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

Abstract

Improve conversion rates by taking into account user consideration time. [Solution] The information processing device of the present application is characterized by comprising a measurement unit that measures the time when an advertisement is clicked on a site of a specific domain as a first time point, and measures the time when the advertisement is displayed again on the site of the specific domain as a second time point; a calculation unit that calculates the time from the first time point to the second time point as the LP dwell time; a determination unit that determines whether the LP dwell time is equal to or greater than a threshold value, and if the LP dwell time is equal to or greater than the threshold value, determines it to be an engaged click; and a delivery control unit that uses the engaged click as auxiliary information just before conversion and controls the delivery of advertisements.

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 has been disclosed that predicts conversion rates using a prediction model that uses input information including advertising information, user information, and distribution surface information as input and predicts the conversion rate of advertising content when the advertising content is distributed to a distribution surface (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-144304 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the above-mentioned conventional technology, the conversion rate is predicted using a prediction model that predicts the conversion rate of advertising content, but the consideration time of users leading up to conversion and the consideration time of users who did not convert are not taken into account. Therefore, we propose a means for improving the conversion rate by taking into account the consideration time of users.

[0005] The present application has been made in view of the above, and aims to improve the conversion rate by taking into account the user's consideration time. [Means for solving the problem]

[0006] The information processing device according to the present application is characterized by comprising: a measurement unit that measures the time when an advertisement is clicked on a site of a specific domain as a first time point, and measures the time when the advertisement is displayed again on the site of the specific domain as a second time point; a calculation unit that calculates the time from the first time point to the second time point as LP dwell time; a determination unit that determines whether the LP dwell time is equal to or greater than a threshold value, and if the LP dwell time is equal to or greater than the threshold value, determines that it is an engaged click; and a delivery control unit that uses the engaged click as auxiliary information just before conversion and controls the delivery of advertisements. [Effects of the Invention]

[0007] According to one aspect of the embodiment, the conversion rate can be improved by taking into account the user's consideration time. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram showing an overview of an information processing system according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram showing an outline of a conventional CV optimization logic. [Figure 3] FIG. 3 is an explanatory diagram showing an overview of an engaged click. [Figure 4] FIG. 4 is an explanatory diagram illustrating an overview of CVR prediction according to the embodiment. [Figure 5] FIG. 5 is an explanatory diagram illustrating an overview of CV estimation according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of the configuration of a server device according to the embodiment. [Figure 8] FIG. 8 is a flowchart showing a processing procedure according to the embodiment. [Figure 9] FIG. 9 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 the information processing system] First, an overview of an information processing system 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 system according to an embodiment. As shown in Fig. 1, an information processing system 1 according to an 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 in a wired or wireless manner so as to be able to communicate with each other. This enables 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), the Internet, etc.

[0011] Terminal device 10 is an information processing device used by a user U. For example, terminal device 10 may be a smart device such as a smartphone or tablet terminal, a desktop or notebook (laptop) type PC (Personal Computer), a mobile phone such as a feature phone (Gala-ke or Gala-ho), a PDA (Personal Digital Assistant), a game console or AV device with communication functions, an information appliance or digital appliance, a car navigation system, a wearable device such as a smart watch, a head-mounted display, or smart glasses. Terminal device 10 may also be a house or building, a car, a home appliance, an electronic device, or the like that is compatible with the Internet of Things (IOT).

[0012] In this embodiment, the terminal device 10 is a smart device such as a smartphone or tablet 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 LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: fifth generation mobile communication system), Bluetooth (registered trademark), or wireless LAN. 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 or a laptop PC.

[0013] 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.

[0014] In this embodiment, 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.

[0015] The server device 100 may also be an information processing device that provides some kind of online service to the terminal device 10 of each user U. For example, the server device 100 may provide the following online services: internet connection, search service, chat service, interactive service using voice, images, videos, etc., 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, weather forecast, etc. In practice, the server device 100 may cooperate with various servers that provide the above-mentioned online services and act as an intermediary for the online services or may be responsible for processing the online services.

[0016] The server device 100 can acquire user information about the user U. For example, the server device 100 acquires, as the user information, information (attribute information) about the attributes of the user U, such as the gender, age, and residential area of ​​the user U. The server device 100 can also acquire information about the attributes of the user U, such as demographic attributes, psychographic attributes, geographic attributes, and behavioral attributes. The server device 100 may also acquire, as the user information, a segment to which the user U belongs in the marketing field or a persona (personality). The server device 100 then stores and manages the information (attribute information) about the attributes of the user U together with identification information (such as a user ID) that identifies the user U.

[0017] 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.

[0018] Furthermore, the number of devices included in the information processing system 1 shown in Fig. 1 is not limited to that shown in the figure. For example, in Fig. 1, 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.

[0019] [2. Utilizing Engaged Clicks] For example, as shown in FIG. 1, the server device 100 measures the time when an advertisement is clicked on a site in the company's own domain as the first time point (step S1).

[0020] Next, the server device 100 measures the time when the advertisement is displayed again on the company's own domain site as the second time point (step S2).

[0021] Next, the server device 100 calculates the time (difference) from the first time point to the second time point as the LP stay time (step S3).

[0022] Next, the server device 100 determines whether the LP stay time is equal to or greater than a threshold value (step S4).

[0023] Next, if the LP stay time is equal to or greater than the threshold, the server device 100 determines that it is an engaged click (EngagedClick) (step S5).

[0024] Next, the server device 100 uses the engaged click as auxiliary information before conversion (CV) and controls the distribution of advertisements (step S6).

[0025] As described above, in this embodiment, the server device 100 controls the delivery of advertisements (digital advertisements) by utilizing the LP dwell time and engaged clicks. For example, the server device 100 performs delivery control to optimize advertisement delivery. Alternatively, the server device 100 proposes to the advertiser an improvement method for optimizing advertisement delivery. Note that the advertisement may be interpreted as content. Furthermore, the advertiser may be interpreted as a poster. That is, the server device 100 may determine the LP dwell time and engaged clicks for content, such as news, SNS, videos, and live broadcasts, of external domains, in addition to advertisements. For example, the server device 100 may measure the time when a thumbnail (image) of content on a site of a specific domain is clicked as a first time point, and measure the time when the thumbnail of the content is displayed again on the site of the specific domain as a second time point. In this embodiment, an advertisement will be described as an example.

[0026] [2-1. Measuring the time spent on the landing page after clicking on an ad] After clicking an advertisement on a company's own domain site, the server device 100 transitions to a landing page (LP), which is a site on an external domain, and therefore cannot acquire user behavior. Therefore, as shown in FIG. 1, the server device 100 measures the time from clicking an advertisement on a company's own domain site to the advertisement being displayed again (impression) on the company's own domain site as the LP dwell time. Because the LP dwell time can be measured by the company as first-party data, the data can be utilized while protecting privacy.

[0027] [2-2. Cases where LP dwell time can / cannot be measured] [2-2-1. Measurable cases] One case in which the LP dwell time can be measured is when a user clicks an ad and then goes back on the browser. Note that "browser back" also includes cases where the user goes back by tracing the history. In this case, the ad impression occurs again at the time of the return, so it can be measured. For example, when a user clicks an ad and then goes back on the browser to access the delivery surface of the display ad again, the ad is displayed again. That is, by returning to the delivery surface of the display ad after clicking an ad, an ad request is generated again, and the ad is displayed again when the server device 100 returns the ad. The ad displayed again when the ad is clicked and the ad displayed again do not have to be the same ad. Note that, depending on the ad display control and delivery control, there may be a case in which an ad request is generated again when the delivery surface of the display ad becomes the active window again, causing the ad to be displayed again. Alternatively, the LP dwell time can be measured by considering the time when the delivery surface of the display ad becomes the active window again as the time when the ad is displayed again.

[0028] Another case in which LP dwell time can be measured is when a user re-accesses the display ad's delivery surface by a method other than clicking back on the browser after clicking an ad. For example, a user may click a link on the LP after clicking an ad (or use a shortcut link such as the favorites function, or return to the homepage / top page) to re-access the display ad's delivery surface. Another case involves a user clicking an ad and the LP being displayed in a separate screen (window), and then closing that separate screen triggering another ad request and displaying the ad again. The separate screen need not be a browser / app screen; it could also be a modal, pop-up, dialog, or similar. In this case, the end of the LP dwell time is determined when the user visits the company's domain, making it possible to measure the time. The screen on which the ad is re-impressed (impressed) does not have to be the original site from which the click occurred.

[0029] Furthermore, one case in which LP dwell time can be measured is when a user clicks on an ad on a display ad delivery surface using a first device, and then accesses the display ad delivery surface again using a second device. That is, after clicking on an ad, the user accesses the display ad delivery surface again on a different device where they have logged in with the same ID / associated ID. In this case, measurement is possible because the LP dwell time is determined to end when the display ad delivery surface is accessed again on a different device. In this way, measurement is possible even if the device is a different device, as long as the same user is logged in.

[0030] One case in which LP dwell time can be measured is when a user clicks on an ad on a display ad using a first browser / app, and then accesses the display ad again using a second browser / app. In other words, after clicking on an ad, the user accesses the display ad again using a different browser / app. In this case, the LP dwell time can be measured because it is determined to end when the user visits your company's domain using a different browser / app.

[0031] Additionally, one case in which LP dwell time can be measured is when a user clicks on an ad in the first tab of a tabbed browser, and then accesses the display ad again in a second tab of the tabbed browser. In other words, after clicking on an ad, the user accesses the display ad again in a different tab of the tabbed browser. In this case, the LP dwell time can be measured because it is determined that the user's dwell time ends when they visit your company's domain in a different tab of the tabbed browser.

[0032] [2-2-2. Cases where measurement is not possible] One case in which it is not possible to measure LP dwell time is when a user closes the app / browser on the LP (or beyond) after clicking an ad. Or, they may shut down or restart their device. Furthermore, there may be some kind of device trouble, communication failure, or other reason that blocks communication and makes it impossible to access the display ad delivery surface again. In this case, the ad impression will not be displayed again, so it cannot be measured.

[0033] [2-3. Definition of Engaged Click] The server device 100 uses a click whose LP dwell time is equal to or greater than a threshold as an engaged click (EngagedClick). For example, the server device 100 determines a click whose LP dwell time is 15 seconds or more as an engaged click (EngagedClick). This is because, when the applicant investigated the relationship between the LP dwell time measured by the applicant's display advertising service and the conversion rate (CVR), a tendency for the rate to increase from around 15 seconds was observed.

[0034] However, since the definition of an engaged click varies depending on the advertising medium and type of advertisement, the 15-second threshold can be changed depending on the advertising medium and type of advertisement. In other words, the threshold for LP dwell time can be set arbitrarily and can be changed as appropriate.

[0035] [2-4. Overview of Engaged Click] Figure 2 is an explanatory diagram showing an overview of conventional conversion optimization logic. As shown in Figure 2, most of the previous conversion optimization logic was constructed mainly based on information at the time of delivery and labels indicating whether or not there was a conversion. Examples of conversion optimization logic include a conversion prediction model for delivery that maximizes conversion, and an estimation and imputation model for missing conversions.

[0036] Regarding whether or not a conversion occurred, the majority of data indicates "no conversion," but this includes both users who considered the ad destination for a long time and users who dropped out after a short time. However, as shown in FIG. 2, in the past, no distinction was made between users who considered the ad destination for a long time and users who dropped out after a short time, and information about users who considered the ad destination for a long time, such as "they considered until the very last moment before conversion, but did not convert," was not effectively utilized. In order to understand users who considered the ad destination for a long time, it is desirable to effectively utilize information such as "they considered until the very last moment before conversion, but did not convert." Therefore, in this embodiment, as shown in FIG. 3, engaged clicks (EngagedClicks) are used as an indicator of whether a user stayed at the destination for a sufficient amount of time, as auxiliary information before conversion, for ad delivery control (ad delivery optimization). FIG. 3 is an explanatory diagram showing an overview of engaged clicks (EngagedClicks).

[0037] [2-5. Utilizing LP Dwell Time / Engaged Clicks] [2-5-1. CVR Prediction] In CVR prediction, the server device 100 uses the LP dwell time as training data to assist the CV, which is the correct label. FIG. 4 is an explanatory diagram showing an overview of CVR prediction according to an embodiment. For example, as shown in FIG. 4, the server device 100 trains a machine learning model to simultaneously predict the LP dwell time in addition to the CV that is the intended prediction (this is called multi-task learning). The server device 100 also predicts engaged clicks from the LP dwell time predicted using the model. In this way, by utilizing the LP dwell time prediction as an auxiliary task, the performance (accuracy) of CVR prediction can be improved.

[0038] For example, as shown in FIG. 4, the server device 100 trains a prediction model using a data set of feature amounts at the time of advertisement distribution, the presence or absence of conversions, and the duration of time spent on the LP as training data (step S11).

[0039] At this time, the server device 100 may train the prediction model using a data set including the feature amount at the time of advertisement distribution, whether or not there was a conversion, the time spent on the LP, and information indicating whether or not the click was engaged as learning data.

[0040] The feature quantities at the time of advertisement distribution include user attributes, past user performance, advertisement features, and distribution surface features. For example, the feature quantities at the time of advertisement distribution include at least one of user attributes, past user performance, advertisement features, and distribution surface features. Note that the above are merely examples of feature quantities. In practice, the feature quantities are not limited to these examples.

[0041] Next, the server device 100 inputs the features at the time of advertisement delivery into the trained prediction model, performs CV prediction as a primary task, and obtains a predicted CV result, and also performs LP dwell time prediction as an auxiliary task, and obtains a predicted LP dwell time result (step S12).

[0042] At this time, the server device 100 may perform engaged click prediction as one of the auxiliary tasks and obtain information indicating whether or not the click is an engaged click.

[0043] Next, the server device 100 determines whether or not the click is an engaged click based on the predicted LP stay time (the predicted result of the LP stay time) (step S13). Note that if the server device 100 has already obtained information indicating whether or not the click is an engaged click, it does not need to perform this process. In other words, this process can be omitted.

[0044] Next, the server device 100 controls the distribution of advertisements by utilizing the predicted conversions and the predicted LP stay times (step S14).

[0045] At this time, the server device 100 may control delivery to optimize ad delivery by utilizing engaged clicks as auxiliary information before conversion. For example, the server device 100 may predict whether there will be a conversion with an engaged click, a conversion without an engaged click, no conversion with an engaged click, or no conversion without an engaged click, and control ad delivery in accordance with the prediction result. Alternatively, the server device 100 may control ad delivery so as to extend the predicted LP dwell time only when there is no engaged click.

[0046] [2-5-2. CV Estimation] The server device 100 estimates conversions that occurred after ad delivery but were not measured due to privacy concerns, and supplements the report. Therefore, the server device 100 can use information obtained after ad delivery to estimate conversions. FIG. 5 is an explanatory diagram showing an overview of conversion estimation according to an embodiment. For example, as shown in FIG. 5, the server device 100 estimates conversions that would have occurred by using the time spent on a landing page as a feature after the ad delivery. In this way, the performance of conversion estimation can be improved by utilizing the time spent on a landing page as a feature.

[0047] For example, as shown in FIG. 5, the server device 100 trains a CV estimation model using a data set of features at the time of advertisement distribution, the presence or absence of CV, and the LP stay time as training data (step S21).

[0048] Next, the server device 100 inputs the feature amount at the time of advertisement distribution and the LP stay time into the trained CV estimation model, performs missing CV estimation, and estimates the CV that is presumed to have occurred (step S22).

[0049] At this time, the server device 100 may determine whether or not the click is an engaged click based on the input LP dwell time, and may estimate the CV by utilizing the engaged click as auxiliary information before the CV in estimating the missing CV. For example, the server device 100 may estimate the CV as the CV in the case of an engaged click or the CV in the case of a non-engaged click based on the input LP dwell time.

[0050] Next, the server device 100 supplements a report for the advertiser based on the estimated CV (step S23).

[0051] [2-5-3. Targeting] Existing targeting methods (clicks, conversions) that use advertising performance data each have the following drawbacks. For example, click targeting has the drawback of including users with low interest, such as accidental clicks. Also, conversion targeting has the drawback of taking time to accumulate a target list. Another drawback is that it cannot include users who have not yet converted.

[0052] Engaged Clicks play an intermediate role, allowing you to target many users while narrowing down to users with a certain level of interest. For example, an Engaged Click indicates a long stay on the landing page (amounting to a threshold), so it is assumed that the user is highly interested. In a sense, the length of time spent on the landing page (the magnitude of the value) can be said to indicate the user's level of interest. Furthermore, by controlling the threshold for the landing page stay time used to determine an Engaged Click, you can expect to achieve a trade-off between the level of interest and the number of potential users (potential number) that can be delivered.

[0053] That is, the server device 100 presumes that a user who has engaged a click is a user who has a high degree of interest in the advertisement, and controls the distribution of the advertisement.

[0054] [2-6. Another perspective] From another perspective, the server device 100 according to this embodiment measures the time it takes for a user to transition from the first domain to the second domain via the first content and then return to the first domain, thereby measuring the time spent in the second domain. Furthermore, the server device 100 trains a model that estimates a score indicating whether a predetermined behavior will be performed on an object corresponding to the second domain, taking into account the measurement result (time spent). The server device 100 then estimates a score using the trained model and provides information about the object according to the estimated score. The information about the object is, for example, an advertisement. The score estimation is, for example, a CVR estimation. The user selects a link for a second content displayed in the first content in the first domain to transition to the second content in the second domain. The first content and the second content are in different domains.

[0055] The server device 100 also trains the model so that it outputs a score when a measurement result is input. The server device 100 also trains the model so that it estimates the measurement result along with the score. The server device 100 also trains the model so that the longer the stay time, the higher the score.

[0056] The server device 100 may implement the mechanism according to this embodiment using AI (Artificial Intelligence) such as GPT (Generative Pre-trained Transformer). GPT is a text generation AI and a language model capable of generating sentences using natural language processing.

[0057] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of the configuration of the terminal device 10 according to the embodiment. As shown in Fig. 6, 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.

[0058] (Communications Department 11) The communication unit 11 is connected to the network N 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.

[0059] (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.

[0060] (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.

[0061] (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).

[0062] 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.

[0063] (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.

[0064] (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.

[0065] (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.

[0066] (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.

[0067] 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.

[0068] (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. 6, 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] (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, 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.

[0076] (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.

[0077] (Receiving unit 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.

[0078] (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.

[0079] (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.

[0080] [4. Server device configuration example] Next, the configuration of the server device 100 according to the embodiment will be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of the configuration of the server device 100 according to the embodiment. As shown in Fig. 7, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0081] (Communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is connected to a network N by wire or wirelessly.

[0082] (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. The storage unit 120 may store attribute information and history information (log data) of the user U together with identification information (such as a user ID) indicating the user U.

[0083] (control unit 130) The control unit 130 is a controller, and is realized by, for example, a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or the like, by 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. 7, the control unit 130 has an acquisition unit 131, a measurement unit 132, a calculation unit 133, a determination unit 134, and a distribution control unit 135.

[0084] (Acquisition part 131) The acquisition unit 131 acquires a search query input by a 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.

[0085] 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 the user U is registered. Then, the acquisition unit 131 stores the user information in the storage unit 120.

[0086] 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 stores the various types of history information in the storage unit 120.

[0087] (Measurement unit 132) The measurement unit 132 measures the time when an advertisement is clicked on a site of a specific domain (such as a company's own domain) as a first time point. The measurement unit 132 also measures the time when an advertisement is displayed again on a site of the specific domain as a second time point. The measurement unit 132 may also have a function as a reception unit that receives clicks by the user U. The measurement unit 132 may also have a function as a display control unit that controls the display of a site of the specific domain.

[0088] For example, the measurement unit 132 measures the time when the user U clicks on an advertisement on a site of a specific domain as a first time point. Also, the measurement unit 132 measures the time when the user U goes back to the browser after clicking on the advertisement and the advertisement is displayed again on the site of the specific domain as a second time point.

[0089] The measurement unit 132 measures the time when the user U clicks on an advertisement on a site of a specific domain as a first time point. The measurement unit 132 measures the time when the user U clicks on a link on a site of another domain to which the user U transitions after clicking on the advertisement (or uses a shortcut link such as a favorites function, or performs an operation to return to the home page / top) and the advertisement is displayed again on the site of the specific domain as a second time point.

[0090] The measurement unit 132 measures the time when the user U clicks on an advertisement on a site of a specific domain using a first device as a first time point, and the measurement unit 132 measures the time when the user U uses a second device and the advertisement is displayed again on a site of a specific domain as a second time point.

[0091] The measurement unit 132 measures the first time point when the user U clicks on an advertisement on a site of a specific domain using a first browser or app, and measures the second time point when the user U uses a second browser or app and the advertisement is displayed again on a site of a specific domain.

[0092] The measurement unit 132 measures the time when the user U clicks on the advertisement on the site of the specific domain as the first time point. The measurement unit 132 measures the time when the user U closes the separate screen that was displayed after clicking on the advertisement and the advertisement is displayed again on the site of the specific domain as the second time point.

[0093] (Calculation unit 133) The calculation unit 133 calculates the time (difference) from the first time point to the second time point as the LP dwell time. That is, the calculation unit 133 measures the time from when an advertisement is clicked on a site of a specific domain to when the advertisement is displayed again (impression) on a site of the specific domain as the LP dwell time. Because the LP dwell time can be measured in-house as first-party data, the data can be used while protecting privacy.

[0094] (Judgment unit 134) The determination unit 134 determines whether the LP dwell time is equal to or greater than a threshold, and if the LP dwell time is equal to or greater than the threshold, determines it to be an engaged click. For example, the server device 100 determines a click whose LP dwell time is 15 seconds or more to be an engaged click (EngagedClick). When the relationship between the LP dwell time measured by our company and CVR was investigated, a tendency for it to increase from around 15 seconds was observed.

[0095] (Distribution control unit 135) The delivery control unit 135 uses the engaged click as auxiliary information before conversion to control the delivery of advertisements. That is, the delivery control unit 135 utilizes the engaged click to perform delivery control for optimizing the delivery of advertisements.

[0096] For example, the delivery control unit 135 trains a prediction model on a set of feature amounts at the time of delivery of an advertisement, whether or not there is a conversion, and the duration of time spent on an LP, inputs the feature amounts at the time of delivery of the advertisement into the prediction model, predicts conversions and the duration of time spent on an LP as outputs of the prediction model, and controls delivery of advertisements based on the prediction results. That is, the delivery control unit 135 has a function as a learning unit that trains the prediction model, which is a machine learning model. Note that the function as the learning unit may be independent from the delivery control unit 135.

[0097] Furthermore, the delivery control unit 135 trains an estimation model on pairs of feature amounts at the time of delivery of an advertisement, the presence or absence of conversion, and the duration of time spent on the LP, inputs the feature amounts at the time of delivery of the advertisement and the duration of time spent on the LP as feature amounts after delivery of the advertisement into the estimation model, estimates conversions that have not been measured but are estimated to have occurred originally as an output of the estimation model, and complements a report to the advertiser based on the estimation results. Note that the delivery control unit 135 may be a providing unit that provides a report to the advertiser via the communication unit 110.

[0098] The feature amount at the time of advertisement distribution includes at least one of the user U attributes, the past performance of the user U, the advertisement feature, and the distribution surface feature.

[0099] Furthermore, the delivery control unit 135 presumes that the user U who has engaged clicked is a user U who has a high degree of interest in the advertisement, and controls the delivery of the advertisement.

[0100] [5. Processing Procedure] Next, a processing procedure by the server device 100 according to the embodiment will be described with reference to Fig. 8. Fig. 8 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.

[0101] For example, as shown in FIG. 8, the measurement unit 132 of the server device 100 measures the first time point when the user U clicks on an advertisement on a site of a specific domain (such as the user's own domain) displayed on the user U's terminal device 10 (step S101).

[0102] Next, the measurement unit 132 of the server device 100 measures the second time point when the advertisement is displayed again on the specific domain site on the terminal device 10 of the user U due to the operation of the user U after clicking on the advertisement (step S102).

[0103] Next, the calculation unit 133 of the server device 100 calculates the time (difference) from the first time point to the second time point as the LP stay time (step S103).

[0104] Next, the determining unit 134 of the server device 100 determines whether the LP stay time is equal to or greater than a threshold value (step S104).

[0105] At this time, if the LP stay time is equal to or greater than the threshold value (step S104; Yes), the determining unit 134 of the server device 100 determines that it is an engaged click (step S105).

[0106] On the other hand, if the LP stay time is less than the threshold (step S104; No), the determining unit 134 of the server device 100 determines that the click is a non-engaged click (step S106).

[0107] Next, the delivery control unit 135 of the server device 100 uses the information indicating the engaged click or the non-engaged click as auxiliary information before conversion, and controls the delivery of the advertisement (step S107).

[0108] [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.

[0109] 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 (or an application running on 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 cooperates with 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.

[0110] Furthermore, in the above embodiment, the server device 100 determines whether the LP dwell time is equal to or greater than a threshold. However, in this case, multiple thresholds may be set in stages to determine engaged clicks (EngagedClicks) in multiple stages. For example, the server device 100 may set a "first threshold < second threshold < third threshold" and determine that if the LP dwell time is equal to or greater than the first threshold but less than the second threshold, it is a first engaged click; if the LP dwell time is equal to or greater than the second threshold but less than the third threshold, it is a second engaged click; and if the LP dwell time is equal to or greater than the third threshold, it is a third engaged click. This makes it possible to further distinguish engaged clicks depending on the length of the LP dwell time, i.e., the length of the user's consideration time.

[0111] In the above embodiment, the server device 100 measures the time when an advertisement is clicked on a site in the company's own domain as the first time point, and measures the time when the advertisement is displayed again on the site in the company's own domain as the second time point, but in reality, the time point may be a site other than the company's own domain. For example, the server device 100 may be requested or entrusted by another company to measure the time when an advertisement is clicked on a site in a domain under its management (managed domain) or a domain under its monitoring (monitored domain) as the first time point, and measure the time when the advertisement is displayed again on the site in the managed domain or monitored domain as the second time point, and calculate the time from the first time point to the second time point as the LP stay time.

[0112] In the above embodiment, the server device 100 may determine that the user is no longer considering the product after a certain period of time (for example, one hour) has elapsed since the time of clicking an advertisement on a website in the company's own domain was measured as the first time point, reset the measurement result, and initialize the first time point without measuring the second time point. In other words, the measurement may be restarted from the beginning.

[0113] Furthermore, in the above embodiment, the server device 100 may measure the time when an advertisement is clicked on a website in the company's domain as a first time point, and then, after a certain time (e.g., 10 minutes) has elapsed since the user opened and started browsing another website unrelated to the consideration of the advertisement / launched and started operating another app, determine that the user is no longer considering the advertisement, reset the measurement result, and initialize the first time point without measuring the second time point. In other words, the measurement may be restarted from the beginning.

[0114] Furthermore, in the above embodiment, the server device 100 may determine the landing page dwell time and engaged clicks for apps other than advertisements. For example, the server device 100 may measure the time when the app icon is clicked on the main screen of the user U's terminal device 10 as a first time point, and the time when the app icon is displayed again on the main screen as a second time point. Note that the app may be a mini-app that operates based on a super app. For example, a case may be considered in which a transition from the screen of one app to the screen of another app is followed by a return to the original app screen.

[0115] [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 measurement unit 132 that measures the time when an advertisement is clicked on a site of a specific domain as a first time point, and measures the time when the advertisement is displayed again on the site of the specific domain as a second time point; a calculation unit 133 that calculates the time from the first time point to the second time point as LP dwell time; a determination unit 134 that determines whether the LP dwell time is equal to or greater than a threshold, and if the LP dwell time is equal to or greater than the threshold, determines that it is an engaged click; and a delivery control unit 135 that uses the engaged click as auxiliary information just before conversion and controls the delivery of advertisements.

[0116] This allows for improved conversion rates by taking into account the user's consideration time. Furthermore, the length of time a user spends on a landing page can be used to indicate the user's consideration time. Furthermore, by treating clicks by users who have considered for a certain period of time or more as engaged clicks, it is possible to distinguish between users who have considered for a long time after clicking an ad and users who have left in a short period of time, regardless of whether they have converted or not.

[0117] The measurement unit 132 measures the time when a user clicks on an advertisement on a site of a specific domain as a first time point, and measures the time when the user goes back to the browser after clicking on the advertisement and the advertisement is displayed again on the site of the specific domain as a second time point.

[0118] This means that in cases where the user clicks on an ad and then goes back to the display screen, the second point in time can be measured at the time when the ad impression occurs again after returning to the display screen, and the LP dwell time can be calculated.

[0119] The measurement unit 132 measures the time when a user clicks on an advertisement on a site of a specific domain as a first time point, and measures the time when the user clicks on a link on a site of another domain to which the user transitions after clicking on the advertisement and the advertisement is displayed again on the site of the specific domain as a second time point.

[0120] This means that in cases where the distribution space is accessed again by a method other than the browser back button after clicking an ad, the second point in time can be measured at the time the distribution space is accessed and the ad is displayed again (impression), and the LP dwell time can be calculated.

[0121] The measurement unit 132 measures the first time point when a user clicks on an advertisement on a site of a specific domain using a first device, and measures the second time point when the user uses a second device and the advertisement is displayed again on a site of the specific domain.

[0122] This means that in cases where the distribution surface is accessed again on a different device after clicking on an ad, the second point in time can be measured when the distribution surface is accessed on a different device where the user is logged in and the ad is displayed again (impression), and the LP dwell time can be calculated.

[0123] The measurement unit 132 measures the first time point when a user clicks on an advertisement on a site of a specific domain using a first browser or app, and measures the second time point when the user uses a second browser or app and the advertisement is displayed again on a site of a specific domain.

[0124] This means that in cases where the distribution surface is accessed again using a different browser or app after clicking on an ad, the second point in time can be measured at the time when the distribution surface is accessed using a different browser or app and the ad is displayed again (impression), and the LP dwell time can be calculated.

[0125] The measurement unit 132 measures the time when a user clicks on an advertisement on a site of a specific domain as a first time point, and measures the time when the user closes another screen that is displayed after clicking on the advertisement and the advertisement is displayed again on the site of the specific domain as a second time point.

[0126] This means that in cases where the separate screen is closed after clicking an ad and the delivery surface is displayed again, the second point in time can be measured at the time when the separate screen is closed, the delivery surface is displayed, and the ad impression occurs again, and the LP dwell time can be calculated.

[0127] The distribution control unit 135 trains a prediction model on the combination of feature values ​​at the time of advertisement distribution, whether or not there is a conversion, and the duration of time spent on the LP, inputs the feature values ​​at the time of advertisement distribution into the prediction model, predicts conversion as an output of the prediction model, and predicts the duration of time spent on the LP, and controls the distribution of advertisements based on the prediction results.

[0128] This allows conversion prediction to be performed as the primary task and LP dwell time prediction to be performed as a secondary task in CVR prediction. The predicted LP dwell time can also be used to determine engaged clicks.

[0129] The distribution control unit 135 trains an estimation model on pairs of feature values ​​at the time of advertisement distribution, whether or not a conversion occurred, and duration of dwell time on the LP, inputs the feature values ​​at the time of advertisement distribution and the duration of dwell time on the LP as feature values ​​after advertisement distribution into the estimation model, estimates conversions that were not measured but are assumed to have occurred originally as output of the estimation model, and complements reports to advertisers based on the estimation results.

[0130] This allows you to supplement your reports by estimating conversions that occurred after ad delivery but were not measured for privacy reasons.

[0131] The feature amount at the time of advertisement distribution includes at least one of user attributes, past performance of the user, advertisement features, and distribution surface features.

[0132] This allows predictions and estimations to be made based on feature quantities such as user attributes, past user performance, advertisement features, and distribution surface features.

[0133] The delivery control unit 135 presumes that a user who has engaged a click is a user who has a high degree of interest in the advertisement, and controls delivery of the advertisement.

[0134] This allows for the use of engaged clicks to estimate which users are highly interested in the advertisement.

[0135] By performing any one or a combination of the above-described processes, the information processing device according to the present application can improve the conversion rate by taking into account the user's consideration time.

[0136] [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. 9, for example. The following description will be given taking the server device 100 as an example. Fig. 9 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.

[0137] 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), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0151] 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]

[0152] 1. Information Processing Systems 10 Terminal Equipment 100 Server device 110 Communications Department 120 Storage section 130 Control Unit 131 Acquisition Department 132 Measurement section 133 Calculation Unit 134 Judgment section 135 Distribution control section

Claims

1. a measurement unit that measures a time when an advertisement is clicked on a site of a specific domain as a first time point, and measures a time when the advertisement is displayed again on the site of the specific domain as a second time point; a calculation unit that calculates a time from the first time point to the second time point as an LP stay time; a determination unit that determines whether the LP stay time is equal to or greater than a threshold, and determines that the click is an engaged click if the LP stay time is equal to or greater than the threshold; a delivery control unit that uses the engaged click as auxiliary information before conversion and controls delivery of advertisements; An information processing device comprising:

2. The measurement unit measures the time when the user clicks on the advertisement on the site of the specific domain as the first time point, and measures the time when the user returns to the browser after clicking on the advertisement and the advertisement is displayed again on the site of the specific domain as the second time point.

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

3. The measurement unit measures the time when the user clicks on the advertisement on the site of the specific domain as the first time point, and measures the time when the user clicks on a link on a site of another domain to which the user is redirected after clicking on the advertisement and the advertisement is displayed again on the site of the specific domain as the second time point.

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

4. The measurement unit measures the time when the user clicks on the advertisement on the site of the specific domain using a first device as the first time point, and measures the time when the user uses a second device and the advertisement is displayed again on the site of the specific domain as the second time point.

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

5. The measurement unit measures the first time point when the user clicks on an advertisement on a site of the specific domain using a first browser or app, and measures the second time point when the user uses a second browser or app and the advertisement is displayed again on the site of the specific domain.

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

6. The measurement unit measures the time when the user clicks on the advertisement on the site of the specific domain as the first time point, and measures the time when the user closes the separate screen displayed after clicking on the advertisement and the advertisement is displayed again on the site of the specific domain as the second time point.

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

7. The distribution control unit trains a prediction model on a set of feature amounts at the time of distribution of the advertisement, whether or not there is a conversion, and the LP stay time, inputs the feature amounts at the time of distribution of the advertisement to the prediction model, predicts conversion as an output of the prediction model, and predicts the LP stay time, and controls distribution of the advertisement based on the prediction results.

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

8. The distribution control unit trains an estimation model on a set of feature amounts at the time of distribution of the advertisement, whether or not a conversion occurred, and the LP stay time, inputs the feature amounts at the time of distribution of the advertisement and the LP stay time as feature amounts after distribution of the advertisement into the estimation model, estimates conversions that have not been measured but are presumed to have occurred, as an output of the estimation model, and complements a report to the advertiser based on the estimation results.

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

9. The feature quantity at the time of distribution of the advertisement includes at least one of a user attribute, a user's past performance, an advertisement feature, and a distribution surface feature.

9. The information processing device according to claim 7, wherein:

10. The delivery control unit estimates that the user who has engaged clicked is a user who has a high degree of interest in the advertisement, and controls delivery of the advertisement.

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

11. An information processing method executed by an information processing device, a measuring step of measuring a time when an advertisement is clicked on a site of a specific domain as a first time point, and measuring a time when the advertisement is displayed again on the site of the specific domain as a second time point; a calculation step of calculating a time from the first time point to the second time point as an LP stay time; a determining step of determining whether the LP stay time is equal to or greater than a threshold, and determining that the click is an engaged click if the LP stay time is equal to or greater than the threshold; a delivery control step of controlling delivery of advertisements by using the engaged clicks as auxiliary information before conversion; An information processing method comprising:

12. a measurement procedure for measuring a time when an advertisement is clicked on a site of a specific domain as a first time point, and measuring a time when the advertisement is displayed again on the site of the specific domain as a second time point; a calculation step of calculating a time from the first time point to the second time point as an LP stay time; a determination step of determining whether the LP stay time is equal to or greater than a threshold, and determining that the click is an engaged click if the LP stay time is equal to or greater than the threshold; a delivery control procedure for controlling delivery of advertisements by using the engaged clicks as auxiliary information before conversion; An information processing program characterized by causing a computer to execute the above.

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