Service providing device for providing search paths and method for providing search paths

Abstract

To provide a service provision device for search path provision and a method therefor for providing support to be used for marketing by easily selecting a related search word having the highest relevance until reaching a user's requested search word.SOLUTION: The present invention relates to a service provision device for search path provision and a method therefor for providing support to be used for marketing by providing a search path which can grasp a relation between other search words or the like related to a search word requested by a user and the search word as distance, and easily selecting a related search word having the highest relevance until reaching the user's requested search word. The service provision device effectively provides support to be used for marketing by easily selecting a related search word which has the highest relevance to the user's requested search word and has high advertisement efficiency among search words or the like located on a path for reaching the user's requested search word.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a service providing apparatus and method for providing a search path, and more particularly, to a service providing apparatus and method for providing a search path that provides a search path capable of grasping a relationship between a search word requested by a user and other search words related thereto as a distance, and that supports easily selecting the most relevant related search word until the user's requested search word is reached and using it for marketing. [Background technology]

[0002] Recently, various strategies have been developed to promote products and services, and with the development of the Internet, keyword search has become an essential element of digital advertising / marketing strategies.

[0003] In particular, in e-commerce, finding a single effective keyword for advertising can play such a big role that it can determine the results of your overall marketing.

[0004] Also, when a searcher searches for a product or service, the searcher repeats the process of exploration and evaluation before finally deciding on an action (subscription, purchase, etc.). For example, a search for a purchase often starts with a general keyword, then moves to a more specific brand or product keyword, and can be converted to a purchase through a journey of looking at reviews and rankings, or comparing competitors within a product line, etc. Here, the search words that ultimately contribute to the conversion are easy to find because they themselves have a relatively high search volume and conversion rate, but they may not be suitable in terms of advertising efficiency because the advertising unit price is high.

[0005] However, search terms that are in the process of exploration and evaluation around the conversion stage often have relatively low search volume or not much advertising competition, and therefore the advertising cost is low, and therefore may be more suitable in terms of advertising efficiency.

[0006] As mentioned above, it is very important to secure search terms that are suitable for advertising and marketing in terms of advertising efficiency. However, the search terms in the process of exploration and evaluation around the conversion stage are often long-tail keywords that are relatively difficult to find, so there is a problem in using such keywords to create a marketing strategy or optimize advertising.

[0007] Although various existing search engines provide search terms with high advertising unit costs and related search terms, they only present simple related search terms, and because the related search terms are so diverse, it is difficult to separate out efficient related search terms until arriving at the search terms desired by marketers, sellers, etc., and it can be difficult to select search terms that can increase advertising efficiency.

[0008] In other words, marketing strategies need to be developed around conversion stages during a user's search journey (user needs for products and services, comparison items within a product range, and information needed before and after a purchase), and although there are a large number of keywords necessary for advertising optimization, the problem is that it is not easy for ordinary companies and users to discover and utilize them. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Korean Patent No. 10-1194296 Summary of the Invention [Problem to be solved by the invention]

[0010] The present invention aims to generate a basic search word by adding different characters to a keyword, and then to expand the search word by adding related search words provided by a search engine to the basic search word, generate a path that displays the relationship between the subsequent search word and the related search word obtained based on the search result of searching the related search word through a search engine using the input search word as a distance based on the search result, generate such paths for various search words, and provide a path list related to the search word requested by the user, thereby easily selecting the most relevant related search word until the user's requested search word is reached and using it for marketing. [Means for solving the problem]

[0011] The apparatus for providing a service for providing a search path according to an embodiment of the present invention includes a search word extension unit that automatically generates one or more basic search words by combining a keyword with characters by adding a series of characters to a keyword in different ways, and acquires one or more related search words related to the basic search word through a preset search engine; and a distance calculation method in which the search engine extracts one or more subsequent search words from one or more response results corresponding to the function types of one or more response functions used by the search engine to grasp the search intention of the input search word based on returned search result list information, and calculates a weighted value in a connection relationship between the input search word and the subsequent search word according to an exposure rank of the subsequent search word in the response result corresponding to the subsequent search word and a function type of the response result as a distance, the weighted value being a distance between the input search word and the subsequent search word according to the exposure rank of the subsequent search word in the response result corresponding to the subsequent search word and the function type of the response result. The system may include a search information extraction unit that generates relationship information in distance and stores it in a search word relationship DB; a search path extraction unit that generates a directed weighted graph in which a distance between nodes is set by setting each of a plurality of search words as a node based on a plurality of relationship information stored in the search word relationship DB, generates search path information for one or more search paths connecting a start node and an end node determined by a predetermined algorithm in the directed weighted graph, generates statistical information by averaging a statistical amount for each predetermined item of a plurality of search words included in the search path information, matches the search path information, and stores the statistical information in the search path DB; and an optimal path recommendation unit that, when receiving request information including a requested search word and a ranking condition from a user terminal, rearranges one or more search path information including the requested search word based on the ranking condition and the statistical information, and provides the recommendation list information to the user terminal.

[0012] In one embodiment of the present invention, the related search words may be auto-complete search words generated by the search engine based on the basic search words.

[0013] As an example related to the present invention, the search information extraction unit may calculate the weighted value by multiplying a preset priority for a type of function corresponding to the subsequent search word and an exposure ranking of the subsequent search word in a response result corresponding to the subsequent search word, and set the calculated weighted value as the distance between the subsequent search word and the input search word.

[0014] As an example related to the present invention, the search information extraction unit may apply the subsequent search word to the search engine as a different input search word, and based on the returned search result list information, extract one or more texts from one or more response results corresponding to the function types of one or more response functions used by the search engine to grasp the search intent of the input search word according to the distance calculation method as additional subsequent search words corresponding to the subsequent search word, calculate a weighted value in a connection relationship between the input search word and the additional subsequent search word according to an exposure rank of the additional subsequent search word in the response result corresponding to the additional subsequent search word and a function type of the response result as a distance, and generate relationship information in the distance between the one or more additional subsequent search words and the input search word that is the subsequent search word, and store the information in the search word relationship DB.

[0015] As one example related to the present invention, the search path extraction unit may be characterized in that it changes the start node, checks one or more end nodes corresponding to the start node through the algorithm, calculates a predetermined number of K search paths in order of shortest search path distance among the search paths connecting the start node and the end node, and then generates the search path information for each of the calculated search paths.

[0016] As an example related to the present invention, the search path extraction unit may determine a preset number of end nodes in order of relative importance to the start node based on a PageRank algorithm.

[0017] As one example related to the present invention, the search path extraction unit may be characterized by calculating statistical information including an average value of at least one of the statistical values ​​for the search volume, advertising cost, and competitive index for the multiple search words included in the search path information, matching the statistical information with the corresponding search path information, and storing the result in the search path DB.

[0018] As one example related to the present invention, when the optimal path recommendation unit receives the request information from the user terminal, it extracts one or more search path information and one or more statistical information corresponding to the requested search word included in the request information from a search path DB, calculates scores for the extracted one or more statistical information according to a preset score calculation standard for one or more items according to the ranking conditions included in the request information, sorts the extracted one or more statistical information in order of scores, and then selects one or more search path information corresponding to statistical information that is ranked at or above a preset criterion rank from among the one or more search path information corresponding to each of the sorted one or more statistical information, and provides recommendation list information including the selected one or more search path information to the user terminal.

[0019] As an example related to the present invention, the optimal path recommendation unit may include at least one of statistical information corresponding to search path information so as to correspond to each of one or more search path information included in the recommendation list information, search volume information including item-by-item statistics for each of a plurality of search words included in the search path information, and function type information of the function types of the response results corresponding to each of a plurality of search words included in the search path information, and then provide the recommendation list information to the user terminal in the form of a UI or an API.

[0020] The service providing method for providing a search path of the service providing apparatus according to an embodiment of the present invention includes the steps of: automatically generating one or more basic search words by combining the keyword and characters while adding a series of characters to a keyword in different ways; acquiring one or more related search words related to the basic search words through a preset search engine; applying the related search words to the search engine as an input search word, and based on returned search result list information, the search engine grasps the search intention of the input search word and extracts one or more subsequent search words from one or more response results corresponding to the function types of one or more response functions used by the search engine; and calculating a weighted value in a connection relationship between the input search word and the subsequent search word according to an exposure rank of the subsequent search word in the response result corresponding to the subsequent search word and a function type of the response result as a distance, The method may include the steps of generating relationship information on the distance to the input search word and storing the relationship information in a search word relationship DB; generating a directed weighted graph in which each of a plurality of search words is set as a node based on the plurality of relationship information stored in the search word relationship DB and a distance between the nodes is set; generating search path information for one or more search paths connecting a start node and an end node determined by a predetermined algorithm in the directed weighted graph; generating statistical information by averaging a statistical amount for each predetermined item of a plurality of search words included in the search path information; matching the search path information with the search path information and storing the statistical information in the search path DB; and generating recommendation list information by rearranging one or more search path information including the requested search word based on the ranking condition and the statistical information when request information including the requested search word and ranking condition is received from a user terminal, and providing the recommendation list information to the user terminal. Effect of the Invention

[0021] The present invention applies each basic search word obtained by adding different characters to a keyword to a search engine, obtains related search words related to the basic search word, expands the search word, applies the expanded search word to a search engine with an input search word, and obtains the relevance between one or more subsequent search words obtained based on the search result searched through the search engine and the input search word as a distance according to the search intention of the input search word grasped by the search engine and the exposure rank of the subsequent search word, generates a search path by interconnecting search words having a distance based on the distance between such search words, and then provides a recommended search path list related to a requested search word requested by a user and an advertising attribute that the user values, thereby providing an effect of easily selecting related search words that are most related to the user's requested search word and have high advertising efficiency from among search words located on a path to reach the user's requested search word through such recommended search path list, and using them for marketing.

[0022] In addition, the present invention has the effect of helping users easily obtain a list of search words suitable for advertising / marketing, search information for the search words (search volume, advertising cost, advertising competition, etc.), and function type information by providing an optimal search path corresponding to the search words requested by the user in the form of a UI or API.

[0023] In addition, the present invention goes beyond simply providing related search words through a word network UI generated based on a search path, and by expressing the connection relationships between search words, it is possible to provide a user with a grasp of the main search journey for products and services, as well as to help the user more intuitively grasp the needs of the user, such as what they are looking for and what they are comparing. [Brief description of the drawings]

[0024] [Figure 1]2 is a configuration diagram of a service providing device for providing a search path according to an embodiment of the present invention; [Diagram 2] 4 is an exemplary diagram illustrating an operation of a service providing device according to an embodiment of the present invention; [Diagram 3] 11 is a diagram illustrating an example of a process of acquiring related search words of a service providing device according to an embodiment of the present invention; [Figure 4] FIG. 2 is an exemplary diagram of a SERP Feature (function type) type definition of a service providing device according to an embodiment of the present invention. [Diagram 5] 13 is a diagram illustrating an example of subsequent search word detection by a service providing device according to an embodiment of the present invention; [Figure 6] 13 is a diagram illustrating an example of generating a search word relation graph in a service providing device according to an embodiment of the present invention. [Figure 7] 11 is an exemplary diagram illustrating a search path generation of a service providing device according to an embodiment of the present invention; [Figure 8] 13 is an exemplary diagram illustrating a service providing device providing recommendation list information based on an API including a recommendation search path according to an embodiment of the present invention. [Figure 9] 13 is an exemplary diagram illustrating a recommended search password network UI of a service providing device according to an embodiment of the present invention. [Figure 10] 13 is an exemplary diagram illustrating a node highlight function in a recommended search password network UI of a service providing device according to an embodiment of the present invention. [Figure 11] 4 is a flowchart of a service providing method for providing a search path according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, detailed embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a configuration diagram of a service providing device for providing a search path (hereinafter, referred to as the service providing device) according to an embodiment of the present invention.

[0026] As shown in the figure, the service providing device (100) according to an embodiment of the present invention may be configured to include a communication unit (120), a storage unit (130), and a control unit (110), etc., but may be configured to include various components without being limited thereto.

[0027] First, the communication unit 120 can communicate with one or more user terminals and various external servers via a communication network.

[0028] Furthermore, the communication network described in the present invention may include wired / wireless communication networks. Examples of such wireless communication networks include Wireless LAN (WLAN), DLNA (Digital Living Network Alliance), Wibro (Wireless Broadband: Wibro), Wimax (World Interoperability for Microwave Access: Wimax), GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), CDMA2000 (Code Division Multi Access 2000), EV-DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only), WCDMA (Wideband CDMA), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), IEEE 802.16, Long Term Evolution (LTE (Long Term Evolution)), LTE-A (Long Term Evolution-Advanced), and Wireless Mobile Broadband (Wireless Mobile Broadband). These include Wireless Broadband Service (WMBS), 5G mobile communications services, Bluetooth (registered trademark), LoRa (Long Range), RFID (Radio Frequency Identification), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee (registered trademark), Near Field Communication (NFC), Ultra Sound Communication (USC), Visible Light Communication (VLC), Wi-Fi, and Wi-Fi Direct.Furthermore, examples of wired communication networks include wired LAN (Local Area Network), wired WAN (Wide Area Network), Power Line Communication (PLC), USB communication, Ethernet (registered trademark), serial communication, and optical / coaxial cables.

[0029] In addition, the storage unit (130) can store various information, and can be configured in various forms such as a hard disk drive (HDD) or a solid state drive (SSD), and can also be configured to include one or more DBs.

[0030] As an example, the storage unit (130) may be configured to include a plurality of DBs necessary for the operation of the service providing device (100), and may include various DBs such as a keyword DB (101) in which a plurality of different keywords are stored, a search word relationship DB (102) in which relationship information regarding the relationship between search words is stored, a function type DB (103) in which function types corresponding to search words and related information are stored, a search path DB (104) in which search paths and statistical information corresponding to search words are stored, and a search volume DB (105) in which search volume information including item-specific statistics preset for each search word is stored.

[0031] Here, the multiple DBs included in the storage unit (130) may each be configured as a separate database server, and the service providing device (100) may communicate with and be linked to the multiple different database servers via a communication network.

[0032] In addition, the control unit (110) performs the overall control function of the service providing device (100), and the control unit (110) may include a RAM, a ROM, a CPU, a GPU, and a bus, and the RAM, ROM, CPU, GPU, etc. may be connected to each other via a bus.

[0033] Also, the communication unit (120) and the storage unit (130) may be configured to be included in the control unit (110). Here, at least one of the control unit (110) and the various components constituting the control unit (110) can communicate with the user terminal and an external server via the communication unit (120), and in the following, the communication configuration through the communication unit (120) will be omitted.

[0034] As shown in the figure, the control unit (110) can be configured to include a search word expansion unit (111), a search information extraction unit (112), a search path extraction unit (113), and an optimal path recommendation unit (114). Here, the multiple components constituting the control unit (110) can be realized by a processor capable of data processing, and each component can be separated and realized by a different processor, or can be functionally separated within a single processor.

[0035] Based on the above-mentioned configuration, the detailed operation configuration of the service providing device (100) will be described with reference to the following drawings.

[0036] FIG. 2 is a detailed operational configuration diagram of the service providing device (100) according to an embodiment of the present invention, and the configuration of the control unit (110) that performs the substantial control function of the service providing device (100) will be mainly described.

[0037] As shown in the figure, the search word extension unit (111) can extract a keyword from a keyword DB (101) in which a plurality of different keywords for companies, brands, products, services, categories, etc. are stored, and can automatically generate one or more search words each of which is a combination of the keyword and one or more characters by adding a series of characters to the keyword in a different manner, as a basic search word.

[0038] Here, the search word expansion unit 111 can communicate with an external knowledge server that provides an online electronic dictionary, collect the keywords from the external knowledge server, and store the keywords in the keyword DB 101. Examples of such an external knowledge server include a server that provides DBPEDIA, Wikipedia (registered trademark), etc.

[0039] As an example, the search word expansion unit (111) can connect to the external knowledge server and extract and store the page name or the title of the knowledge panel as the keyword.

[0040] In addition, the search word expansion unit 111 can obtain one or more related search words related to the basic search word through a preset search engine.

[0041] To explain this with reference to FIG. 3, the search word extension unit (111) extracts the keyword "Kookin Bank" stored in the keyword DB (101), adds "Ga" to the keyword to generate a basic search word such as "Kookin Bank Ga", and adds "Na" to the keyword to generate a basic search word such as "Kookin Bank Na".

[0042] In addition, the search word extension unit (111) can add characters (or letters) from "da" to "ha" in order to the keyword "Kookmin Bank" to generate basic search words corresponding to each added character. In addition to the above examples, it can also add a character with a final sound such as "gan" to the keyword, add a foreign word such as "A", or add multiple characters such as "ga-ga" and "AA".

[0043] In addition, the characters that the search word extension unit (111) can add to the keyword can include blank characters and special characters.

[0044] In addition, the search word expansion unit (111) can apply the search word "Kookmin Bank.ga" generated as described above to a search engine preset in the service providing device (100) or a search engine provided by an external server through communication with the external server, and obtain related search words related to the "Kookmin Bank.ga" from the search engine.

[0045] As an example of such a search engine, the search engines "NAVER" (registered trademark) and "GOOGLE" (registered trademark) can be used, and when the search engine is included in the service providing device (100), execution data related to the search engine can be stored in the storage unit (130) of the service providing device (100).

[0046] The related search words may also be composed of autocomplete search words (or autocomplete query languages) that the search engine autocompletes when a basic search word is input and a meaningful search word (or query language) is generated.

[0047] For example, when the search engine inputs the basic search word "Kookmin Bank Ga" from the search word expansion unit (111), the search engine automatically generates related search words such as "Kookmin Bank virtual account" and "Kookmin Bank household account book" which are auto-complete search words starting with "Kookmin Bank Ga" or including the basic search word, and the search word expansion unit (111) can obtain one or more related search words for one basic search word from the search engine.

[0048] As another example, when the basic search word “iPhone-ga” is input from the search word extension unit (111), the search engine can automatically generate related search words such as “iPhone price” and “iPhone forced shutdown” that start with “iPhone-ga” or are autocomplete search words that include the basic search word.

[0049] Alternatively, the search engine may search a database that includes an external server that provides the search engine and stores various search words based on the basic search word, obtain related search words that start with the basic search word or that include the basic search word, and then provide the related search words to the search word extension unit (111).

[0050] In addition, the search engine may be included in an external server that provides the search engine based on a basic search word, and may search the DB in which various search words are stored to generate related search words in a sentence, or may extract the sentence including the basic search word from the DB and provide it to the search word extension unit (111) as related search words.

[0051] In addition, the search word extension unit (111) can store the related search words acquired through the search engine as keywords in the keyword DB (101).

[0052] Meanwhile, the search information extraction unit (112), in cooperation with the search word extension unit (111), applies the one or more related search words corresponding to the basic search word to the search engine as an input search word, and obtains search result list information including one or more search results corresponding to the input search word through the search engine.

[0053] In addition, the search results described in the present invention may be search result information generated by the search engine, and the search result information may include search language, search area, search engine name, search result position (rank), domain, feature type (SERP Feature), title, body text, etc., and may be composed of a text-based document.

[0054] In addition to providing document-based search results on websites or web pages for an input search word, the search engine may also grasp the search intent of the input search word in the search engine, generate direct response results using a response function corresponding to the grasped search intent among one or more different unique response functions provided by the search engine, and then provide the result as the search result.

[0055] For example, as shown in FIG. 4, a search engine provides response results for one or more response features as search results through a SERP (Search Engine Result Page), and generates and provides response results according to a function type (SERP feature) corresponding to the search intent among a plurality of different response function types pre-set in the search engine, such as Keyword Ad (AD), Ad Carousel, Application, Featured Snippets, Image, Job Search, Knowledge Panel, People also search for, Related Searches, etc., by grasping the search intent of an input search word among the response results provided through the SERP.

[0056] For example, as shown in FIG. 5, when an input search word is input, a search engine can grasp the search intent of the input search word and generate multiple response results each corresponding to a response function such as “People also search for,” which is a response function in a collection of other search words searched together with the search word, and “related search words,” which is a response function in a collection of other search words related to the search word. Such response results may include an identifier of the type of function (response function) corresponding to the response result, a response processing result (or response content) obtained through the response function, etc.

[0057] That is, when an input search word for "women's perfume recommendations" is input, the search engine grasps the search intent of the input search word, and based on the grasped search intent, calculates a response result including one or more subsequent search words such as "women's perfume rankings 2021" or "women's perfume rankings 2021" using the "People also search for" response function, or calculates a response result including one or more subsequent search words such as "Olive Young women's perfume recommendations" or "women's perfume gifts" using the "related search word" response function, and returns the response result as a search result.

[0058] In addition, the search information extraction unit (112) may check the search result list information and delete or exclude related search words that do not search anything, and not store them in the keyword DB (101) (102). That is, the search information extraction unit (112) may exclude or delete related search words whose search volume is 0, without storing them in the keyword DB (101).

[0059] In addition, the search information extraction unit (112) may, when one or more response results are included in one or more search result list information corresponding to one or more related search words corresponding to the basic search word, identify a function type from each of the one or more response results, generate function type information including the identified function type and an appearance frequency of each of the identified function types, and match the generated function type information with the corresponding related search words, and store the generated function type information in a function type DB (103).

[0060] In addition, the search information extraction unit (112) may extract, as a subsequent search word, one or more texts from one or more response results each corresponding to a function type for one or more response functions used by the search engine to grasp the search intent of the input search word based on search result list information returned by the search engine for the input search word, and generate relationship information in the distance between the input search word and the subsequent search word for each of the extracted one or more subsequent search words using a distance calculation method that calculates, as a distance, a weighted value in the connection relationship between the input search word and the subsequent search word based on the exposure rank of a specific subsequent search word in the response result corresponding to the specific subsequent search word and the function type of the response result corresponding to the specific subsequent search word.

[0061] Here, the search information extraction unit (112) may calculate the weighted value by multiplying a preset priority for a type of function corresponding to the subsequent search word by an exposure rank (or a placement rank) of the subsequent search word in a response result corresponding to the subsequent search word, and set the calculated weighted value as the distance between the subsequent search word and the input search word.

[0062] As an example, the search information extraction unit (112) can identify multiple response results (search results) included in the search result list information of the input search word “women’s perfume recommendations” and the corresponding function types “others also searched for” and “related search words.”

[0063] Here, the search information extraction unit (112) may be preset with setting information in which one or more function types that are targets for extracting the subsequent search word among a plurality of function types provided by the search engine are preset, and the subsequent search word may be extracted only from the function types that are targets for extracting the subsequent search word.

[0064] In addition, the setting information may preset different priorities among one or more types of functions that are targets for the extraction of the subsequent search word.

[0065] Accordingly, the search information extraction unit (112) assigns priority values ​​of 1 and 2 to each of the multiple function types, "others also search for" and "related search words", identified in the search list information corresponding to the input search word "women's perfume recommendations" according to the setting information, assigns an exposure rank (or an exposure rank weighting value) according to the ranking of the subsequent search word within the function type, extracts one or more subsequent search words from each of the multiple response results, such as ["Women's perfume ranking 2021", priority 1, exposure rank 1, distance 1], ["Women's perfume ranking 2021 for women in their 20s", priority 1, exposure rank 2, distance 2], ["Olive Young women's perfume recommendations", priority 2, exposure rank 1, distance 2], and ["Women's perfume gift", priority 2, exposure rank 2, distance 4], etc., calculates a weighting value, which is a value obtained by multiplying the priority and exposure rank for each of the extracted one or more subsequent search words, and then calculates the weighting value as a distance (distance weighting value) from the input search word.

[0066] Here, the search information extraction unit 112 may identify (detect) the placement order of the subsequent search words for each subsequent search word from the response result, and may determine the exposure order of the subsequent search words according to the placement order.

[0067] In addition, the priority may be defined as a priority weight, the exposure rank may be defined as an exposure rank weight, and the distance calculated as a value obtained by multiplying the priority weight and the exposure rank weight may be defined as a distance weight.

[0068] In addition, the search information extraction unit (112) can generate relationship information including the calculated distance and the subsequent search word and the input search word for each of one or more subsequent search words corresponding to the input search word based on the input search word, and can store the relationship information in the search word relationship DB (102).

[0069] From the above configuration, the search information extraction unit (112) can apply the subsequent search word to the search engine as a separate input search word, and thereafter, applies the subsequent search word to the search engine as the input search word, and extracts one or more texts from one or more response results corresponding to the function types of one or more response functions used by the search engine to grasp the search intent of the input search word according to the distance calculation method based on the returned search result list information, as additional subsequent search words corresponding to the subsequent search word, and then calculates a weighted value in the connection relationship between the input search word and the additional subsequent search word according to the exposure rank of the additional subsequent search word in the response result corresponding to the additional subsequent search word and the function type of the response result corresponding to the additional subsequent search word as a distance, and generates relationship information in the distance between the input search word that is the subsequent search word for each of the one or more additional subsequent search words, and stores the information in the search word relationship DB (102).

[0070] That is, the search information extraction unit (112) may apply the subsequent search word to a search engine again as the input search word each time a subsequent search word corresponding to the input search word is extracted, and may repeatedly extract other subsequent search words related to the subsequent search word according to the distance calculation method, and may generate relationship information in the distance relationship between the input search word related to the other subsequent search word and the other subsequent search word, and store the relationship information in the search word relationship DB (102).

[0071] Meanwhile, the search path extraction unit (113) may generate a directed weighted graph by setting each of a plurality of search words corresponding to a plurality of relationship information as a node based on a plurality of relationship information stored in the search word relationship DB (102) and setting a distance between the nodes, and may then calculate path information for one or more paths connecting a start node and an end node determined by a preset algorithm in the directed weighted graph, and may generate statistical information by averaging preset item-based statistics for a plurality of search words included in the path information in conjunction with an external server (including) that provides the search engine, and may then match the path information and store the statistical information in the search path DB (104).

[0072] Here, the search path extraction unit (113) can determine that a distance value exists between a plurality of different search words based on the relationship information, and can generate the directed weighted graph by connecting the plurality of different search words having distance values ​​with connecting lines and setting distance values ​​to the connecting lines.

[0073] Furthermore, the path may be configured to include one or more connecting lines for reaching the end node from the start node and nodes each corresponding to the one or more connecting lines, and hereinafter, the path is referred to as a search path, and path information corresponding to the path is referred to as search path information.

[0074] As an example, as shown in FIG. 6, the search path extraction unit (113) extracts connection relationships and distance information (weighted value information) between multiple search words included in multiple relationship information based on multiple relationship information stored in the search word relationship DB (102), sets each search word as a node, and generates a directed weighted graph with edges representing forward relationships in which the distance (distance value or distance weighted value) according to the relationship information is added to the distance between different nodes (or the search path connecting different nodes).

[0075] As an example, the directed weighted graph may be configured to include input search words and subsequent search words as nodes, relationships between the input search words and subsequent search words as forward edges, and distance weights between the input search words and subsequent search words may be set on the edges.

[0076] Here, the search path extraction unit (113) can store the directed weighted graph in the search path DB (104).

[0077] In addition, the search path extraction unit (113) may select an arbitrary search word as a start node according to a preset algorithm in the directed weighted graph, and then select N nodes with high relative importance as an end node from among the nodes connected in a forward direction within L hops from the start node according to the preset algorithm.

[0078] In addition, the relative importance of different nodes based on the start node can be determined using a Pagerank algorithm that determines the importance of a web document.

[0079] In addition, the search path extraction unit (113) may generate search path information for each of one or more search paths connecting the selected start node and the selected end node at each of the selected one or more end nodes based on the selected start node according to the directed weighted graph.

[0080] In addition, the search path extraction unit (113) can change the start node and identify one or more end nodes corresponding to the start node through the algorithm, calculate K pre-defined search paths in order of shortest search path distance (or total search path distance) among the search paths connecting the start node and the end node, and then generate the search path information for each of the calculated search paths.

[0081] In addition, the search path extraction unit (113) can generate path list information including one or more pieces of search path information whose start node and end node are the same. That is, the search path extraction unit (113) can generate path list information by grouping one or more pieces of search path information whose start node is a first search word and whose end node is a second search word.

[0082] Also, as shown in FIG. 7, when generating the path list information, the search path extraction unit (113) can communicate with an external server that provides the search engine and request pre-set item-specific statistics for a plurality of search words included in the path list information from the external server, and can generate statistics list information based on the item-specific statistics collected (received) for the plurality of search words from the external server.

[0083] Here, the preset items may include search volume, advertising cost, competitive index, etc., and the search path extraction unit (113) may store the search volume information including statistics for each of the preset items for each of the plurality of search words from the external server in a search volume DB (105) included in the service providing device (100).

[0084] In addition, when the search engine is included in the service providing device (100), the control unit (110) of the service providing device (100) may further include a search volume generation unit that generates the search volume information in conjunction with the search engine, and the search volume generation unit may store the search volume information in the search volume DB (105) each time it generates search volume information for each of a plurality of search words in conjunction with the search engine.

[0085] Also, the search path extraction unit (113) can match the statistics list information corresponding to the path list information with the path list information and store the matched information in the search path DB (104).

[0086] In addition, the search path extraction unit (113) can check the search volume of the search word corresponding to the end node from one or more search path information pieces having the same start node and end node, and can exclude the search path information pieces corresponding to the end node whose search volume is 0 from the path list information.

[0087] In addition, the search path extraction unit (113) may confirm the predetermined item-specific statistics of one or more search words included in the search path information in each of one or more search path information included in the path list information based on the statistics list information, and then average the confirmed search word-specific statistics for the predetermined items to generate statistical information including an average value of the item-specific statistics.

[0088] As an example, the statistical information may include an average search volume for multiple search words included in specific search path information, an average advertising cost for multiple search words included in specific search path information, an average competitive index for multiple search words included in specific path information, etc.

[0089] In addition, when generating the statistical information, the search path extraction unit (113) can match the statistical information with search path information corresponding to the statistical information and store the matched information in the search path DB (104).

[0090] Here, when generating search path information, the search path extraction unit (113) can set a unique identifier to the search path information, and can also set the unique identifier in statistical information matching the search path information, thereby enabling mutual matching of the search path information and the statistical information.

[0091] Alternatively, the search path extraction unit (113) can include the statistical information in search path information corresponding to the statistical information and store the information in the search path DB (104).

[0092] An example of this will be described with reference to FIG. 7. As shown in the figure, when the search path extraction unit (113) selects "women's perfume recommendations" as the start node (starting search word), it selects "faint women's perfume" as the end node (ending search word) according to the algorithm, and generates a plurality of search path information corresponding to a first search path ("women's perfume" → "women's perfume recommendation ranking 2021" → "faint women's perfume") and a second search path ("women's perfume" → "women's perfume ranking for women in their 20s 2021" → "faint women's perfume") connecting the start node and the end node based on the directed weighted graph, and then generates path list information including the plurality of search path information.

[0093] In addition, the search path extraction unit (113) can, in conjunction with the external server, identify a plurality of search words included in the path list information based on one or more search path information included in the path list information, collect item-specific statistics for each of the plurality of search words, and generate statistics list information.

[0094] In addition, the search path extraction unit (113) can check the statistics for each of a plurality of pre-set items for each of a plurality of search words including “women's perfume”, “women's perfume recommendation ranking 2021”, and “faint women's perfume” based on the first search path information corresponding to the first search path included in the path list information and the statistics list information corresponding to the path list information, calculate the average search volume (347), the average advertising cost (0.44), and the average competitive index (0.95), generate first statistical information including the calculated average search volume, average advertising cost, and average competitive index, match the first statistical information with the first search path information, and store it in the search path DB (104).

[0095] Similarly, the search path extraction unit (113) can calculate second statistical information including an average search volume (373), an average advertising price (0.34), and an average competitive index (0.91) based on second search path information corresponding to the second search path included in the path list information and statistical amount list information, and then match the second statistical information with the second search path information corresponding to the second statistical information and store it in the search path DB (104).

[0096] Meanwhile, when the optimal path recommendation unit (114) receives request information including a requested search word and a ranking condition from a user terminal, it can generate recommendation list information (recommended path list information) by rearranging one or more search path information including the requested search word based on the ranking condition and statistical information, and provide the recommendation list information to the user terminal.

[0097] Here, the service providing device (100) may include a user input unit that receives user input, and the optimal path recommender (114) included in the control unit (110) may receive the request information based on user input via the user input unit.

[0098] In addition, the ranking conditions may be set based on various conditions such as search volume, advertisement cost, competitive index, and the presence or absence of a variety of functions.

[0099] That is, when the optimal path recommendation unit (114) receives the request information from the user terminal, it extracts one or more search path information and one or more statistical information corresponding to the requested search word included in the request information from the search path DB (104), calculates scores for the extracted one or more statistical information according to a preset score calculation standard for one or more items set in the ranking conditions included in the request information, sorts the extracted one or more statistical information in order of scores, and then selects one or more search path information corresponding to statistical information that is ranked at or above a preset criterion rank from among the one or more search path information corresponding to each of the sorted one or more statistical information, and provides recommendation list information including the selected one or more search path information to the user terminal.

[0100] Here, the optimal path recommendation unit (114) may compare a calculated score for statistical information corresponding to search path information with a score for another statistical information (etc.) to calculate a ranking of the statistical information, and then determine and rearrange the ranking of one or more search path information corresponding to the request information in a ranking determination method in which the calculated ranking is determined as the ranking of the search path information corresponding to the statistical information, and may select one or more search path information that is equal to or higher than a predetermined reference ranking from among the one or more search path information corresponding to the request information.

[0101] From the above-mentioned configuration, the optimal path recommendation unit (114) can extract only one or more search path information having the requested search word as an end node from the search path DB (104), and generate recommendation list information based on the extracted one or more search path information.

[0102] That is, the optimal path recommender 114 may select a search path consisting of one or more search words necessary to reach the requested search word as a recommended search path, and generate recommended list information.

[0103] In addition, the optimal path recommendation unit (114) may include at least one of statistical information corresponding to search path information, search volume information including item-by-item statistics for each of a plurality of search words included in the search path information, and function type information of the function types of the response results corresponding to each of a plurality of search words included in the recommended search path information, in the recommendation list information so as to correspond to one or more search path information included in the recommendation list information, and then provide the recommendation list information to the user terminal in the form of a UI or API.

[0104] Here, the optimal path recommendation unit (114) may generate analysis result information including the recommendation list information and at least one of statistical information corresponding to the search path information for each of one or more search path information included in the recommendation list information, search volume information including item-by-item statistics for each of a plurality of search words included in the search path information, and function type information for the function types of the response results corresponding to the plurality of search words included in the search path information, in the form of a UI or API, and provide the analysis result information to the user terminal.

[0105] To this end, the optimal path recommendation unit (114) can provide the recommendation list information to the user terminal by including one or more statistical information corresponding to one or more search path information included in the recommendation list information, thereby allowing the user terminal to display specific statistical information corresponding to specific search path information selected by the user from the one or more search path information included in the recommendation list information together with the specific search path information.

[0106] In addition, the optimal path recommending unit (114) can request statistics for each item preset for a plurality of search words included in the recommendation list information from the external server, generate statistics list information based on the statistics for each item collected (received) for a plurality of search words corresponding to the recommendation list information from the external server, and provide the statistics list information to the user terminal by including it in the recommendation list information. Here, the preset items can include search volume, advertisement cost, competition index, etc.

[0107] In addition, the optimal path recommendation unit (114) can extract search volume information for each of a plurality of search words included in the recommendation list information from the search volume DB (105), and generate the statistics list information based on the extracted search volume information for each of a plurality of searches, and then include the statistics list information in the recommendation list information and provide it to the user terminal.

[0108] Thereby, when the user terminal receives the recommendation list information, it displays specific search path information selected by the user from one or more search path information included in the recommendation list information, and when a specific search word included in the specific search path information is selected by the user, it can display and provide statistics (or search volume information) for each item related to the specific search word.

[0109] As an example of the above, as shown in FIG. 8, when the optimal path recommender (114) receives request information, when the requested search word "Anbang Grill" according to the request information and the ranking condition "order by cost per click (CPC)" according to the request information are received, the optimal path recommender (114) acquires one or more search path information corresponding to the requested search word "Anbang Grill" as recommended search path information from the search path DB (104), and then acquires one or more statistical information corresponding to the one or more recommended search path information as interest statistical information from the search path DB (104).

[0110] In addition, the optimal path recommendation unit (114) can generate and return recommendation list information in the form of an API (Application Programming Interface) in which the one or more pieces of recommended search path information are sorted in order of advertising cost, which is a ranking condition, based on the one or more pieces of interest statistical information.

[0111] As an example, the CPC values ​​of the search terms “grill”, “grill”, “grill recommendation”, “Anbang Grill”, “Anbang Grill 501”, “Anbang Grill AB301MF”, and “fish grill” from the first recommendation search path with the highest ranking in the recommendation list information, “grill” → “grill” → “grill recommendation”, “Anbang Grill”, “Anbang Grill 501”, “Anbang Grill AB301MF”, and “fish grill”, are 2.503594, 0.119853, and 0.12749, respectively. 8, 0.248646, 0, 0.118544, and 0.28, the average CPC value of the search words etc. included in the first recommended search path, which is 0.4854, can be calculated (returned) as the score of the first recommended search path, and for each of the other recommended search paths included in the recommendation list information, the score can be calculated in the same manner as the score of the first recommended search path, and one or more recommended search paths etc. included in the recommendation list information can be rearranged in order of score.

[0112] Here, the optimal path recommendation unit (114) can check the average CPC value of the search words, etc. included in the first recommended search path from statistical information (interest statistical information) corresponding to the first recommended search path information, and calculate it as a score.

[0113] In addition, in the above-mentioned configuration, the optimal path recommendation unit (114) acquires one or more search path information corresponding to the requested search word from the search path DB (104) as recommended candidate path information, calculates a score for each of the one or more recommended candidate path information as described above, selects one or more recommended candidate path information whose score is calculated to be equal to or greater than a preset reference value as recommended search path information, includes only the selected recommended search path information in the recommendation list information, and provides the recommendation list information to the user terminal as a final result (final result information) corresponding to the requested information.

[0114] As a result, the optimal path recommendation unit (114) can provide only search paths that are highly relevant to the requested search words and ranking conditions in the final results, and can exclude search paths that are less relevant to the requested search words and ranking conditions from the final results.

[0115] Also, as shown in FIG. 9, when the optimal path recommendation unit (114) receives the requested search word “Anbang Grill” and the ranking condition “order by cost per click (CPC)”, it can diagram and output a word network UI based on the recommendation list information acquired corresponding to the requested search word.

[0116] For example, when the first recommended search path information included in the recommendation list information acquired in response to the request information is 'grill' → 'grill' → 'grill recommendation' → 'Anbang Grill' → 'Anbang Grill 501' → 'Anbang Grill AB301MF' → 'fish grill' and the second recommended search path information included in the recommendation list information is 'oil-free grill' → 'Anbang Grill' → 'Anbang Grill drawbacks' → 'electric grill drawbacks', the optimal path recommendation unit (114) may generate and provide recommendation list information configured in a UI (User Interface) in the form of a flow chart in which each search word is represented by a box, a previous search word in 'Anbang Grill', which is a specific node corresponding to the requested search word on the path, is placed to the left of the specific node, and a subsequent search word in the specific node is placed to the right of the specific node, and different nodes (search words) are interconnected by edges according to the first and second recommended search path information.

[0117] In addition, the optimal path recommendation unit (114) can represent the height of the box by the size (or number) of the incoming edge or outgoing edge of each search word, or represent the color of the box by information of the search words on the path (search volume, advertising cost, competitive index, etc.).

[0118] Also, as shown in FIG. 10, the optimal path recommendation unit (114) can extract function type information corresponding to a search word for each of a plurality of search words included in the recommendation list information from the function type DB (103), match the search word, add it to the recommendation list information, and provide it to the user terminal. Thereby, when a UI according to the recommendation list information is displayed on the user terminal, one or more boxes corresponding to one or more search words corresponding to a specific function type selected by the user may be highlighted and displayed, or one or more boxes corresponding to the remaining search words excluding the one or more search words corresponding to the specific function type may be filtered and displayed.

[0119] As described above, the present invention applies each basic search word obtained by adding different characters to a keyword to a search engine, obtains related search words related to the basic search word, expands the search word, applies the expanded search word to a search engine with an input search word, and obtains the relevance between one or more subsequent search words obtained based on the search result searched through the search engine and the input search word as a distance according to the search intention of the input search word grasped by the search engine and the exposure rank of the subsequent search word, generates a search path by interconnecting search words having a distance based on the distance between such search words, and then provides a recommended search path list related to a requested search word requested by a user and an advertising attribute that the user values, thereby supporting the easy selection of related search words that are most related to the user's requested search word and have high advertising efficiency from among search words located on a path to reach the user's requested search word through such recommended search path list, and use them for marketing.

[0120] In addition, the present invention provides an optimal search path corresponding to a user's requested search word in the form of a UI or API, thereby enabling the user to easily obtain a list of search words suitable for advertising / marketing, search information for the search words (search volume, advertising cost, advertising competition, etc.), and function type information.

[0121] In addition, the present invention goes beyond simply providing related search words through a word network UI generated based on a search path, and by expressing the connection relationships between search words, can provide a way to grasp the main search journey of users for products and services, as well as assist users in more intuitively grasping their needs, such as what they are looking for and what they are comparing.

[0122] FIG. 11 is a flowchart of a service providing method for providing a search path by the service providing apparatus (100) according to an embodiment of the present invention.

[0123] The service providing device (100) automatically generates one or more basic search words by combining the keyword and the characters by adding a series of characters to the keyword in different ways, and can obtain one or more related search words related to the basic search word through a preset search engine (S1).

[0124] In addition, the service providing device (100) applies the related search words to the search engine as input search words, and based on the returned search result list information, extracts one or more subsequent search words from each of one or more response results corresponding to the function types of one or more response functions used by the search engine to grasp the search intent of the input search word based on the returned search result list information, and then generates relationship information on the distance between the input search word and the subsequent search word for each of one or more subsequent search words in a distance calculation method that calculates a weighted value in the connection relationship between the input search word and the subsequent search word according to the exposure rank of the subsequent search word in the response result corresponding to the subsequent search word and the function type of the response result as a distance, and stores the relationship information in a search word relationship DB (102) (S2).

[0125] In addition, the service providing device (100) may generate a directed weighted graph by setting each of a plurality of search words as a node based on a plurality of relationship information stored in the search word relationship DB (102) and setting the distance between the nodes, generate search path information for one or more search paths connecting a start node and an end node determined by a preset algorithm in the directed weighted graph, generate statistical information by averaging statistics for preset items for a plurality of search words included in the search path information, and then match the statistical information with the search path information and store it in the search path DB (104) (S3).

[0126] In addition, when the service providing device (100) receives request information including requested search words and ranking conditions from a user terminal (S4), it can generate recommendation list information by rearranging one or more search path information including the requested search words based on the ranking conditions and statistical information, and provide the recommendation list information to the user terminal (S5).

[0127] The components described in the embodiments of the present invention may be embodied using one or more general-purpose or special-purpose computers, such as hardware including a storage unit such as a memory, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a Field Programmable Gate Array (FPGA), a programmable logic unit (PLU), a microprocessor, software including a command set, or a combination thereof, or any other device capable of executing and responding to instructions.

[0128] The above contents should be modifiable and changed by a person having ordinary skill in the art to which the present invention belongs, without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are for illustration purposes, not for limiting the technical phenomena of the present invention, and the scope of the technical phenomena of the present invention is not limited by such embodiments. The scope of protection of the present invention should be interpreted according to the following claims, and all technical phenomena within the scope equivalent thereto should be interpreted as being included in the scope of the present invention. [Explanation of symbols]

[0129] 100: Service providing device 101:Keyword DB 102: Search word related DB 103: Function type DB 104: Search path DB 105: Search volume DB 110: Control unit 111: Search word extension 112: Search information extraction unit 113: Search path extraction part 114: Optimal path recommendation section 120: Communications Department 130: Preservation Department

Claims

1. a search word expansion unit that automatically generates one or more basic search words by combining the keyword and the characters by adding a series of characters to the keyword in different ways, and acquires one or more related search words related to the basic search word through a preset search engine; a search information extraction unit that uses the related search word as an input search word to search a search word storage DB in the search engine, extracts one or more subsequent search words from one or more response results in the search result list information corresponding to one or more response function types used by the search engine based on search result list information that is a response from the search engine, and calculates a weighted value in a connection relationship between the input search word and the subsequent search word, which is a value obtained by multiplying an exposure rank of the subsequent search word in the response result corresponding to the subsequent search word by a priority level preset for the function type of the response result, as a distance, and generates relationship information in the distance between the input search word and the subsequent search word for each one or more subsequent search words and stores the generated relationship information in a search word relationship DB; a search path extraction unit that generates a directed weighted graph by setting each of the input search word and the one or more subsequent search words as a node based on a plurality of relationship information stored in the search word relationship DB and setting a distance between the nodes, generates search path information for one or more search paths connecting a start node and an end node determined by a predetermined algorithm in the directed weighted graph, generates statistical information by averaging a predetermined item-by-item statistical amount obtained for each of the plurality of search words included in the search path information, matches the search path information with the statistical information, and stores the matched information in the search path DB; and an optimal path recommendation unit that, when receiving request information including a requested search word and a ranking condition from a user terminal, generates recommendation list information by rearranging one or more search path information including the requested search word based on the ranking condition and statistical information, and provides the recommendation list information to the user terminal. A service providing device for providing a search path.

2. The related search words are auto-complete search words generated by the search engine based on the basic search words. A service providing device for providing a search path according to claim 1.

3. The search information extraction unit a weighted value is calculated by multiplying a priority level preset for a type of function of the response result corresponding to the subsequent search word by an exposure rank of the subsequent search word in the response result corresponding to the subsequent search word, and the calculated weighted value is set as the distance between the subsequent search word and the input search word. A service providing device for providing a search path according to claim 1.

4. The search information extraction unit The subsequent search word is applied to the search engine as another input search word, and based on the returned search result list information, the search engine grasps the search intent of the input search word according to the distance calculation method, and extracts one or more texts from one or more response results corresponding to the function types of one or more response functions used by the search engine as additional subsequent search words corresponding to the subsequent search word, respectively. Then, a weight value in a connection relationship between the input search word and the additional subsequent search word according to an exposure rank of the additional subsequent search word in the response result corresponding to the additional subsequent search word and a function type of the response result is calculated as a distance, and relationship information in a distance between the input search word that is the subsequent search word and the one or more additional subsequent search words is generated for each of the one or more additional subsequent search words, and is stored in the search word relationship DB. A service providing device for providing a search path according to claim 1.

5. The search path extraction unit While changing the start node, one or more end nodes corresponding to the start node are identified by the algorithm, and a predetermined number K of search paths are calculated in order of shortest search path distance among search paths connecting the start node and the end node, and the search path information is generated for each of the calculated search paths. A service providing device for providing a search path according to claim 1.

6. The search path extraction unit A preset number of end nodes are determined in order of relative importance to the start node based on a PageRank algorithm. A service providing device for providing a search path according to claim 5.

7. The search path extraction unit Calculating statistical information including an average value of at least one of the statistical values ​​for a plurality of search words included in the search path information, the search volume, the advertisement unit price, and the competitive index, and then matching the statistical information with the search path information corresponding to the statistical information and storing the search path information in the search path DB. A service providing device for providing a search path according to claim 1.

8. The optimal path recommendation unit includes: When the request information is received from the user terminal, one or more search path information and one or more statistical information corresponding to a requested search word included in the request information are extracted from a search path DB, and scores are calculated for the extracted one or more statistical information according to a preset score calculation standard for one or more items according to a ranking condition included in the request information, and the extracted one or more statistical information are rearranged in order of scores. Then, one or more search path information corresponding to statistical information ranked at or above a preset criterion among the one or more search path information corresponding to the rearranged one or more statistical information is selected, and recommendation list information including the selected one or more search path information is provided to the user terminal. A service providing device for providing a search path according to claim 1.

9. The optimal path recommendation unit includes: At least one of statistical information corresponding to search path information so as to correspond to each of one or more pieces of search path information included in the recommendation list information, search volume information including item-by-item statistics for each of a plurality of search words included in the search path information, and function type information of the function types of the response results corresponding to the plurality of search words included in the search path information is included in the recommendation list information, and then the recommendation list information is provided to the user terminal in the form of a UI or an API. A service providing device for providing a search path according to claim 1.

10. A method for a service providing device to provide a search path, comprising: automatically generating one or more basic search words by combining the keyword and the characters by adding a series of characters to the keyword in different ways, and acquiring one or more related search words related to the basic search words through a preset search engine; Instructing the search engine to search a search word storage DB using the related search word as an input search word, extracting one or more subsequent search words from one or more response results in the search result list information corresponding to one or more response function types used by the search engine based on search result list information that is a response from the search engine, and then calculating a weighted value in a connection relationship between the input search word and the subsequent search word, which is a value obtained by multiplying an exposure rank of the subsequent search word in the response result corresponding to the subsequent search word by a predetermined priority of the function type of the response result, as a distance, and storing the relationship information in a search word relationship DB in the distance between the input search word and the subsequent search word for each one or more subsequent search words; generating a directed weighted graph in which each of the input search word and the one or more subsequent search words is set as a node based on a plurality of pieces of relationship information stored in the search word relationship DB and a distance between the nodes is set, generating search path information for one or more search paths connecting a start node and an end node determined by a preset algorithm in the directed weighted graph, generating statistical information by averaging a predetermined item-by-item statistical amount obtained for each of a plurality of search words included in the search path information, matching the search path information with the statistical information, and storing the matching information in the search path DB; and and generating recommendation list information by rearranging one or more search path information including the requested search word based on the ranking condition and statistical information when receiving request information including the requested search word and ranking condition from the user terminal, and providing the recommendation list information to the user terminal. How to provide a search path.

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