Information processing device, information processing method, and information processing program

Abstract

To create a model with machine learning from a database of a search advertisement to predict a recommended word.SOLUTION: An information processor according to the present application comprises: an analysis part that performs morphological analysis of a text of an advertisement in a unit of a word or a phrase; an extraction part that extracts CTR or CVR in a unit of a word or a phrase; and an estimation part that estimates the advertising efficiency for each word or phrase based on the extracted CTR or CVR. The information processor further comprises a provisioning part that suggests improvement of a TD of the advertisement based on the prediction of the CTR or CVR for each word or phrase.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] A technology is disclosed that can appropriately select advertisements that are judged to have a high user click rate in accordance with the content of the advertisements. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-037070 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the above-mentioned conventional technology, articles are grouped according to the content of the articles, such as the similarity in the appearance trends of words that appear in the articles, and for each article cluster, advertising information consisting of advertisements previously assigned to the articles and advertisement profitability information is stored in association with each other, and for a specified article, keywords are selected from advertisements related to the identified article cluster and words that appear in the article, advertisements related to the selected keywords are acquired, and for the article cluster of the specified article identified by the article cluster identification means, a recommended advertisement is selected from the acquired advertisements based on the advertisement profitability information stored in the article advertising database so that the more profitable the advertisement, the higher the selection probability. If the advertising effectiveness could be evaluated not only for the advertisement itself but also for each keyword included in the advertisement, it is expected that the effectiveness of advertising delivery could be further improved.

[0005] The present application has been made in view of the above, and aims to create a model using machine learning from a database of search advertisements and predict recommended words. [Means for solving the problem]

[0006] The information processing device according to the present application comprises: A classification unit classifies delivered advertisements by category and stores them in a database; an estimation unit estimates the advertising effectiveness of the delivered advertisements for each category based on the CTR or CVR of the advertisement; a reception unit receives from an advertiser a keyword indicating a specific category; an extraction unit extracts from the database advertisements in a category corresponding to the keyword specified by the advertiser; advertisement The title and description strings contained in Morphological analysis and extract words or phrases Analysis department an assigning unit that assigns a score to a word or phrase included in an advertisement according to the CTR or CVR of the advertisement including the extracted word or phrase; and a providing unit that provides the advertiser with information according to the words or phrases included in advertisements of a specified category and the scores of each of the words or phrases individually or in combination. The present invention is characterized by comprising: [Effects of the Invention]

[0007] According to one aspect of the embodiment, a model can be created using machine learning from a database of search advertisements to predict recommended words. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram showing an overview of the morphological analysis and prediction results. [Figure 3] FIG. 3 is an explanatory diagram showing an overview of the advertising copy creation support. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of a server device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of the user information database. [Figure 8] FIG. 8 is a diagram illustrating an example of the history information database. [Figure 9] FIG. 9 is a diagram illustrating an example of the advertisement information database. [Figure 10] FIG. 10 is a flowchart showing a processing procedure according to the embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of a hardware configuration. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an information processing device, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to these embodiments. Furthermore, the same components in the following embodiments will be denoted by the same reference numerals, and duplicated descriptions will be omitted.

[0010] [1. Overview of information processing method] First, an overview of an information processing method performed by an information processing device according to an embodiment will be described with reference to Fig. 1. Fig. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. Note that Fig. 1 explains an example in which a model is created by machine learning from a database of search advertisements and recommended words are predicted.

[0011] 1, the information processing system 1 includes a terminal device 10 and a server device 100. The terminal device 10 and the server device 100 are connected to each other via a network N (see FIG. 4) in a wired or wireless manner so as to be able to communicate with each other. In this embodiment, the terminal device 10 cooperates with the server device 100.

[0012] The terminal device 10 is a smart device such as a smartphone or tablet terminal used by a user U, and is a portable terminal device capable of communicating with any server device via a wireless communication network such as 4G (Generation (4G)) or LTE (Long Term Evolution) networks. The terminal device 10 has a screen such as a liquid crystal display with a touch panel function, and accepts various operations on displayed data such as content, such as tapping, sliding, and scrolling, performed by the user U with a finger or a stylus. An operation performed on an area of ​​the screen where content is displayed may be considered an operation on the content. The terminal device 10 may be not only a smart device, but also an information processing device such as a desktop PC (Personal Computer) or a notebook PC.

[0013] The server device 100 is an information processing device that works in conjunction with the terminal device 10 of each user U and provides API (Application Programming Interface) services for various applications (hereinafter referred to as apps) and various data to the terminal device 10 of each user U, and is realized by a computer, a cloud system, etc.

[0014] The server device 100 may also be an information processing device that provides some kind of online web service to the terminal device 10 of each user U. For example, the server device 100 may provide the following web services: internet connection, search service, social networking service (SNS), electronic commerce (EC), electronic payment, online games, online banking, online trading, hotel and ticket reservations, video and music distribution, news, maps, route search, route guidance, line information, operation information, and weather forecast. In practice, the server device 100 may cooperate with various servers that provide the above-mentioned web services and act as an intermediary for the web services or may be responsible for processing the web services.

[0015] The server device 100 can acquire user information about the user U. For example, the server device 100 acquires information about the attributes of the user U, such as the gender, age, and residential area of ​​the user U. The server device 100 then stores and manages the information about the attributes of the user U together with identification information (such as a user ID) that identifies the user U.

[0016] The server device 100 also acquires various types of history information (log data) indicating the behavior of the user U from the terminal device 10 of the user U or from various servers based on the user ID, etc. For example, the server device 100 acquires a location history, which is a history of the user U's location and date and time, from the terminal device 10. The server device 100 also acquires a search history, which is a history of search queries entered by the user U, from a search server (search engine). The server device 100 also acquires a browsing history, which is a history of content viewed by the user U, from a content server. The server device 100 also acquires a purchase history (payment history), which is a history of the user U's product purchases and payment processes, from an e-commerce server or a payment processing server. The server device 100 may also acquire a listing history and a sales history, which are a history of the user U's listings on the marketplace, from the e-commerce server or the payment processing server. The server device 100 also acquires a posting history, which is a history of the user U's posts, from a posting server or SNS server that provides a word-of-mouth posting service. The various servers and the like described above may be the server device 100 itself. That is, the server device 100 may function as the various servers and the like described above.

[0017] [1-1. Advertising copy writing support] In this embodiment, as shown in Fig. 1, the server device 100 creates a model using machine learning from a database of search advertisements and predicts recommended words. Specifically, as shown in Fig. 2, the server device 100 performs morphological analysis on each word of the search advertisement, extracts a CTR (Click Through Rate) or a CVR (Conversion Rate), and predicts efficient words / phrases using machine learning. Fig. 2 is an explanatory diagram showing an overview of the morphological analysis and prediction results.

[0018] An overview of the advertising copy creation support will be described with reference to Fig. 3. Fig. 3 is an explanatory diagram showing an overview of the advertising copy creation support.

[0019] For example, as shown in Fig. 3, the server device 100 classifies distributed advertisements by category and stores them in a database (step S1). Note that the advertisements may be product advertisements, brand advertisements, corporate advertisements, CSR (Corporate Social Responsibility) advertisements, etc. The categories of advertisements may be classified according to the content of the advertisements, the type of advertisement, the purpose of the advertisements, or the advertising medium.

[0020] For example, known types of advertising include web / internet advertising, human advertising, mass media advertising, and sales promotion advertising (SP). Types of web / internet advertising include email advertising, display advertising (banner advertising), retargeting advertising, listing advertising (search-linked advertising), pop-up advertising, native advertising, SNS advertising, and video advertising. Types of human advertising include affiliate advertising, influencer advertising, and live streaming advertising. Types of mass media advertising include television advertising (CM), newspaper advertising, magazine advertising, and radio advertising. Types of sales promotion advertising (SP) include flyer advertising, transit advertising, outdoor advertising / OOH (OUT OF HOME) advertising, DM (direct mail) advertising, enclosed / bundled advertising, and event promotions.

[0021] Next, the server device 100 estimates the advertising effectiveness of the delivered advertisements for each category (step S2). For example, the server device 100 estimates that the higher the CTR or CVR of the advertisement, the higher the advertising effectiveness. Note that, in practice, it is not limited to CTR or CVR, but may be CTVR. CTVR is the product of the click-through rate (CTR) and the conversion rate (CVR) of the advertisement (CTVR=CTR×CVR). In other words, it is not limited to CTR or CVR, but may be any index for evaluating the advertisement, clicks, etc. In this embodiment, the explanation will be given taking CTR or CVR as an example.

[0022] Next, the server device 100 receives, via the network N (see FIG. 4), a keyword (KW) indicating a specific category from a user U who is an advertiser among an unspecified number of users U (step S3). For example, the server device 100 receives a keyword (KW) indicating a specific category input by the user U who is an advertiser from the terminal device 10 of the user U. The keyword may be a search query.

[0023] For example, the server device 100 may accept, as an advertisement category, keywords relating to the advertisement content, the advertisement type, the advertisement purpose, and the advertisement medium.

[0024] In practice, the server device 100 may allow the user to select an arbitrary keyword from a list of categories rather than directly inputting a keyword. In this case, the selected keyword becomes the specified keyword.

[0025] Next, the server device 100 extracts advertisements in the category corresponding to the specified keyword from the database (step S4).

[0026] Next, the server device 100 performs morphological analysis on the character strings (title, description, etc.) included in the extracted advertisement using natural language processing (NLP) to extract words / phrases (step S5). At this time, the server device 100 performs morphological analysis on the advertisement text on a word or phrase basis. In practice, the server device 100 is not limited to morphological analysis, and may also perform syntactic analysis, semantic analysis, and contextual analysis.

[0027] Next, the server device 100 assigns a score to the word / phrase included in the advertisement according to the CTR (Click Through Rate) or CVR (Conversion Rate) of the advertisement including the extracted word / phrase (step S6). At this time, the server device 100 extracts the CTR or CVR for each word or phrase. For example, the higher the CTR or CVR of the advertisement including the extracted word / phrase, the higher the score the server device 100 assigns to the word / phrase. Note that the score may be based on the CTR or CVR. The score based on the CTR or CVR is not necessarily expressed as a percentage (%).

[0028] In this embodiment, the server device 100 vectorizes character strings included in advertisements using DOC2VEC and predicts CTR or CVR from the vectors (document vectors). Doc2Vec is a technology that converts sentences of any length into vectors of a fixed length. This allows the server device 100 to predict CTR or CVR for each word / phrase.

[0029] At this time, the server device 100 calculates a score for each word / phrase and / or combination thereof for each category. The server device 100 may also use the aggregate (sum) value, average value, or median value of the scores calculated for each word / phrase as the score for that word / phrase. Note that if the same word / phrase is classified into different categories, the server device 100 calculates the score for the word / phrase separately for each category. In other words, even if the same word / phrase is in different categories, it is treated as a different word / phrase and scores are calculated separately.

[0030] Furthermore, the server device 100 generates a model for calculating the score by machine learning. For example, the server device 100 performs morphological analysis on character strings included in advertisements for a category, extracts words / phrases, and trains the model so that, when the extracted words / phrases are input, the higher the CTR or CVR of an advertisement including the words / phrases, the higher the score output. The server device 100 then inputs the specified search query (keywords indicating a specific category) and the words / phrases included in the corresponding advertisements into the model to calculate the score.

[0031] The machine learning method may be deep learning, a recurrent neural network (RNN), a long short-term memory (LSTM), etc. Note that these are merely examples and are not intended to be limiting.

[0032] Next, the server device 100 provides, via the network N (see FIG. 4), to the terminal device 10 of the user U who specified the category, information according to the words / phrases included in the advertisements of the specified category and the scores of each of these words / phrases individually or in combination (step S7). Furthermore, the server device 100 may provide information regarding the estimated advertising effectiveness.

[0033] At this time, the server device 100 estimates the advertising efficiency of each word or phrase using machine learning based on the CTR or CVR extracted for each word or phrase, and predicts words or phrases with high advertising efficiency. In other words, words or phrases with high advertising efficiency are words or phrases with high CTR or CVR. For example, the server device 100 predicts the CTR or CVR for each word or phrase using machine learning and sorts them in descending order of CTR or CVR. Furthermore, the server device 100 suggests optimization or improvement of the TD (title and description: the title and description of the advertising copy) of the advertisement based on the predicted CTR or CVR for each word or phrase. Furthermore, the server device 100 may present the words or phrases in a ranking format based on the score for each word or phrase. Furthermore, as shown in FIG. 2, the server device 100 may extract or calculate a coefficient (correction coefficient) for excluding from learning the ranking (advertising amount) of factors that increase CTR or CVR. The correction coefficient varies depending on the input keywords and TD. In practice, the server device 100 may estimate the advertising efficiency based on the entire advertising copy and the CTR or CVR of the advertisement, not only on / in addition to the word or phrase unit.

[0034] In this embodiment, when an advertiser inputs a search query when submitting an advertisement, the server device 100 outputs effective words / phrases in order of CTR or CVR in order of the degree of advertising effectiveness. For example, when the search query is "refrigerator," the server device 100 outputs words / phrases such as "quick freezing" and "large freezer" in order of the degree of advertising effectiveness.

[0035] The server device 100 also estimates the advertising effectiveness for each keyword (KW) based on the distribution history of advertisements corresponding to the input search query. The server device 100 also provides an estimated result of the group corresponding to the search query. The server device 100 also evaluates the advertising effectiveness for each keyword over the past week.

[0036] Note that the server device 100 may score not only words / phrases but also the advertisement copy itself. Furthermore, the server device 100 may automatically change words / phrases included in the advertisement so that they have a higher score, based on the score for each word / phrase. For example, the server device 100 may automatically change words / phrases included in the advertisement to words / phrases with a higher score.

[0037] The server device 100 may also score the advertising copy based on its likelihood of being an advertising copy (the appropriateness of the advertising copy). For example, the server device 100 may calculate a score based on the advertising writing style. The server device 100 may also score the advertising copy based on its length.

[0038] The server device 100 may also generate and provide rankings for each trend, such as image trends, character decoration trends, etc. The server device 100 may also provide appropriate combinations of words / phrases.

[0039] As described above, in this embodiment, the server device 100 estimates the advertising effectiveness for each keyword from the distribution history of advertisements corresponding to the input search query, and provides information indicating the estimated advertising effectiveness.

[0040] In addition, the server device 100 uses, as a scoring model, a model that has been trained so that when a word / phrase in an advertisement copy is input, the higher the CTR or CVR of an advertisement containing that word / phrase, the higher the score output.

[0041] As a result, in this embodiment, it is possible to predict the CTR or CVR for each word / phrase in the advertising copy, which can be used to optimize and improve the TD of the advertisement.

[0042] [2. Example of information processing system configuration] Next, a configuration of an information processing system 1 including a server device 100 according to an embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration of the information processing system 1 according to an embodiment. As shown in Fig. 4, the information processing system 1 according to an embodiment includes a terminal device 10 and a server device 100. These various devices are connected to each other via a network N so as to be able to communicate with each other via a wired or wireless connection. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0043] Furthermore, the number of devices included in the information processing system 1 shown in Fig. 4 is not limited to that shown in the figure. For example, in Fig. 4, for the sake of simplicity, only one terminal device 10 is shown, but this is merely an example and is not limiting, and two or more devices may be included.

[0044] The terminal device 10 is an information processing device used by a user U. For example, the terminal device 10 may be a smart device such as a smartphone or tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game console or AV device with a communication function, a car navigation system, a wearable device such as a smart watch or a head-mounted display, smart glasses, etc. The terminal device 10 may also be a house / building, a car, a home appliance, an electronic device, etc. that is compatible with the Internet of Things (IOT).

[0045] In addition, the terminal device 10 can connect to the network N via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: 5th generation mobile communication system), or via short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local Area Network), and communicate with the server device 100.

[0046] The server device 100 is, for example, a computer such as a PC or a blade server, or a mainframe or a workstation, etc. The server device 100 may be realized by cloud computing.

[0047] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of the configuration of the terminal device 10. As shown in Fig. 5, the terminal device 10 includes a communication unit 11, a display unit 12, an input unit 13, a positioning unit 14, a sensor unit 20, a control unit 30 (controller), and a storage unit 40.

[0048] (Communications Department 11) The communication unit 11 is connected to a network N (see FIG. 4) by wire or wirelessly, and transmits and receives information to and from the server device 100 via the network N. For example, the communication unit 11 is realized by a NIC (Network Interface Card), an antenna, etc.

[0049] (Display section 12) Display unit 12 is a display device that displays various information such as position information. For example, display unit 12 is a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). Display unit 12 is also a touch panel display, but is not limited to this.

[0050] (Input section 13) The input unit 13 is an input device that accepts various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, etc. The input unit 13 may be an input / output port (I / O port), a USB (Universal Serial Bus) port, etc. If the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13. The input unit 13 may be a microphone that accepts voice input from the user U. The microphone may be wireless.

[0051] (Positioning unit 14) The positioning unit 14 receives signals (radio waves) transmitted from satellites of a GPS (Global Positioning System), and acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10, which is the device itself, based on the received signals. That is, the positioning unit 14 positions the position of the terminal device 10. Note that GPS is merely an example of a GNSS (Global Navigation Satellite System).

[0052] The positioning unit 14 can also measure the position using various methods other than GPS. For example, the positioning unit 14 may measure the position by using various communication functions of the terminal device 10 as an auxiliary positioning means for position correction, etc., as described below.

[0053] (Wi-Fi positioning) For example, the positioning unit 14 uses a Wi-Fi (registered trademark) communication function of the terminal device 10 or a communication network provided by each communication company to measure the position of the terminal device 10. Specifically, the positioning unit 14 performs Wi-Fi communication or the like and measures the distance to a nearby base station or access point, thereby measuring the position of the terminal device 10.

[0054] (Beacon positioning) The positioning unit 14 may also measure the position by using a Bluetooth (registered trademark) function of the terminal device 10. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting to a beacon transmitter connected by the Bluetooth (registered trademark) function.

[0055] (geomagnetic positioning) The positioning unit 14 also measures the position of the terminal device 10 based on a geomagnetic pattern of a structure that has been measured in advance and a geomagnetic sensor that the terminal device 10 has.

[0056] (RFID positioning) Furthermore, for example, if the terminal device 10 has a function of an RFID (Radio Frequency Identification) tag equivalent to a contactless IC card used at station ticket gates, in stores, etc., or has a function of reading an RFID tag, the location where the terminal device 10 was used is recorded together with information on the payment or the like made by the terminal device 10. The positioning unit 14 may obtain such information to determine the location of the terminal device 10. Alternatively, the location may be determined by an optical sensor, an infrared sensor, or the like provided in the terminal device 10.

[0057] The positioning unit 14 may measure the position of the terminal device 10 using one or a combination of the above-mentioned positioning means, as needed.

[0058] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be wired or wireless. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices or wireless devices. In the example shown in FIG. 5, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, a barometric pressure sensor 23, a temperature sensor 24, a sound sensor 25, a light sensor 26, a magnetic sensor 27, and an image sensor (camera) 28.

[0059] The above-described sensors 21 to 28 are merely examples and are not intended to be limiting. That is, the sensor unit 20 may be configured to include some of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28.

[0060] The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects physical movements of the terminal device 10, such as the direction of movement, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects physical movements of the terminal device 10, such as tilt in three axial directions, based on the angular velocity of the terminal device 10. The air pressure sensor 23 detects, for example, the air pressure around the terminal device 10.

[0061] Since the terminal device 10 includes the acceleration sensor 21, the gyro sensor 22, the atmospheric pressure sensor 23, etc., it is possible to measure the position of the terminal device 10 using a technique such as Pedestrian Dead-Reckoning (PDR) that uses these sensors 21 to 23. This makes it possible to obtain indoor position information that is difficult to obtain using a positioning system such as GPS.

[0062] For example, the number of steps, walking speed, and distance walked can be calculated using a pedometer that uses the acceleration sensor 21. In addition, the direction of travel, line of sight, and body tilt of the user U can be determined using the gyro sensor 22. In addition, the altitude and floor on which the terminal device 10 of the user U is located can be determined from the air pressure detected by the air pressure sensor 23.

[0063] The temperature sensor 24 detects, for example, the temperature around the terminal device 10. The sound sensor 25 detects, for example, the sound around the terminal device 10. The light sensor 26 detects the illuminance around the terminal device 10. The magnetic sensor 27 detects, for example, the geomagnetism around the terminal device 10. The image sensor 28 captures an image around the terminal device 10.

[0064] The above-mentioned air pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the air pressure, temperature, sound, and illuminance, respectively, and capture images of the surroundings, thereby detecting the environment and situation around the terminal device 10. Furthermore, the accuracy of the location information of the terminal device 10 can be improved based on the environment and situation around the terminal device 10.

[0065] (control unit 30) The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), ROM (Read Only Memory), RAM, input / output ports, etc., and various other circuits. The control unit 30 may also be configured with hardware such as an integrated circuit, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 includes a transmitting unit 31, a receiving unit 32, and a processing unit 33.

[0066] (Transmitter 31) The transmission unit 31 can transmit, for example, various information input by the user U using the input unit 13, various information detected by each sensor 21 to 28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 measured by the positioning unit 14 to the server device 100 via the communication unit 11.

[0067] (Receiver 32) The receiving unit 32 can receive various types of information provided by the server device 100 and requests for various types of information from the server device 100 via the communication unit 11.

[0068] (Processing unit 33) The processing unit 33 controls the entire terminal device 10, including the display unit 12. For example, the processing unit 33 can output various information transmitted by the transmitting unit 31 and various information received from the server device 100 by the receiving unit 32 to the display unit 12 for display.

[0069] (Storage unit 40) The storage unit 40 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), an optical disk, etc. The storage unit 40 stores various programs, various data, etc.

[0070] [4. Server device configuration example] Next, the configuration of the server device 100 according to the embodiment will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of the configuration of the server device 100 according to the embodiment. As shown in Fig. 6, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0071] (Communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is also connected to a network N (see FIG. 4) by wire or wirelessly.

[0072] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as an HDD, an SSD, an optical disk, etc. As shown in Fig. 6, the storage unit 120 has a user information database 121, a history information database 122, and an advertisement information database 123.

[0073] (User Information Database 121) The user information database 121 stores user information about the user U. For example, the user information database 121 stores various information such as the attributes of the user U. FIG. 7 is a diagram showing an example of the user information database 121. In the example shown in FIG. 7, the user information database 121 has items such as "User ID (Identifier)," "Age," "Gender," "Home," "Workplace," and "Interests."

[0074] The "user ID" indicates identification information for identifying the user U. The "user ID" may be the contact information of the user U (telephone number, email address, etc.), or may be identification information for identifying the terminal device 10 of the user U.

[0075] Furthermore, "age" indicates the age of user U identified by the user ID. Note that "age" may be information indicating the specific age of user U (e.g., 35 years old), or may be information indicating the generation of user U (e.g., 30s). Alternatively, "age" may be information indicating the date of birth of user U, or may be information indicating the generation of user U (e.g., born in the 1980s). Furthermore, "gender" indicates the gender of user U identified by the user ID.

[0076] Furthermore, "home" indicates the location information of the home of user U identified by the user ID. In the example shown in FIG. 7, "home" is illustrated as an abstract code such as "LC11," but it may also be latitude and longitude information, etc. Furthermore, for example, "home" may also be the name of an area or an address.

[0077] Furthermore, "workplace" indicates location information of the workplace (school in the case of a student) of user U identified by the user ID. In the example shown in FIG. 7, "workplace" is illustrated as an abstract code such as "LC12," but it may also be latitude and longitude information, etc. Furthermore, for example, "workplace" may also be the name of a region or an address.

[0078] Furthermore, "interests" indicate the interests of user U identified by the user ID. In other words, "interests" indicate subjects in which user U identified by the user ID is highly interested. For example, "interests" may be search queries (keywords) entered by user U into a search engine. In the example shown in FIG. 7, each user U is shown with one "interest," but there may be multiple "interests."

[0079] For example, in the example shown in FIG. 7, the age of user U identified by user ID "U1" is "20s" and the gender is "male." Furthermore, for example, the home address of user U identified by user ID "U1" is "LC11." Furthermore, for example, the workplace of user U identified by user ID "U1" is "LC12." Furthermore, for example, the user U identified by user ID "U1" is interested in "sports."

[0080] 7, abstract values ​​such as "U1", "LC11", and "LC12" are used for illustration, but "U1", "LC11", and "LC12" are assumed to store information such as specific character strings and numerical values. Below, abstract values ​​may also be illustrated in diagrams relating to other information.

[0081] The user information database 121 may store various types of information depending on the purpose, without being limited to the above. For example, the user information database 121 may store various types of information related to the terminal device 10 of the user U. The user information database 121 may also store information related to the user U's attributes, such as demographic attributes, psychographic attributes, geographic attributes, and behavioral attributes. For example, the user information database 121 may store information such as name, family structure, hometown (hometown), occupation, job title, income, qualifications, type of residence (detached house, apartment, etc.), whether or not the user has a car, commuting time, commuting route, commuter pass area (station, line, etc.), frequently used stations (other than the station nearest to home or workplace), extracurricular activities (location, time zone, etc.), hobbies, interests, lifestyle, etc.

[0082] (History Information Database 122) The history information database 122 stores various information related to history information (log data) that indicates the behavior of the user U. Fig. 8 is a diagram showing an example of the history information database 122. In the example shown in Fig. 8, the history information database 122 has items such as "user ID," "location history," "search history," "browsing history," "purchase history," and "posting history."

[0083] "User ID" indicates identification information for identifying user U. "Location history" indicates the location history, which is the history of user U's location and movements. "Search history" indicates the search history, which is the history of search queries entered by user U. "Browsing history" indicates the browsing history, which is the history of content viewed by user U. "Purchase history" indicates the purchase history, which is the history of purchases made by user U. "Posting history" indicates the posting history, which is the history of posts made by user U. "Posting history" may also include questions about user U's possessions.

[0084] For example, in the example shown in Figure 8, user U, identified by user ID "U1," moved as shown in "Location History #1," searched as shown in "Search History #1," viewed content as shown in "Viewing History #1," purchased specific products at specific stores as shown in "Purchase History #1," and posted as shown in "Posting History #1."

[0085] Here, in the example shown in Figure 8, abstract values ​​such as "U1", "Location History #1", "Search History #1", "Browsing History #1", "Purchase History #1", and "Post History #1" are used for the illustration, but "U1", "Location History #1", "Search History #1", "Browsing History #1", "Purchase History #1", and "Post History #1" are assumed to store specific information such as character strings and numbers.

[0086] The history information database 122 is not limited to the above and may store various types of information depending on the purpose. For example, the history information database 122 may store the user U's usage history of a predetermined service. The history information database 122 may also store the user U's store visit history or facility visit history. The history information database 122 may also store the user U's payment history (electronic payment) using the terminal device 10.

[0087] (Advertising Information Database 123) The advertisement information database 123 stores various information related to history information (log data) that indicates the behavior of the user U. Fig. 9 is a diagram showing an example of the advertisement information database 123. In the example shown in Fig. 9, the advertisement information database 123 has items such as "advertisement," "category," "word / phrase," "CTR," "CVR," and "CTVR."

[0088] "Advertisement" indicates identification information for identifying an advertisement. "Category" indicates the category of the advertisement. Advertisements may be categorized by the content of the advertisement, the type of advertisement, the purpose of the advertisement, or the advertising medium. Advertisements are not limited to one category, and one advertisement may indicate multiple categories.

[0089] "Words / phrases" refer to words / phrases extracted by natural language processing through morphological analysis of character strings (titles, descriptions, etc.) included in advertisements. In practice, they may be vectors (document vectors) obtained by vectorizing words / phrases.

[0090] Furthermore, "CTR" indicates the CTR (Click Through Rate) for each word / phrase. However, in reality, it may be a score based on the CTR. Furthermore, "CVR" indicates the CVR (Conversion Rate) for each word / phrase. However, in reality, it may be a score based on the CVR. Furthermore, "CTVR" indicates the CTVR for each word / phrase. CTVR is the product of the click-through rate (CTR) of the advertisement and the conversion rate (CVR) (CTVR = CTR x CVR). However, in reality, it may be a score based on the CTVR.

[0091] For example, in the example shown in Figure 9, user U identified by advertisement "Advertisement A" is classified into category "Category #A", and the CTR for each word / phrase of "word / phrase #A1" included in the advertisement is "CTR #A1", the CVR for each word / phrase is "CVR #A1", and the CTVR for each word / phrase is "CTVR #A1".

[0092] Here, in the example shown in Figure 9, abstract values ​​such as "Advertisement A," "Category #A," "Word / Phrase #A1," "CTR #A1," "CVR #A1," and "CTVR #A1" are used for the illustration, but "Advertisement A," "Category #A," "Word / Phrase #A1," "CTR #A1," "CVR #A1," and "CTVR #A1" are assumed to store specific information such as character strings and numbers.

[0093] The advertisement information database 123 may store various types of information depending on the purpose, without being limited to the above. For example, the advertisement information database 123 may store information, suggestions, etc. regarding the advertising effectiveness of each word / phrase. The advertisement information database 123 may also store the CTR, CVR, etc. for each word / phrase, separated by the attributes of the clicked user (which may be a user segment or user persona). The advertisement information database 123 may also store information regarding the ranking of words / phrases.

[0094] (control unit 130) 6, the explanation will be continued. The control unit 130 is a controller, and is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like, executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the server device 100 using a storage area such as a RAM as a working area. In the example shown in FIG. 6, the control unit 130 has an acquisition unit 131, a classification unit 132, a reception unit 133, an analysis unit 134, an extraction unit 135, an estimation unit 136, and a provision unit 137.

[0095] (Acquisition part 131) The acquisition unit 131 acquires a search query input by the user U. For example, when the user U inputs a search query into a search engine or the like to perform a keyword search, the acquisition unit 131 acquires the search query via the communication unit 110. That is, the acquisition unit 131 acquires, via the communication unit 110, the keywords input by the user U into the search box of a search engine, website, or app.

[0096] Furthermore, the acquisition unit 131 acquires user information about the user U via the communication unit 110. For example, the acquisition unit 131 acquires identification information (such as a user ID) indicating the user U, location information of the user U, attribute information of the user U, etc. from the terminal device 10 of the user U. Furthermore, the acquisition unit 131 may acquire the identification information indicating the user U, attribute information of the user U, etc. when registering the user U. Then, the acquisition unit 131 registers the user information in the user information database 121 of the storage unit 120.

[0097] Furthermore, the acquisition unit 131 acquires various types of history information (log data) indicating the behavior of the user U via the communication unit 110. For example, the acquisition unit 131 acquires various types of history information indicating the behavior of the user U from the terminal device 10 of the user U or from various servers based on the user ID or the like. Then, the acquisition unit 131 registers the various types of history information in the history information database 122 of the storage unit 120.

[0098] (Classification section 132) The classification unit 132 classifies advertisements by category and stores the classified advertisements in a database. The database may be stored in the storage unit 120, or may be stored in an external storage device, an external online server, or the like.

[0099] (Reception Department 133) The receiving unit 133 receives a designation of an advertisement category. For example, the receiving unit 133 receives, via the communication unit 110, a designation of a keyword (KW) indicating a specific category from a user U who is an advertiser among an unspecified number of users U (users). The keyword may be a search query. Furthermore, the receiving unit 133 may not directly input a keyword, but may allow the user to select an arbitrary keyword from a list of categories. Note that the receiving unit 133 may be the acquisition unit 131 or a part thereof.

[0100] (Analysis Department 134) The analysis unit 134 performs morphological analysis on the text of the advertisement on a word or phrase basis. For example, the analysis unit 134 extracts advertisements of a specified category from a database and performs morphological analysis on the text of the extracted advertisement on a word or phrase basis. At this time, the analysis unit 134 vectorizes the text of the advertisement using DOC2VEC.

[0101] (Extraction part 135) The extraction unit 135 extracts the CTR or CVR for each word or phrase. For example, the extraction unit 135 assigns a higher score to a word or phrase included in an advertisement as the CTR or CVR of the advertisement increases. The provision unit 137 provides information based on the score for each word or phrase.

[0102] At this time, the extraction unit 135 predicts the CTR or CVR from the vector (document vector) of the text of the advertisement. For example, the extraction unit 135 generates a model by machine learning that outputs a higher score to a word or phrase included in the advertisement as the CTR or CVR of the advertisement increases, and calculates the score by inputting the word or phrase included in the advertisement of the specified category into the model. In other words, the extraction unit 135 may be a calculation unit that calculates the score.

[0103] (Estimation part 136) The estimation unit 136 estimates the advertising efficiency for each word or phrase using machine learning based on the extracted CTR or CVR, and predicts words or phrases with high advertising efficiency. For example, the estimation unit 136 uses machine learning to generate an estimation model that outputs a CTR or CVR when a word or phrase is input, using pairs of words or phrases and their CTRs or CVRs as learning data.

[0104] (Providing Department 137) The providing unit 137 proposes improvement of the TD of the advertisement based on the predicted CTR or CVR for each word or phrase. For example, the providing unit 137 proposes improvement of the TD of the advertisement in a specified category based on the predicted CTR or CVR for each word or phrase. Note that the improvement may be optimization.

[0105] Furthermore, the providing unit 137 presents the words or phrases in a ranking format based on the score for each word or phrase.

[0106] Alternatively, the providing unit 137 automatically changes (modifies) the TD of the advertisement based on the predicted CTR or CVR for each word or phrase.

[0107] [5. Processing Procedure] Next, a processing procedure by the server device 100 according to the embodiment will be described with reference to Fig. 10. Fig. 10 is a flowchart showing the processing procedure according to the embodiment. Note that the processing procedure shown below is repeatedly executed by the control unit 130 of the server device 100.

[0108] For example, as shown in FIG. 10, the acquisition unit 131 of the server device 100 collects access logs of distributed advertisements directly or indirectly (via another server device, etc.) via the communication unit 110 (step S101).

[0109] Next, the classification unit 132 of the server device 100 classifies the advertisements by category and stores them in a database (step S102).

[0110] Next, the receiving unit 133 of the server device 100 receives, via the communication unit 110, a specification of a keyword (KW) indicating a specific category from the user U who is the advertiser (step S103).

[0111] Next, the analysis unit 134 of the server device 100 extracts advertisements of the specified category from the database, and performs morphological analysis on the text of the extracted advertisements on a word or phrase basis (step S104).

[0112] Next, the extraction unit 135 of the server device 100 extracts the CTR or CVR for each word or phrase (step S105).

[0113] Next, the estimation unit 136 of the server device 100 performs machine learning to estimate the The CTR or CVR is predicted (Step S106). That is, the estimation unit 136 estimates the advertising efficiency for each word or phrase.

[0114] Next, the providing unit 137 of the server device 100 presents the words or phrases in a ranking format based on the score for each word or phrase (step S107).

[0115] Next, the providing unit 137 of the server device 100 proposes an improvement to the TD of the advertisement based on the predicted CTR or CVR for each word or phrase (step S108). Note that the providing unit 137 may automatically change (modify) the TD of the advertisement based on the predicted CTR or CVR for each word or phrase.

[0116] [6. Modifications] The terminal device 10 and the server device 100 described above may be implemented in various different forms other than the above embodiment. Therefore, modifications of the embodiment will be described below.

[0117] In the above embodiment, some or all of the processing executed by the server device 100 may actually be executed by the terminal device 10. For example, the processing may be completed in a stand-alone manner (by the terminal device 10 alone). In this case, the terminal device 10 is assumed to have the functions of the server device 100 in the above embodiment. Furthermore, in the above embodiment, the terminal device 10 is linked to the server device 100, and therefore, from the perspective of the user U, it appears that the processing of the server device 100 is also being executed by the terminal device 10. In other words, from another perspective, the terminal device 10 can also be said to be equipped with the server device 100.

[0118] Furthermore, in the above embodiment, the server device 100 may classify the CTR or CVR for each word or phrase according to the attributes, history, etc. of the user U. For example, the server device 100 may store the CTR, CVR, etc. for each word / phrase by the attributes of the user who clicked (which may be a user segment or user persona). In addition to attributes such as gender, age group, and region, the CTR and CVR for each word / phrase may also vary depending on the context, such as the location (home, station / train, in-store, etc.) and situation / behavior (weekday / holiday, time of day, during commute / before bedtime, when purchasing a specific product, etc.) when the user clicked on the advertisement. In other words, the server device 100 may use the attributes, history, etc. of the target user as one of the categories of advertisements.

[0119] Furthermore, in the above embodiment, the server device 100 may classify the CTR or CVR for each word or phrase depending on the advertiser / advertiser (provider) of the advertisement. The advertising effectiveness of a word / phrase may differ depending on who is placing the advertisement, such as the image of the advertiser, and the like, which may result in different CTRs and CVRs for each word / phrase. In other words, the server device 100 may treat the advertiser / advertiser (provider) as one of the categories of advertisements.

[0120] Furthermore, in the above-described embodiments, CTR or CVR is merely an example. The server device 100 may use any of Click (number of clicks), Imp (number of impressions), CTR (click-through rate), CPC (Cost Per Click: average cost per click), etc. as an index related to clicks. The server device 100 may use any of CV (number of conversions), CVR (conversion rate), Cost (advertising cost), CPA (Cost Per Action), etc. as an index related to conversions. Note that CPA = Cost (advertising cost) ÷ CV (number of conversions).

[0121] In the above embodiment, the server device 100 may evaluate the consistency or gap between the content of the advertisement copy (TD) and the content of the landing page (LP) clicked on. For example, the server device 100 may compare words / phrases included in the advertisement copy with words / phrases included in the landing page.

[0122] Furthermore, in the above-described embodiment, the words or phrases may be fixed phrases or idioms, parts of lyrics or poetry, or famous lines or famous sayings. Furthermore, the words or phrases are not limited to text data, but may also be character strings contained in images (still images, videos, etc.) or audio. For example, the server device 100 may extract words or phrases contained in images or audio using techniques such as image recognition or audio recognition. Furthermore, the server device 100 may extract CTR or CVR not only on a word or phrase basis, but also on a sentence or clause basis. Furthermore, the server device 100 may extract CTR or CVR for each template of advertising text.

[0123] [7. Effects] As described above, the information processing device (terminal device 10 and server device 100) according to the present application includes an analysis unit 134 that performs morphological analysis of the advertisement text on a word-by-word or phrase-by-phrase basis, an extraction unit 135 that extracts the CTR or CVR on a word-by-phrase basis, and an estimation unit 136 that estimates the advertising efficiency for each word or phrase based on the extracted CTR or CVR.

[0124] Moreover, the information processing device according to the present application further includes a providing unit 137 that suggests improvements to the TD of an advertisement based on the predicted CTR or CVR for each word or phrase.

[0125] The higher the CTR or CVR of the advertisement, the higher the score assigned to the word or phrase included in the advertisement by the extraction unit 135. The provision unit 137 provides information based on the score for each word or phrase.

[0126] The providing unit 137 presents the words or phrases in a ranking format based on the score for each word or phrase.

[0127] The information processing device according to the present application further includes a classification unit 132 that classifies advertisements by category and stores the classified advertisements in a database, and a reception unit 133 that receives a designated advertisement category. The analysis unit 134 extracts advertisements of the designated category from the database and performs morphological analysis on the extracted advertisement text on a word- or phrase-by-word basis. The extraction unit 135 extracts CTR or CVR on a word- or phrase-by-word basis. The estimation unit 136 estimates advertising efficiency for each word or phrase based on the extracted CTR or CVR. The provision unit 137 proposes improvements to the TD of advertisements of the designated category based on the predicted CTR or CVR for each word or phrase.

[0128] The extraction unit 135 uses machine learning to generate a model that outputs a higher score to words or phrases contained in an advertisement when the CTR or CVR of the advertisement is higher, and calculates the score by inputting words or phrases contained in advertisements of a specified category into the model.

[0129] The providing unit 137 automatically changes the TD of the advertisement based on the predicted CTR or CVR for each word or phrase.

[0130] The analysis unit 134 vectorizes the text of the advertisement using DOC2VEC. The extraction unit 135 predicts the CTR or CVR from the vector of the text of the advertisement.

[0131] By performing any one or a combination of the above-described processes, the information processing device according to the present application can create a model using machine learning from a database of search advertisements and predict recommended words.

[0132] [8. Hardware Configuration] The terminal device 10 and the server device 100 according to the above-described embodiments are realized by a computer 1000 having a configuration as shown in Fig. 11, for example. The following description will be given taking the server device 100 as an example. Fig. 11 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output I / F (Interface) 1060, an input I / F 1070, and a network I / F 1080 are connected via a bus 1090.

[0133] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic device 1030 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

[0134] The primary storage device 1040 is a memory device such as a RAM (Random Access Memory) that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. The secondary storage device 1050 may be an internal storage device or an external storage device. The secondary storage device 1050 may also be a removable storage medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) memory card. The secondary storage device 1050 may also be cloud storage (online storage), a NAS (Network Attached Storage), a file server, or the like.

[0135] The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a display, a projector, a printer, etc., and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (High Definition Multimedia Interface), etc. The input I / F 1070 is an interface for receiving information from various input devices 1020, such as a mouse, a keyboard, a keypad, a button, a scanner, etc., and is realized by a USB, etc.

[0136] Furthermore, the output I / F 1060 and the input I / F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

[0137] The output device 1010 and the input device 1020 may be integrated into one device, such as a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated into one device as an input / output I / F.

[0138] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0139] The network I / F 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0140] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I / F 1060 and the input I / F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0141] For example, when the computer 1000 functions as the server device 100, the arithmetic unit 1030 of the computer 1000 executes a program loaded onto the primary storage device 1040 to realize the functions of the control unit 130. The arithmetic unit 1030 of the computer 1000 may also load a program acquired from another device via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. The arithmetic unit 1030 of the computer 1000 may also cooperate with the other device via the network I / F 1080 to call and use the functions and data of a program from another program of the other device.

[0142] [9. Other] Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. Furthermore, the above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the scope of so-called equivalents. Furthermore, the above-described components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments.

[0143] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0144] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0145] For example, the above-mentioned server device 100 may be realized by multiple server computers, and depending on the function, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

[0146] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0147] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, an acquisition unit can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0148] 1. Information Processing Systems 10 Terminal Equipment 100 Server device 110 Communications Department 120 Storage section 121 User Information Database 122 Historical Information Database 123 Advertising Information Database 130 control section 131 Acquisition Department 132 Classification Department 133 Reception Department 134 Analysis Department 135 Extraction part 136 Estimation Department 137 Provision Department

Claims

1. A classification unit that classifies distributed advertisements by category and stores them in a database; an estimation unit that estimates advertising effectiveness based on CTR or CVR of a distributed advertisement for each category; a reception unit that receives, from an advertiser, a keyword indicating a specific category; an extracting unit that extracts advertisements in a category corresponding to the keyword designated by the advertiser from a database; an analysis unit that performs morphological analysis on character strings of titles and descriptions included in the extracted advertisements and extracts words or phrases; an assigning unit that assigns a score to a word or phrase included in an advertisement according to a CTR or CVR of the advertisement including the extracted word or phrase; a providing unit that provides the advertiser with information according to words or phrases included in advertisements of a designated category and scores for each of the words or phrases individually or in combination; An information processing device comprising:

2. The reception unit receives, as a category of advertisements, designation of keywords relating to at least one of advertisement content, advertisement type, advertisement purpose, and advertisement medium.

2. The information processing apparatus according to claim 1, wherein:

3. the assigning unit assigns a higher score to an advertisement including the extracted word or phrase as the CTR or CVR of the advertisement is higher, The providing unit presents the words or phrases in descending order of score.

2. The information processing apparatus according to claim 1, wherein:

4. A generation unit that generates a model that has learned a word or phrase and a score corresponding to the CTR or CVR of an advertisement that includes the word or phrase; a calculation unit that inputs words or phrases included in advertisements in a category corresponding to keywords designated by the advertiser into the model and calculates a score; The information processing apparatus according to claim 1 , further comprising:

5. The calculation unit calculates, for each category, a total value, an average value, or a median value of the scores calculated for each word or phrase as the score for that word or phrase.

5. The information processing apparatus according to claim 4,

6. The analysis unit vectorizes words or phrases contained in the advertisement using DOC2VEC; The generation unit generates a model that learns vectors and scores of words or phrases; The calculation unit inputs a vector of a word or phrase into the model and calculates a score.

5. The information processing apparatus according to claim 4,

7. A modification unit that automatically modifies words or phrases included in an advertisement to words or phrases with higher scores based on the score for each word or phrase.

5. The information processing apparatus according to claim 4, further comprising:

8. The calculation unit predicts a CTR or CVR for each word or phrase based on the score of the word or phrase; The estimation unit estimates advertising efficiency for each word or phrase based on the predicted CTR or CVR, and predicts words or phrases with high advertising efficiency.

5. The information processing apparatus according to claim 4,

9. An information processing method executed by an information processing device, a classification step of classifying the delivered advertisements by category and storing them in a database; an estimation step of estimating advertising effectiveness based on CTR or CVR of advertisements that have been delivered for each category; a receiving step of receiving, from an advertiser, a specification of a keyword indicating a specific category; an extraction step of extracting from a database advertisements in a category corresponding to the keyword designated by the advertiser; an analyzing step of performing morphological analysis on character strings of titles and descriptions included in the extracted advertisements to extract words or phrases; an assigning step of assigning a score to the word or phrase included in the advertisement according to the CTR or CVR of the advertisement including the extracted word or phrase; a providing step of providing the advertiser with information according to words or phrases included in advertisements of a specified category and scores for each of the words or phrases individually or in combination; An information processing method comprising:

10. A classification step of classifying delivered advertisements by category and storing them in a database; an estimation step of estimating advertising effectiveness based on CTR or CVR of advertisements that have already been delivered for each category; an acceptance procedure for accepting from an advertiser a specification of keywords indicating a particular category; an extraction step of extracting from a database advertisements in a category corresponding to the keyword designated by the advertiser; an analysis step of morphologically analyzing the character strings of the title and description included in the extracted advertisement to extract words or phrases; an assignment step of assigning a score to a word or phrase included in an advertisement according to a CTR or CVR of the advertisement including the extracted word or phrase; a provision step of providing the advertiser with information according to words or phrases included in advertisements of a designated category and scores for each of the words or phrases individually or in combination; An information processing program characterized by causing a computer to execute the above.

Images