unit 2240, the shape of the bottle according to each bottle type may be displayed according to the bottle type. In addition, the terminal 200 may display the amount of liquor as weak, normal or per week by varying the shape of the bottle according to the main type and the amount of liquor contained in the bottle in the amount selection unit 2240. In the embodiment of the present disclosure, but the amount of selector 2240 is displayed by varying the shape of the bottle and the amount of liquor contained in the bottle according to the amount of liquor, but in addition to the bottle may be displayed in the shape of a cup or cup, etc. It may be indicated by.

The user may select a bottle-shaped figure or other visual object according to his / her liquor, and the terminal 200 displays a circle around a bottle, a glass, or a figure in the liquor selector 2240, and displays a liquor display with a different liquor display and color. By varying or varying the thickness, the user can select the amount of liquor selected. The display of the liquor selected by the user may be distinguished from other liquor display bottles or figures by a method in which a check mark is displayed or the color of the surrounding color is changed, but embodiments of the present invention are not limited thereto.

The terminal 200 displays a taste filter related to a restaurant as a result of the search in the taste filter selector 2250 and receives a selection of a taste filter from a user. The taste filter is a kind of reserved word, which is generated according to the contents of the present invention described above with reference to FIGS. 1 through 20, and may be a word, a phrase, or a sentence. ) Receives and transmits a predetermined number of taste filters having a high degree of association with the main quantity or the main species to the terminal 200, and the terminal 200 displays them on the taste filter selection unit 2250. For example, when the user selects the soju in the alcoholic beverage selection unit 2230 and selects weak in the alcoholic beverage selection unit 2240, the terminal 2230 transmits the alcoholic beverages and the alcoholic beverages to the information providing apparatus 300 and provides information. The device 300 transmits to the terminal 200 five taste filters, such as "Shochu bulge", "healthy", "good value", "soft", and "fresh", which are highly related to the type or the amount of alcohol selected by the user. The terminal 200 displays this on the taste filter selection unit 2250.

When the user does not select a taste filter displayed on the taste filter selecting unit 2250, the terminal 200 treats all of the selections and transmits a preset number, for example, five taste filters, to the information providing apparatus 300. .

The recommended result display unit 2260 displays the found restaurant. The searched restaurant is searched and displayed according to the restaurant- taste filter association degree, that is, the reserved word-object association degree described above, and has a high degree of association with the taste filter, that is, the reserved word displayed in the taste filter selection unit 2250. The searched restaurants may be related to the contents described through SNS, portal sites, blogs, Internet cafes, and thesis data according to class and quantity.

The recommendation result is that the information providing apparatus 300 receives a taste filter and a region selected by the taste filter selection unit 2230 from the user transmitted by the terminal 200 and searches for a restaurant based on the taste filter, region and menu type. Thereafter, the search results may be transmitted in a predetermined number, for example, 10 search results having a high relevance, in order of high relevance with the taste filter.

The preference filter associated with the main species is selected by a keyword that is highly related to the main species (soju, beer, etc.), and is determined by determining the frequency of word2vec or the main species and the specific keyword, or the distance between the main species and the specific keyword.

In addition, the degree of association information with the preference menu or keywords related to the amount of alcohol is determined using the article information.

For example, if "Shochu" is selected as the main drink and "Weak" as the quantity, SNS, portal sites, blogs, Internet cafes, etc. have a review of a certain Korean restaurant with pictures of a specialty restaurant, Comments can be with meal photos.

In addition, a few kinds of shochu, or a good food or shochu-related keywords related to the shochu may be described in the thesis and the like, and these thesis data may also be reflected in the search for taste filters.

On the other hand, the reserved word may itself be related to the quantity or the quantity. For example, the reserved word "Shochu" is related to the main soju, the reserved word "healthy" may be related to the quantity "weak", and the reserved word "want" may be related to the quantity "per week".

Therefore, the method of searching for restaurants by alcohol and alcohol is not only by searching through SNS, portal sites, blogs, Internet cafes, and contents and thesis data, but also by the alcohol and alcohol. There may also be a method of searching for and searching for restaurants that are highly related to the taste filter.

The restaurant- taste filter association may be calculated based on the distance between the restaurant and the taste filter or the frequency of the taste filter.

Alternatively, the restaurant-flavor filter association degree may be calculated by extracting sub keywords for the taste filter and using the association between the sub keywords and the restaurant.

The all view button 2270 displays the entire search results. In this case, the search result may be displayed with the entire view button 2270 disappearing on the same page as the existing page, or the page may be changed and displayed on a new page. When the user selects the all view button 2270, the information providing apparatus 300 transmits the entire search results.

Meanwhile, when the interface page 2200 is displayed for the first time, the terminal 200 may display an area, a week or a week, or a week or week in the area selector 2220, the master / slave selector 2230, or the weekly selector 2340. Can be. If the default information is not displayed, a pop-up window may be displayed to inform the user of not selecting a region, selecting a master, or selecting a quantity of alcohol.

FIG. 23 is a diagram illustrating an interface page of a package for performing a restaurant recommendation method according to alcohol and alcohol according to another embodiment of the present specification.

Referring to FIG. 23, an interface page 2300 of a package for performing a restaurant recommendation method according to another embodiment of the present disclosure includes a taste filter display unit 2310 and a recommendation result display unit 2320.

The interface page 2300 may be connected to the terminal 200 when the user selects the all view button 2270 of FIG. 22 or when the user selects the master / base, amount, and taste filter in FIG. 22 and selects a search button (not shown). Can be displayed.

The terminal 200 displays the reserved word or the taste filter used for the search on the taste filter display unit 2310. The taste filter is deleted when the user presses the delete button, and the terminal 200 rearranges the search result by reflecting the remaining filter except the deleted reserved word or the taste filter. That is, the search result is sorted by reflecting the remaining reserved word or taste filter except the search result corresponding to the deleted reserved word or taste filter.

The terminal 200 displays search results according to a taste filter selected by the user on the result display unit 2320.

The search result is that the information providing device 300 receives a taste filter and a region selected by the taste filter selection unit 2250, searches for a restaurant based on the taste filter, the region and the menu type, and then associates with the taste filter. The figures are arranged in high order and transmitted to the terminal 200.

The result display unit 2320 may include a plurality of tabs 2321, and a search result and a ranking of other restaurants may be displayed on each tab. For example, a result reflecting a user's taste may be displayed on a first tab, and an overall restaurant ranking may be displayed on a second tab.

At this time, it will be understood that each block of the flowchart illustrations and combinations of flowchart illustrations may be performed by computer program instructions. These computer program instructions may be mounted on a processor of a general purpose computer, special purpose computer, or other programmable data processing equipment, such that the instructions being performed through the processor of the computer or other programmable data processing equipment are described in the flowchart block (s). It will create means to perform the functions. These computer program instructions may be stored in a computer usable or computer readable memory that can be directed to a computer or other programmable data processing equipment to implement functionality in a particular manner, and thus the computer usable or computer readable memory. It is also possible for the instructions stored in to produce an article of manufacture containing instruction means for performing the functions described in the flowchart block (s). Computer program instructions may also be mounted on a computer or other programmable data processing equipment, such that a series of operating steps may be performed on the computer or other programmable data processing equipment to create a computer-implemented process to create a computer or other programmable data. Instructions for performing the processing equipment may also provide steps for performing the functions described in the flowchart block (s).

In addition, each block may represent a portion of a module, segment, or code that includes one or more executable instructions for executing a specified logical function (s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of order. For example, the two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the corresponding function.

In this case, the term '~ part' used in the present embodiment refers to software or a hardware component such as an FPGA or an ASIC, and '~ part' performs certain roles. However, '~' is not meant to be limited to software or hardware. '~ Portion' may be configured to be in an addressable storage medium or may be configured to play one or more processors. Thus, as an example, '~' means components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, procedures, and the like. Subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and the '~' may be combined into a smaller number of components and the '~' or further separated into additional components and the '~'. In addition, the components and '~' may be implemented to play one or more CPUs in the device or secure multimedia card.

Those skilled in the art will appreciate that the present disclosure may be embodied in other specific forms without changing the technical spirit or essential features of the present disclosure. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. The scope of the present specification is indicated by the following claims rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and the equivalent concept are included in the scope of the present specification. Should be interpreted.

On the other hand, the present specification and drawings have been described with respect to the preferred embodiments of the present specification, although specific terms are used, it is merely used in a general sense to easily describe the technical content of the present specification and help the understanding of the invention, It is not intended to limit the scope of the specification. It will be apparent to those skilled in the art that other modifications based on the technical spirit of the present disclosure may be implemented in addition to the embodiments disclosed herein.

Claims (15)

Local selection unit to receive the region,
Master slave selector to input the master
Liquor selection unit for receiving a liquor for the master species, and
A display unit including a taste filter selection unit which displays a taste filter associated with the master or wine quantity and receives the taste filter;
And a controller for controlling the display unit and transmitting the received region and taste filter to an information providing device. The method of claim 1,
And the display unit further includes a recommendation result display unit for receiving and displaying a restaurant having high association with the selected taste filter and the restaurant- taste filter in the region from the information providing server. The method of claim 1,
The display unit, the taste filter display unit for displaying the selected taste filter, and
And a recommendation result display unit for receiving and displaying a restaurant having a high degree of association between the selected taste filter and the restaurant- taste filter in the region from the information providing server. The method of claim 3, wherein
The restaurant recommendation terminal device according to the masters and the amount of liquor to display the recommended restaurants according to the remaining taste filter if some of the taste filter is deleted. The method of claim 1,
And a taste filter associated with the master alcohol is selected based on any one or more of a distance between the master alcohol and the taste filter and a frequency at which the master alcohol and the taste filter are simultaneously described. The method of claim 1,
The restaurant recommendation terminal device according to the type and amount of liquor and the taste filter associated with the liquor is selected based on the article information. Receiving a region, a master and a liquor selected by the user;
Selecting a taste filter by calculating a master / flavor filter association degree or a liquor-flavor filter association degree with respect to the master or master quantity;
And transmitting a predetermined number of taste filters among the selected taste filters to a terminal. The method of claim 7, wherein
And receiving a taste filter selected from the transmitted taste filters. The method of claim 7, wherein
And the predominant-preferred filter association degree is selected based on any one or more of the distance between the pre-owned master and the taste filter, and the frequency of the pre-owned master and the favorite filter simultaneously. The method of claim 7, wherein
The restaurant-recommended server according to the alcoholic beverages and the amount of alcohol is determined based on the article information associated with the liquor-flavor filter association degree and the liquor. The method according to claim 9 or 10,
Computing a restaurant-flavor filter association; restaurant recommendation server according to the masters and masters configured to perform more. The method of claim 11,
And searching for and transmitting the restaurant associated with the region, the taste filter, and the menu to a terminal. The method of claim 12,
The restaurant is a restaurant recommendation server according to the type and amount of alcohol is sorted in order of the restaurant- taste filter association. The method of claim 11,
The restaurant-flavor filter association degree is the restaurant recommendation server according to the type and the amount of alcohol that is calculated based on the distance between the restaurant and the taste filter or the frequency of the taste filter. The method of claim 11,
The restaurant-flavor filter association degree is a restaurant recommendation server according to the type and amount of alcohol is calculated by using the sub-keywords for the taste filter and the association between the sub-keywords and the restaurant.

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