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

Abstract

It provides estimated imputation for missing conversion tracking data in specific browsers. [Solution] The information processing device according to the present invention is characterized by comprising: a PB estimation model learning unit that determines data related to private browsing based on click logs of delivered advertisements and creates learning data, and trains a PB missing CV estimation model that estimates conversions that were missing due to private browsing using the learning data; a PB missing CV estimation unit that estimates conversions that were missing due to private browsing using the PB missing CV estimation model; and an output processing unit that reports or makes improvements regarding delivered advertisements based on the missing conversions estimated by the PB missing CV estimation model.

Description

Technical Field

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

Background Art

[0002] Techniques for estimating missing data in conversion (CV) measurement have been disclosed (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above prior art, although the missing data in conversion measurement is appropriately estimated, there is room for improvement in the method of estimating and complementing CV in a situation where missing occurs in conversion measurement in a specific browser after a predetermined version.

[0005] The present application has been made in view of the above, and an object thereof is to estimate and complement missing conversion measurement in a specific browser.

Means for Solving the Problems

[0006] The information processing device according to the present application is characterized by comprising: a PB estimation model learning unit that determines data related to private browsing based on click logs of delivered advertisements and creates learning data, and trains a PB missing CV estimation model that estimates conversions that were missing due to private browsing and could not be measured using the learning data; a PB missing CV estimation unit that estimates conversions that were missing due to private browsing and could not be measured using the PB missing CV estimation model; and an output processing unit that reports or makes improvements regarding delivered advertisements based on the missing conversions estimated by the PB missing CV estimation model. [Effects of the Invention]

[0007] According to one embodiment of the system, it is possible to estimate and fill in missing conversion measurement data for a particular browser. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is an explanatory diagram showing an overview of the information processing system according to the embodiment. [Figure 2] Figure 2 is an explanatory diagram illustrating the overview of the conversion measurement supplementary function. [Figure 3] Figure 3 shows an example of the configuration of a terminal device according to this embodiment. [Figure 4] Figure 4 shows an example of the configuration of a server device according to this embodiment. [Figure 5] Figure 5 is a flowchart showing the processing procedure according to the embodiment. [Figure 6] Figure 6 shows an example of a hardware configuration. [Modes for carrying out the invention]

[0009] The following describes in detail, with reference to the drawings, embodiments for implementing the information processing device, information processing method, and information processing program according to the present application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the information processing device, information processing method, and information processing program according to the present application. Furthermore, the same parts are denoted by the same reference numerals in the following embodiments, and redundant descriptions are omitted.

[0010] [1. Overview of the Information Processing System] First, with reference to Figure 1, an overview of the information processing system according to the embodiment will be described. Figure 1 is an explanatory diagram showing an overview of the information processing system according to the embodiment. As shown in Figure 1, the information processing system 1 according to the embodiment 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, either by wired or wireless means, enabling communication between them. This allows the terminal device 10 to cooperate with the server device 100. The network N is, for example, a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet.

[0011] Terminal device 10 is an information processing device used by user U. For example, terminal device 10 may be a smart device such as a smartphone or tablet, a PC (Personal Computer) such as a desktop or notebook (laptop), a mobile phone such as a feature phone, a PDA (Personal Digital Assistant), a game console or AV equipment with communication functions, information appliances / digital appliances, a car navigation system, a smartwatch or head-mounted display (HDD), a wearable device such as smart glasses, etc. Alternatively, terminal device 10 may be a house / building, car, home appliance, electronic device, etc. that are compatible with IoT (Internet of Things).

[0012] In this embodiment, the terminal device 10 is a smart device such as a smartphone or tablet used by user U, and is a mobile terminal device that can communicate with any server device via wireless communication networks such as LTE (Long Term Evolution), 4G (4th Generation), 5G (5th Generation), Bluetooth (registered trademark), or wireless LAN. The terminal device 10 also has a screen such as a liquid crystal display with touch panel functionality, and accepts various operations on displayed data such as content from user U using a finger or stylus, such as tapping, sliding, and scrolling. Operations performed on the area of ​​the screen where content is displayed may also be considered as operations on the content. Furthermore, the terminal device 10 may be an information processing device such as a desktop PC or notebook PC, not just a smart device.

[0013] The server device 100 is, for example, a computer such as a PC or blade server, or a mainframe or workstation. The server device 100 may also be implemented through cloud computing.

[0014] In this embodiment, the server device 100 is an information processing device that works in conjunction with each user U's terminal device 10 and provides each user U's terminal device 10 with API (Application Programming Interface) services for various applications (hereinafter referred to as "apps") and various data, and is implemented by a computer or cloud system.

[0015] Furthermore, the server device 100 may be an information processing device that provides some kind of online service to each user U's terminal device 10. For example, the server device 100 may provide services such as internet connection, search service, advertising distribution service, chat service, conversational service using voice, images, video, etc., SNS (Social Networking Service), e-commerce (EC), electronic payment, online games, online banking, online trading, accommodation / ticket reservations, video / music distribution, news, maps, route search, route guidance, route information, service information, and weather forecasts as online services. In practice, the server device 100 may cooperate with various servers that provide the above-mentioned online services and act as an intermediary for online services, or it may be responsible for processing online services.

[0016] The server device 100 can acquire user information about user U. For example, the server device 100 can acquire information about user U's attributes (attribute information), such as gender, age, and residential area. The server device 100 can also acquire information about user U's demographics, psychographics, geographics, behavioral attributes, etc. The server device 100 may also acquire information about the segment or persona to which user U belongs in the field of marketing, as user information. The server device 100 stores and manages information about user U's attributes (attribute information) along with identification information (user ID, etc.) that identifies user U.

[0017] In addition, the server device 100 acquires various types of history information (log data) indicating the actions of the user U from the terminal device 10 of the user U or from various servers or the like based on the user ID or the like. For example, the server device 100 acquires a location history, which is a history of the location and time of the user U, from the terminal device 10. In addition, the server device 100 acquires a search history, which is a history of search queries input by the user U, from a search server (search engine). In addition, the server device 100 acquires a browsing history, which is a history of the content viewed by the user U, from a content server. In addition, the server device 100 acquires a purchase history (settlement history), which is a history of the user U's product purchases and settlement processes, from an e-commerce server or a settlement processing server. In addition, the server device 100 may acquire a listing history and a sales history, which are histories of the user U's listings on the marketplace, from an e-commerce server or a settlement processing server. In addition, the server device 100 acquires a posting history, which is a history of the user U's posts, from a posting server or an SNS server that provides a word-of-mouth posting service. Note that each of the above various servers or the like may be the server device 100 itself. That is, the server device 100 may function as each of the above various servers or the like.

[0018] In addition, the number of each device included in the information processing system 1 shown in FIG. 1 is not limited to that shown. For example, in FIG. 1, for the sake of simplification of illustration, only one terminal device 10 is shown, but this is merely an example and is not limiting, and two or more may be provided.

[0019] [2. Estimated CV Expansion Function for Private Browsing] [2-1. Conversion Measurement Completion Function] Referring to FIG. 2, the conversion measurement completion function will be described. FIG. 2 is an explanatory diagram showing an overview of the conversion measurement completion function. In the present embodiment, the server device 100 performs delivery control of Web advertisements. Note that, for the advertiser server, which is a server device that operates the advertiser's website, since it will be described together with the server device 100 as being integrated, the description and illustration thereof are omitted. In reality, they exist independently.

[0020] Beginning with the impact of ITP (Intelligent Tracking Prevention), the restriction of third-party cookies in each browser has a significant impact on advertising effectiveness measurement. Therefore, the server device 100 provides several functions (such as automatic tag setting, site general tag, conversion (CV) measurement complement function tag, etc.) as a conversion (CV) measurement complement function in web advertising services. A third-party cookie is a cookie issued by a domain other than the site where the user has flowed in. A cookie is a small data file for temporarily storing information of a user who has accessed a website or web server in a browser.

[0021] As shown in FIG. 2, when an agency / advertiser turns on automatic tag setting in an advertising management tool, an advertising click ID is attached behind the URL (Uniform Resource Locator) when an advertisement is clicked. Then, "site general tag" and "conversion (CV) measurement complement function tag" are installed on all pages within the advertiser's website. By installing this tag on all pages, when transitioning from an advertisement with an attached advertising click ID, the information is saved in the advertiser site's cookie. After that, when a conversion (CV) occurs, the information can be transmitted to the server device 100.

[0022] 〔2-2. Counter-Private Browsing Estimated CV Extension Function〕 However, in a specific browser after a certain version, privacy is enhanced in private browsing (PB), the conversion (CV) measurement complement function does not work, and a deficit occurs in conversion (CV) measurement. Therefore, it is necessary to estimate and complement the missing CV. Private browsing is a browser function that does not save browsing information such as browsing history, cookies, site data, and login information during browsing, and is not tracked when the session ends. It is also called secret mode.

[0023] The reason for the missing data is that the link decoration used for tracking purposes has been removed, making it impossible to obtain the ad click ID. Link decoration is a technique that adds information to a URL, and when the URL is clicked, that information is passed to the linked site. Specifically, the part that appears after the "?" symbol added to the URL is the additional information. This additional information is called a query string. A query string can also be composed of multiple pieces of information called query parameters. Query parameters are separated by the "&" symbol and have the same format, and are written in the order of information label, "=" symbol, and the information itself, such as "label=information". The ad click ID is an identifier assigned when a user clicks on an ad, as an alternative to third-party cookies for measuring conversions (CV).

[0024] Therefore, in this embodiment, the framework and interface (I / F) for estimated CV for ITP are extended, and estimated CV for private browsing (PB) mode is newly provided for report generation and automatic optimization.

[0025] For example, as shown in Figure 1, the server device 100 separates raw data such as past click logs into training data for ITP identification and training data for PB identification. In the training job, it trains separate models with each type of training data, and in the inference job, it estimates the conversion rate (CV) using each model. At this time, the server device 100 trains an ITP-missing CV estimation model with the training data for ITP identification, and calculates and outputs the estimated CV for ITP using the ITP-missing CV estimation model. The server device 100 also trains a PB-missing CV estimation model with the training data for PB identification, and calculates and outputs the estimated CV for PB using the PB-missing CV estimation model. Then, the server device 100 performs automatic optimization of ad delivery control and generates reports on ad delivery based on the estimated CV via an interface (I / F) that accepts the estimated CV. If the estimated CV for ITP and the estimated CV for PB overlap, the interface (I / F) overwrites the estimated CV for ITP with the estimated CV for PB.

[0026] The inference jobs are performed daily in batch inference. Specifically, the server device 100 batch infers the number of CVs that occurred on the job execution day, linked to past click logs, and transmits the results to each component via the database.

[0027] The reason why the estimated CV models for ITP and PB are separated is because the problem settings for ITP and PB are different. In ITP, the CV is assumed to be missing from 8 days after the click, while in PB, the CV is assumed to be missing from the day of the click. Therefore, for ITP, a model is built to predict the CV from the 8th day onward, adding CV measurements for the 7 days after the click as features, while for PB, a model is built to predict the CV from the first day without including CV measurements as features. Thus, different models are built and operated for each.

[0028] In the example shown in Figure 1, the server device 100 acquires raw data such as past click logs (step S1). For example, the server device 100 accesses a database that collects and stores click logs, refers to past click logs, and uses them as raw data.

[0029] Next, the server device 100 extracts clicks and the presence or absence of CVs related to ITP from the raw data such as past click logs to create training data for ITP identification, and trains an ITP missing CV estimation model with this training data (step S2).

[0030] Next, the server device 100 inputs the ITP-related clicks into the ITP missing CV estimation model to estimate the CV missing due to ITP (step S3). That is, the server device 100 causes the ITP missing CV estimation model to output the estimated CV for ITP. This process may also be performed using batch processing.

[0031] Furthermore, the server device 100 estimates the presence or absence of clicks and CVs related to PB based on raw data such as past click logs, creates training data for PB identification, and trains a PB-missing CV estimation model with this training data (step S4).

[0032] Next, the server device 100 inputs clicks related to PB into the PB-missing CV estimation model to estimate the CV missing due to PB (step S5). That is, the server device 100 causes the PB-missing CV estimation model to output the estimated CV for PB. Note that this process may be performed in batch processing.

[0033] Next, the server device 100 collects the estimated CV for ITP and the estimated CV for PB at the interface (I / F) that accepts the estimated CV (Step S6).

[0034] Next, the server device 100 creates a report based on the collected estimated conversions and provides it to the agency / advertiser (step S7). In this report, the server device 100 may make suggestions to the agency / advertiser regarding the content of the advertisement and delivery settings.

[0035] Furthermore, the server device 100 automatically optimizes the delivered advertisements based on the collected estimated conversions (step S8). In other words, the server device 100 automatically controls the delivery of advertisements and changes settings based on the collected estimated conversions.

[0036] [2-3. Private Browsing Determination] It is impossible to determine from the logs whether or not it is private browsing (PB). Therefore, an approximate determination of PB is made only for the company's own distribution channels. For example, the server device 100 approximates a user as PB if the elapsed time since the issuance of a cookie issued by the company to the user is within a certain threshold. In this embodiment, the threshold is conservatively set to "1 hour". In reality, the threshold can be arbitrary. However, there may be false positives / false negatives in the determination. Also, the company's own distribution channels are just one example. In reality, it is not limited to the company itself, but could be a specific domain, etc., that is subject to CV measurement, such as a client company that is a target of the company's services and can obtain user click logs in the same way as the company itself. Furthermore, it is not limited to distribution channels, but could be a specific web distribution medium, a specific website or web page (landing page (LP)), a specific app, a specific thumbnail, specific content, etc.

[0037] [2-4. Alternatives to User Information] In this embodiment, since private browsing (PB) is the target of estimation, it may be difficult to utilize user information during estimation. In this case, the estimation model may internally perform estimation using advertiser account information and advertising information.

[0038] The server device 100 may implement the above mechanism using AI (Artificial Intelligence) such as GPT (Generative Pre-trained Transformer). GPT is a text generation AI and a language model capable of generating text using natural language processing.

[0039] [2-5. Expected Effects] (1) Estimated increase in CV When using the extended function according to this embodiment, the estimated CV increased by approximately 10%-12% on each day of the measurement period compared to when it was not used. (2) Strengthening distribution to suspected targets By strengthening ad delivery to estimated targets, the delivery of ads to new estimated targets was promoted, resulting in a 20.9% increase in clicks for new estimated targets in online tests.

[0040] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be described using Figure 3. Figure 3 is a diagram showing an example of the configuration of the terminal device 10 according to the embodiment. As shown in Figure 3, the terminal device 10 comprises 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.

[0041] (Communications Section 11) The communication unit 11 is connected to the network N by wire or wireless connection and transmits and receives information to and from the server device 100 via the network N. For example, the communication unit 11 can be implemented using a NIC (Network Interface Card) or an antenna.

[0042] (Display section 12) The display unit 12 is a display device that displays various information such as location information. For example, the display unit 12 may be a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). The display unit 12 may also be a touch panel display, but is not limited to this.

[0043] (Input section 13) The input unit 13 is an input device that receives various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, etc. The input unit 13 may also be an input / output port (I / O port) or a USB (Universal Serial Bus) port. 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 also be a microphone that receives voice input from the user U. The microphone may be wireless.

[0044] (Positioning unit 14) The positioning unit 14 receives signals (radio waves) transmitted from GPS (Global Positioning System) satellites and, based on the received signals, acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10. In other words, the positioning unit 14 determines the position of the terminal device 10. Note that GPS is just one example of a GNSS (Global Navigation Satellite System).

[0045] Furthermore, the positioning unit 14 can determine its position using various methods other than GPS. For example, the positioning unit 14 may use various communication functions of the terminal device 10 to determine its position as an auxiliary positioning means for position correction, etc., as described below.

[0046] (Wi-Fi positioning) For example, the positioning unit 14 determines the location of the terminal device 10 by utilizing the Wi-Fi® communication function of the terminal device 10 and the communication network provided by each telecommunications company. Specifically, the positioning unit 14 determines the location of the terminal device 10 by performing Wi-Fi communication, etc., and determining the distance to nearby base stations and access points.

[0047] (Beacon positioning) Furthermore, the positioning unit 14 may determine the location using the Bluetooth® function of the terminal device 10. For example, the positioning unit 14 determines the location of the terminal device 10 by connecting to a beacon transmitter connected via the Bluetooth® function.

[0048] (Geomagnetic positioning) Furthermore, the positioning unit 14 determines the position of the terminal device 10 based on the geomagnetic pattern of the structure, which has been measured in advance, and the geomagnetic sensor provided by the terminal device 10.

[0049] (RFID positioning) Furthermore, if, for example, the terminal device 10 is equipped with an RFID (Radio Frequency Identification) tag function equivalent to that of a contactless IC card used at a train station ticket gate or in a store, or if it is equipped with a function to read RFID tags, the location where it was used will be recorded along with the information on the payment or other transactions made by the terminal device 10. The positioning unit 14 may determine the location of the terminal device 10 by acquiring such information. Alternatively, the location may be determined by an optical sensor or infrared sensor equipped in the terminal device 10.

[0050] The positioning unit 14 may, if necessary, determine the position of the terminal device 10 using one or a combination of the positioning means described above.

[0051] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection can 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 Figure 3, 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.

[0052] The sensors 21-28 described above are merely examples and not limiting. In other words, the sensor unit 20 may be configured to include some of the sensors 21-28, or it may include other sensors such as humidity sensors in addition to or instead of the sensors 21-28.

[0053] The acceleration sensor 21 is, for example, a 3-axis acceleration sensor and detects the physical movement of the terminal device 10, such as its direction of movement, velocity, and acceleration. The gyro sensor 22 detects the physical movement of the terminal device 10, such as its tilt in the three axes, based on its angular velocity. The barometric pressure sensor 23 detects the atmospheric pressure around the terminal device 10, for example.

[0054] Since the terminal device 10 is equipped with the acceleration sensor 21, gyroscope 22, barometric pressure sensor 23, etc., it becomes possible to determine the position of the terminal device 10 using technologies such as pedestrian dead-reckoning (PDR) that utilize these sensors 21 to 23. This makes it possible to obtain indoor location information that is difficult to obtain with positioning systems such as GPS.

[0055] For example, a pedometer using an accelerometer 21 can calculate the number of steps, walking speed, and distance walked. Additionally, a gyroscope 22 can be used to determine the user U's direction of movement, gaze direction, and body tilt. Furthermore, the barometric pressure detected by the barometric pressure sensor 23 can be used to determine the altitude and floor number of the user U's terminal device 10.

[0056] The temperature sensor 24 detects, for example, the ambient temperature around the terminal device 10. The sound sensor 25 detects, for example, the ambient sound around the terminal device 10. The light sensor 26 detects the ambient illumination around the terminal device 10. The magnetic sensor 27 detects, for example, the Earth's magnetic field around the terminal device 10. The image sensor 28 captures an image of the area around the terminal device 10.

[0057] The aforementioned pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the surrounding environment and conditions of the terminal device 10 by detecting atmospheric pressure, temperature, sound, and illuminance, respectively, and by capturing images of the surroundings. Furthermore, it becomes possible to improve the accuracy of the location information of the terminal device 10 based on the surrounding environment and conditions.

[0058] (Control Unit 30) The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit) or MPU (Micro Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), input / output ports, and various circuits. Alternatively, the control unit 30 may be composed of hardware such as an integrated circuit (ASIC) or FPGA (Field Programmable Gate Array). The control unit 30 includes a transmission unit 31, a reception unit 32, and a processing unit 33.

[0059] (Transmitter 31) The transmission unit 31 can transmit various information, such as information input by the user U using the input unit 13, various information detected by sensors 21-28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 determined by the positioning unit 14, to the server device 100 via the communication unit 11.

[0060] (Receiving unit 32) The receiving unit 32 can receive various information provided by the server device 100, as well as requests for various information from the server device 100, via the communication unit 11.

[0061] (Processing 33) The processing unit 33 controls the entire terminal device 10, including the display unit 12. For example, the processing unit 33 can output and display various information transmitted by the transmission unit 31 and various information received from the server device 100 by the reception unit 32 to the display unit 12.

[0062] (Storage unit 40) The storage unit 40 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as HDD (Hard Disk Drive), SSD (Solid State Drive), and optical discs. Various programs and various data are stored in this storage unit 40.

[0063] [4. Example of Server Device Configuration] Next, the configuration of the server device 100 according to the embodiment will be described using Figure 4. Figure 4 is a diagram showing an example of the configuration of the server device 100 according to the embodiment. As shown in Figure 4, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0064] (Communications Department 110) The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). The communication unit 110 is connected to the network N by wire or wireless connection.

[0065] (Storage unit 120) The storage unit 120 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as HDDs, SSDs, and optical discs. The storage unit 120 may store identification information (such as a user ID) indicating user U, as well as attribute information and history information (log data) of user U.

[0066] (Control unit 130) The control unit 130 is a controller, and is realized by executing various programs (corresponding to an example of an information processing program) stored in the internal storage device of the server device 100 using a storage area such as RAM as a working area, for example, by a CPU (Central Processing Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), or FPGA (Field Programmable Gate Array). In the example shown in Figure 4, the control unit 130 has an acquisition unit 131, an ITP estimation model learning unit 132, an ITP missing CV estimation unit 133, a PB estimation model learning unit 134, a PB missing CV estimation unit 135, and an output processing unit 136.

[0067] (Acquisition part 131) The acquisition unit 131 acquires the search query entered by the user U. For example, when the user U enters a search query into a search engine or the like and performs a keyword search, the acquisition unit 131 acquires the search query via the communication unit 110. In other words, the acquisition unit 131 acquires the keyword entered by the user U into the search box of a search engine, website, or application via the communication unit 110.

[0068] Furthermore, the acquisition unit 131 acquires user information about user U via the communication unit 110. For example, the acquisition unit 131 acquires identification information (such as user ID), location information, and attribute information of user U from user U's terminal device 10. The acquisition unit 131 may also acquire identification information and attribute information of user U when user U is registered. The acquisition unit 131 then stores the user information in the storage unit 120.

[0069] Furthermore, the acquisition unit 131 acquires various historical information (log data) indicating the user U's actions via the communication unit 110. For example, the acquisition unit 131 acquires various historical information indicating the user U's actions from the user U's terminal device 10, or from various servers based on the user ID, etc. The acquisition unit 131 then stores the various historical information in the storage unit 120.

[0070] Furthermore, the acquisition unit 131 acquires click logs for delivered advertisements via the communication unit 110. The acquisition unit 131 then stores the click logs for delivered advertisements in the storage unit 120. At this time, the acquisition unit 131 may also store the click logs for delivered advertisements in a database in the storage unit 120.

[0071] (ITP Estimation Model Learning Unit 132) The ITP estimation model learning unit 132 extracts ITP-related data, which is a tracking prevention function, from the click logs of delivered advertisements to create learning data, and then trains an ITP missing conversion estimation model that estimates conversions (CV) that were missing due to ITP and could not be measured.

[0072] (ITP missing CV estimation unit 133) The ITP missing CV estimation unit 133 estimates the missing conversions (CV) that could not be measured by ITP using the above ITP missing CV estimation model.

[0073] (PB Estimation Model Learning Unit 134) The PB estimation model learning unit 134, separately from the ITP missing CV estimation model described above, determines private browsing (PB) related data based on the click logs of delivered advertisements to create learning data, and uses this learning data to train a PB missing CV estimation model that estimates conversions (CV) that were missing due to private browsing (PB) and could not be measured.

[0074] At this time, the PB estimation model learning unit 134 performs an approximate determination of private browsing (PB) limited to a specific domain.

[0075] For example, the PB estimation model learning unit 134, limited to a specific domain, approximates that a cookie issued in that specific domain is private browsing (PB) if the elapsed time since issuance is within a certain threshold.

[0076] Furthermore, the PB estimation model learning unit 134, in order to estimate private browsing (PB), does not use user information, but instead performs estimation using advertiser account information or advertising information.

[0077] (PB-deficient CV estimation unit 135) The PB missing CV estimation unit 135 estimates the missing conversions (CV) that could not be measured due to private browsing (PB) using the PB missing CV estimation model described above.

[0078] (Output processing unit 136) The output processing unit 136 reports on or makes improvements to delivered advertisements based on the missing conversions (CV) estimated by the PB missing CV estimation model. Here, the output processing unit 136 reports on or makes improvements to delivered advertisements based on the missing conversions (CV) estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model, respectively.

[0079] For example, the output processing unit 136 creates a report based on the missing conversions (CVs) estimated by the PB missing CV estimation model and provides it to the advertising agency or advertiser. Here, the output processing unit 136 creates a report based on the missing conversions (CVs) estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model, and provides it to the advertising agency or advertiser.

[0080] Alternatively, the output processing unit 136 automatically optimizes delivered advertisements based on missing conversions (CVs) estimated by the PB missing CV estimation model. Here, the output processing unit 136 automatically optimizes delivered advertisements based on missing conversions (CVs) individually estimated by the ITP missing CV estimation model and the PB missing CV estimation model, respectively. For example, the output processing unit 136 automatically performs ad delivery control and setting changes based on missing conversions (CVs) individually estimated by the ITP missing CV estimation model and the PB missing CV estimation model, respectively.

[0081] Furthermore, the output processing unit 136 includes an interface (I / F) and, if the missing conversion (CV) estimated by the ITP missing CV estimation model and the missing conversion (CV) estimated by the PB missing CV estimation model overlap, it performs a process to overwrite the missing conversion (CV) estimated by the ITP missing CV estimation model with the missing conversion (CV) estimated by the PB missing CV estimation model.

[0082] [5. Processing Procedure] Next, the processing procedure by the server device 100 according to the embodiment will be described using Figure 5. Figure 5 is a flowchart of 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.

[0083] For example, as shown in Figure 5, the acquisition unit 131 of the server device 100 acquires click logs for delivered advertisements via the communication unit 110 and stores them in the storage unit 120 (step S101).

[0084] Next, the ITP estimation model learning unit 132 of the server device 100 extracts ITP-related data, which is a tracking prevention function, from the click logs of delivered advertisements to create learning data, and then trains an ITP missing conversion estimation model that estimates conversions (CV) that were missing due to ITP and could not be measured (step S102).

[0085] Next, the ITP missing CV estimation unit 133 of the server device 100 estimates the missing conversions (CV) that could not be measured by ITP using the above ITP missing CV estimation model (step S103).

[0086] Furthermore, the PB estimation model learning unit 134 of the server device 100, separately from the ITP missing CV estimation model, determines private browsing (PB) related data based on the click logs of delivered advertisements to create learning data, and trains a PB missing CV estimation model that estimates conversions (CV) that were missing due to private browsing (PB) (step S104).

[0087] Next, the PB-missing CV estimation unit 135 of the server device 100 estimates the conversions (CV) that were missing due to private browsing (PB) and could not be measured, using the PB-missing CV estimation model (step S105).

[0088] Furthermore, the processing in steps S102 and S103 for ITP and the processing in steps S104 and S105 for private browsing (PB) may be executed in parallel or at different times.

[0089] Next, the output processing unit 136 of the server device 100 obtains the missing conversions (CVs) estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model, respectively, via the interface (I / F) (step S106). At this time, if the missing conversions (CVs) estimated by the ITP missing CV estimation model and the missing conversions (CVs) estimated by the PB missing CV estimation model overlap, the output processing unit 136 overwrites the missing conversions (CVs) estimated by the ITP missing CV estimation model with the missing conversions (CVs) estimated by the PB missing CV estimation model.

[0090] Next, the output processing unit 136 of the server device 100 creates a report based on the missing conversions (CVs) estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model, and provides it to the advertising agency or advertiser (step S107).

[0091] Alternatively, the delivered advertisements are automatically optimized based on the missing conversions (CVs) estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model of the server device 100 (step S108).

[0092] Note that either step S107 or step S108 may be performed individually, or both may be performed.

[0093] [6. Variant Example] The terminal device 10 and server device 100 described above may be implemented in various other forms besides those of the embodiment described above. Therefore, the following describes modifications of the embodiment.

[0094] In the above embodiment, some or all of the processing performed by the server device 100 may actually be performed by the terminal device 10 (or an application running on the terminal device 10). For example, the terminal device 10 may perform all processing in a standalone manner. In this case, the terminal device 10 is assumed to have the same functions as the server device 100 in the above embodiment. Furthermore, in the above embodiment, since the terminal device 10 is in cooperation with the server device 100, from the perspective of the user U, it appears as if the processing of the server device 100 is also being performed by the terminal device 10. In other words, from another perspective, it can be said that the terminal device 10 is equipped with the server device 100.

[0095] Furthermore, in the above embodiment, the server device 100 estimates the conversions (CVs) lost due to ITP and PB using an estimation model, but is not limited to this. For example, the server device 100 may estimate the order of conversions (CVs) in ITP and PB using an estimation model, or it may estimate engagements in addition to conversions (CVs).

[0096] Furthermore, in the above embodiment, the conversion (CV) estimated by the estimation model may be a direct conversion or an indirect conversion. It may also be a unique conversion or a total conversion. In other words, the type of conversion is irrelevant.

[0097] Furthermore, in the above embodiment, the server device 100 may estimate the conversion rate (CVR) based on the number of conversions (CVs) estimated by the estimation model. That is, the server device 100 may calculate the estimated CVR based on the number of estimated CVs. The server device 100 may then generate reports and perform automatic optimization based on the estimated CVR.

[0098] Furthermore, in the above embodiment, the estimated CV for ITP and the estimated CV for PB may be merged.

[0099] [7. Effects] As described above, the information processing device (terminal device 10 and server device 100) according to the present application is characterized by comprising: a PB estimation model learning unit 134 that determines data related to private browsing based on click logs of delivered advertisements and creates learning data, and trains a PB missing CV estimation model that estimates conversions that were missing due to private browsing using the learning data; a PB missing CV estimation unit 135 that estimates conversions that were missing due to private browsing using the PB missing CV estimation model; and an output processing unit 136 that reports or makes improvements regarding delivered advertisements based on the missing conversions estimated by the PB missing CV estimation model.

[0100] This allows for estimation and imputation of missing conversion (CV) data from private browsing (PB) in specific browsers.

[0101] Furthermore, the information processing device according to the present application further includes an ITP estimation model learning unit 132 that extracts ITP-related data, which is a tracking prevention function, from the click logs of delivered advertisements to create learning data, and trains an ITP-missing CV estimation model that estimates conversions that are missing due to ITP and cannot be measured, separately from the PB-missing CV estimation model, and an ITP-missing CV estimation unit 133 that estimates conversions that are missing due to ITP and cannot be measured, using the ITP-missing CV estimation model. The PB estimation model learning unit 134, separately from the ITP-missing CV estimation model, determines private browsing-related data based on the click logs of delivered advertisements to create learning data, and trains a PB-missing CV estimation model that estimates conversions that are missing due to private browsing and cannot be measured, using the learning data. The PB-missing CV estimation unit 135 estimates conversions that are missing due to private browsing and cannot be measured, using the PB-missing CV estimation model. The output processing unit 136 reports on or makes improvements to delivered advertisements based on the missing conversions estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model, respectively.

[0102] This allows for the construction and operation of separate models for each, such as assuming that conversions (CV) are missing from 8 days after a click in ITP, while CV is missing from the day of the click in private browsing (PB). For example, a model is built for ITP that predicts CV from the 8th day onwards by adding CV measurements for the 7 days after the click as features, and a model is built for PB that predicts CV from the first day without including CV measurements as features.

[0103] The PB estimation model learning unit 134 performs approximate determination of private browsing, limited to specific domains.

[0104] This allows for approximate determination, even when it's impossible to determine from the logs whether or not it's private browsing (PB), by limiting it to the company's own domain and delivery platform.

[0105] The PB estimation model learning unit 134, limited to a specific domain, approximates private browsing if the elapsed time since the issuance of a cookie issued in that specific domain is within a certain threshold.

[0106] This allows, for example, to limit the scope to one's own distribution channels and investigate whether a user is browsing privately if the elapsed time since the issuance of a cookie issued by that company is within a certain threshold (e.g., 1 hour).

[0107] The PB estimation model learning unit 134, in order to estimate private browsing, does not use user information but instead uses advertiser account information or advertising information to perform the estimation.

[0108] This allows for the estimation of private browsing even when it is difficult to utilize user information during estimation, by internally using advertiser account information or advertising information in the estimation model.

[0109] The output processing unit 136 creates a report based on the missing conversions estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model, and provides it to the advertising agency or advertiser.

[0110] This allows us to report to agencies or advertisers the missing conversions (CVs) in ITP and private browsing (PB) based on the conversions estimated by the estimation model, and to propose improvement plans based on the estimated CVs.

[0111] The output processing unit 136 automatically optimizes delivered advertisements based on missing conversions estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model, respectively.

[0112] This allows for the automatic implementation of ad delivery control and setting changes, etc., based on conversions (CV) estimated by the estimation model, to reflect missing CVs in ITP and private browsing (PB).

[0113] If the missing conversion estimated by the ITP missing CV estimation model and the missing conversion estimated by the PB missing CV estimation model overlap, the output processing unit 136 performs a process to overwrite the missing conversion estimated by the ITP missing CV estimation model with the missing conversion estimated by the PB missing CV estimation model.

[0114] This allows us to resolve overlaps between estimated CVs for ITP and PB, report missing CVs, and propose improvement plans based on the estimated CVs.

[0115] By any or a combination of the above-described processes, the information processing device according to the present invention can estimate and fill in gaps in conversion measurement for a specific browser.

[0116] [8. Hardware Configuration] Furthermore, the terminal device 10 and server device 100 according to the above-described embodiment are realized by a computer 1000 having a configuration such as that shown in Figure 6. The following explanation will use the server device 100 as an example. Figure 6 is a diagram showing an example of the hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which an arithmetic unit 1030, a primary storage device 1040, a secondary storage device 1050, an output interface 1060, an input interface 1070, and a network interface 1080 are connected by a bus 1090.

[0117] The arithmetic unit 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, as well as programs read from the input device 1020, and executes various processes. The arithmetic unit 1030 can be implemented using, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field Programmable Gate Array).

[0118] The primary storage device 1040 is a memory device, such as RAM (Random Access Memory), that temporarily stores data used by the arithmetic unit 1030 for various calculations. The secondary storage device 1050 is a storage device where data used by the arithmetic unit 1030 for various calculations and various databases are registered, and can be implemented using ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, etc. The secondary storage device 1050 may be internal storage or external storage. The secondary storage device 1050 may also be a removable storage medium such as USB (Universal Serial Bus) memory or SD (Secure Digital) memory card. The secondary storage device 1050 may also be cloud storage (online storage), NAS (Network Attached Storage), file server, etc.

[0119] The output I / F 1060 is an interface for transmitting information to be output to output devices 1010, such as displays, projectors, and printers, and is implemented using connectors of standards such as USB (Universal Serial Bus), DVI (Digital Visual Interface), and HDMI (High Definition Multimedia Interface). The input I / F 1070 is an interface for receiving information from various input devices 1020, such as mice, keyboards, keypads, buttons, and scanners, and is implemented using, for example, USB.

[0120] Furthermore, the output interface 1060 and input interface 1070 may be wirelessly connected to the output device 1010 and input device 1020, respectively. In other words, the output device 1010 and input device 1020 may be wireless devices.

[0121] Furthermore, the output device 1010 and the input device 1020 may be integrated as a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated as an input / output I / F.

[0122] The input device 1020 may also be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), DVD (Digital Versatile Disc), or 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.

[0123] The network interface 1080 receives data from other devices via network N and sends it to the computing unit 1030, and also transmits data generated by the computing unit 1030 to other devices via network N.

[0124] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output interface 1060 and the input interface 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.

[0125] For example, when computer 1000 functions as a server device 100, the arithmetic unit 1030 of computer 1000 realizes the functions of the control unit 130 by executing a program loaded onto the primary storage device 1040. Alternatively, the arithmetic unit 1030 of computer 1000 may load a program obtained from another device via the network interface 1080 onto the primary storage device 1040 and execute the loaded program. Furthermore, the arithmetic unit 1030 of computer 1000 may cooperate with other devices via the network interface 1080 and call and use program functions, data, etc., from other programs on other devices.

[0126] [9. Other] Although embodiments of the present invention have been described above, the present invention is not limited by the content of these embodiments. Furthermore, the aforementioned components include those that can be easily conceived by those skilled in the art, those that are substantially the same, and those that fall within the so-called equivalent range. Moreover, the aforementioned components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the gist of the embodiments described above.

[0127] 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 by known methods. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0128] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0129] For example, the server device 100 described above may be implemented using multiple server computers, and the configuration can be flexibly changed, such as by calling external platforms via APIs (Application Programming Interfaces) or network computing depending on the function.

[0130] Furthermore, the embodiments and modifications described above can be combined as appropriate, provided that the processing content is not inconsistent.

[0131] Furthermore, the terms "section, module, unit" mentioned above can be replaced with "means" or "circuit," etc. For example, the acquisition unit can be replaced with acquisition means or acquisition circuit. [Explanation of Symbols]

[0132] 1. Information Processing System 10 Terminal devices 100 Server Devices 110 Communications Department 120 Storage section 130 Control Unit 131 Acquisition Department 132 ITP Estimation Model Learning Unit 133 ITP Missing CV Estimation Unit 134 PB Estimation Model Learning Unit 135 PB-deficient CV estimation unit 136 Output Processing Unit

Claims

1. A PB estimation model learning unit that determines private browsing-related data based on click logs of delivered advertisements to create training data, and then trains a PB missing conversion estimation model that estimates conversions that were missing due to private browsing, and A PB missing CV estimation unit estimates conversions that were missing due to private browsing and could not be measured using the aforementioned PB missing CV estimation model, An output processing unit that reports on or makes improvements to delivered advertisements based on missing conversions estimated by the PB missing CV estimation model, An information processing device characterized by comprising:

2. In addition to the aforementioned PB missing CV estimation model, the ITP estimation model learning unit extracts ITP-related data, which is a tracking prevention function, from the click logs of delivered advertisements to create training data, and trains an ITP missing CV estimation model that estimates conversions that were missing due to ITP not being able to be measured, using this training data. An ITP missing CV estimation unit estimates missing conversions that could not be measured by ITP using the aforementioned ITP missing CV estimation model, Furthermore, The PB estimation model learning unit, separately from the ITP missing CV estimation model, determines private browsing-related data based on the click logs of delivered advertisements to create learning data, and uses this learning data to train the PB missing CV estimation model, which estimates conversions that were missing due to private browsing and could not be measured. The PB Missing CV Estimation Unit estimates conversions that were missing due to private browsing and could not be measured using the PB Missing CV Estimation Model. The output processing unit reports or makes improvements to delivered advertisements based on the missing conversions estimated individually by the ITP missing CV estimation model and the PB missing CV estimation model, respectively. The information processing apparatus according to feature 1.

3. The aforementioned PB estimation model learning unit performs an approximate determination of private browsing only for specific domains. The information processing apparatus according to feature 1.

4. The PB estimation model learning unit, limited to a specific domain, approximates private browsing if the elapsed time since the issuance of a cookie issued in that specific domain is within a certain threshold. The information processing apparatus according to feature 1.

5. The aforementioned PB estimation model learning unit, in order to estimate private browsing, does not use user information but instead performs estimations using advertiser account information or advertising information. The information processing apparatus according to feature 1.

6. The output processing unit creates a report based on the missing conversions estimated by the PB missing CV estimation model and provides it to the advertising agency or advertiser. The information processing apparatus according to feature 1.

7. The output processing unit automatically optimizes delivered advertisements based on the missing conversions estimated by the PB missing CV estimation model. The information processing apparatus according to feature 1.

8. If the missing conversion estimated by the ITP missing CV estimation model and the missing conversion estimated by the PB missing CV estimation model overlap, the output processing unit performs a process to overwrite the missing conversion estimated by the ITP missing CV estimation model with the missing conversion estimated by the PB missing CV estimation model. The information processing apparatus according to feature 2.

9. An information processing method performed by an information processing device, The process involves creating training data by determining private browsing-related data based on click logs of delivered advertisements, and then training a PB missing conversion estimation model with this training data to estimate conversions that were missing due to private browsing. A PB missing CV estimation step is performed to estimate conversions that were missing due to private browsing and could not be measured using the aforementioned PB missing CV estimation model, An output processing step that reports on or improves the delivered advertisement based on the missing conversions estimated by the PB missing CV estimation model, An information processing method characterized by including

10. A PB estimation model training procedure involves determining private browsing-related data based on click logs of delivered advertisements to create training data, and then training a PB missing conversion estimation model that estimates conversions that were not measured due to private browsing using this training data. A procedure for estimating missing conversions that could not be measured due to private browsing, using the aforementioned PB missing conversion estimation model, and An output processing procedure that reports on or improves the delivered advertisement based on the missing conversions estimated by the PB missing CV estimation model, An information processing program characterized by causing a computer to execute it.

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