JP2021128723A - Information processing device, information processing method, and information processing program - Google Patents

Abstract

To evaluate quality of parts constituting content.SOLUTION: An information processing device: creates vector data for a whole screen image of content used by a user; inputs the vector data for the whole screen image of content into a model and causes the model to learn an absence time as correct answer data by deep learning, the absence time, for example, being a period lasting from when the user uses content to when the user reuses the same content; inputs the vector data for the whole screen image of content into the learned model and acquires the absence time; and calculates an average value of the acquired absence time as a quality score of parts.SELECTED DRAWING: Figure 1

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

A technique for increasing the viewing rate of highly interesting or topical content is disclosed. For example, among multiple contents delivered to the network, for each of the similar contents that are similar to each other, a score is calculated based on the click rate and the passage of time from the delivery, and the latest similar contents among the similar contents are calculated. A technique is disclosed in which the scores of other similar contents are added to the scores of the contents, and the presentation order of the similar contents is controlled based on the scores of the similar contents.

Japanese Unexamined Patent Publication No. 2019-105942

However, in the above-mentioned prior art, the score is only calculated based on the click rate and the passage of time from the delivery for each of the similar contents that are similar to each other. The content contains a plurality of components, including those actually clicked by the user and those not clicked. In addition, even if the user actually clicks, the one clicked again when the user revisits and the one not clicked again are included. However, it cannot be said that the above-mentioned prior art evaluates the quality of the components of the content.

The present application has been made in view of the above, and an object of the present application is to evaluate the quality of the components of the content.

The information processing device according to the present application has a creation unit that creates vector data of a screen image of the content used by the user, and the user uses the content and then uses the content again from the vector data. It is characterized by including a learning unit for training a model to output withdrawal period information indicating a withdrawal period up to, and a calculation unit for calculating a quality score of a component of the content from the withdrawal period information.

According to one aspect of the embodiment, the quality of the components of the content can be evaluated.

FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. FIG. 2 is an explanatory diagram showing an outline of a method of acquiring a quality score of a component of a content. FIG. 3 is a diagram showing a configuration example of the information processing system according to the embodiment. FIG. 4 is a diagram showing a configuration example of the information processing device according to the embodiment. FIG. 5 is a diagram showing an example of a user database. FIG. 6 is a diagram showing an example of an image vector database. FIG. 7 is a diagram showing an example of a quality score database. FIG. 8 is a flowchart showing a processing procedure according to the embodiment. FIG. 9 is a diagram showing an example of a hardware configuration.

Hereinafter, the information processing apparatus according to the present application, the information processing method, and a mode for carrying out the information processing program (hereinafter, referred to as “the embodiment”) will be described in detail with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present application are not limited by this embodiment. Further, in the following embodiments, the same parts are designated by the same reference numerals, and duplicate description is omitted.

[1. Information processing method overview]
First, with reference to FIG. 1, an outline of an information processing method performed by the information processing apparatus according to the embodiment will be described. FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. In FIG. 1, a case where a quantitative score is given to a component of the content will be described as an example.

As shown in FIG. 1, the information processing system 1 includes a terminal device 10, an information processing device 100, and an information providing device 200. The terminal device 10, the information processing device 100, and the information providing device 200 are connected to each other via a network N (see FIG. 3) so as to be able to communicate with each other by wire or wirelessly.

The terminal device 10 is a smart device such as a smartphone or tablet used by the user U, and communicates with an arbitrary server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). It is an information processing device that can be used. Further, the terminal device 10 is a screen of a liquid crystal display or the like and has a screen having a touch panel function. Accepts various operations. The operation performed on the area of the screen on which the content is displayed may be an operation on the content. Further, the terminal device 10 may be an information processing device such as a desktop PC (Personal Computer) or a notebook PC as well as a smart device.

The information providing device 200 is an information processing device that provides contents to the terminal device 10 of the user U, and is realized by a server device, a cloud system, or the like. The number of information providing devices 200 may be plurality. In addition, the information providing device 200 provides a search function for searching content such as a web page, a website, and / or an image file existing on the Internet in response to a search request from the terminal device 10 of the user U. It may be a server (search engine). For example, the information providing device 200 may provide a search site including a portal site or the like. Here, when the terminal device 10 or the like accesses the information providing device 200, the information providing device 200 manages the state between the information providing device 200 and the web browser from the terminal device 10 or the like via the web browser. You may acquire the communication protocol, or the HTTP Cookie which is the data stored in the web browser used therein. Further, the information providing device 200 may be a relay server (Proxy), a redirector (Redirector), or the like.

The information processing device 100 is an information processing device that gives a quantitative score (quality score) to the components of the content, and is realized by a server device, a cloud system, or the like. The information processing device 100 may be one of the information providing devices 200. That is, the information processing device 100 may also be an information processing device that provides content.

For example, the user U uses the terminal device 10 to use various contents provided by each information providing device 200 (step S1).

Each information providing device 200 aggregates the accesses from the terminal device 10 of each user U and generates an access log of each user U (step S2).

The information processing device 100 acquires the access log of each user U from each information providing device 200 (step S3).

At this time, the information processing apparatus 100 may acquire only the access log of the user U who has consented to the use of the access log in advance. For example, the information processing device 100 may acquire only the access log of the user U registered in advance.

When the information processing device 100 itself provides the content to the terminal device 10 of each user U, the information processing device 100 can generate an access log of each user U regarding the content provided by the information processing device 100. ..

The information processing device 100 converts an image (image) of the entire screen of the content into vector data based on the access log of each user U (step S4).

In the example shown in FIG. 1, a WEB search result page is shown as an example of the content. The layout of this WEB search result page is only an example. The information processing device 100 converts the image of the entire WEB search result page into vector data as shown in FIG. 1 from the access log of each user U.

[1-1. Vector data conversion of the image of the entire screen of the content]
As shown in FIG. 1, a "search window" is arranged as a part (part) constituting the content at the position "0" corresponding to the top of the WEB search result page. The parts that make up the content are examples of the components of the content. Here, "Kyoto sightseeing" is entered as a search query (search keyword) in the search window. That is, the WEB search result page shows the search results related to "Kyoto sightseeing".

In addition, "DQNN vector" is added as incidental information. The DQNN vector is an embedded vector that reflects the search intention of the user U. For example, the DQNN vector is a plurality of search queries, and if the user's intentions at the time of input are similar, a similar vector is output, and if the intentions are not similar, a dissimilar vector is output. It is a vector generated from the search query input by the user using the trained model. Further, the DQNN vector may be a distributed representation. Further, as an action of the user U, "Click: Yes" indicating the presence or absence of a click is shown. That is, it indicates that the search query has been entered in the search window and the search has been performed.

The operation of "Click: Yes" is not limited to pressing the search button displayed next to the search window, but the enter key (Enter) on the keyboard or the like while the search query is entered in the search window. The key) may be pressed. Also, click may be read as tap.

Further, a "sponsored search (SS)" is arranged as a part at the position "1" corresponding to the second row of the WEB search result page. "Sponsored search (SS)" indicates a listing advertisement (search-linked advertisement).

In addition, "LP domain" is added as incidental information. The LP domain indicates the domain of the landing page (LP) of the listing advertisement. The domain may be a subdomain, a URL (Uniform Resource Locator), or the like. Further, as an action of the user U, "no click (Click: No)" indicating the presence or absence of a click is shown. That is, it indicates that the listing advertisement displayed as sponsored search (SS) was not viewed.

Further, a "direct display (DD)" is arranged as a part at the position "2" corresponding to the third row of the WEB search result page. The "direct display (DD)" is a display of search results directly on the screen. For example, when a search is performed by inputting a place name, a map is directly displayed, and when a talent name is used, an image search result is directly displayed. Here, as a search result for the search query "Kyoto sightseeing", images and names of sightseeing spots (famous places, etc.) in Kyoto are displayed on the screen. The image may be a moving image.

In addition, "local" is given as incidental information. In addition, local indicates information originating from the area, tourist information, and the like. Further, as an action of the user U, "Click: Yes" indicating the presence or absence of a click is shown. That is, it indicates that the search result displayed directly on the screen as a direct display (DD) has been browsed.

Further, the "algorithm search result (Algo)" is arranged as a part at the position "3" corresponding to the fourth row (bottom row) of the WEB search result page. "Algorithm search result (Algo)" indicates the search result obtained by the algorithm of the search engine. That is, it is an area for displaying so-called general search results.

In addition, "LP domain" is added as incidental information. The LP domain indicates the domain of the landing page (LP) of the search result. The domain may be a subdomain, a URL (Uniform Resource Locator), or the like. Further, as an action of the user U, "Click: Yes" indicating the presence or absence of a click is shown. That is, it indicates that one of the links displayed as the search result is selected and the linked page is browsed.

Then, the information processing apparatus 100 inputs vector data of an image of the entire screen of the content into the model, and learns the content withdrawal period (Absence Time) as correct answer data by deep learning (deep learning) (step S5). ).

Any type of model can be adopted as the model. For example, the information processing apparatus 100 may use an SVM (Support Vector Machine) or a DNN (Deep Neural Network) as a model. Here, the DNN may be a CNN (Convolutional Neural Network) or an RNN (Recurrent Neural Network). Further, the RNN may be an LSTM (Long short-term memory) or the like. That is, any type of model can be adopted as the model. Further, the model may be a model realized by combining a plurality of models, for example, a model in which a CNN and an RNN are combined.

Further, in the present embodiment, the information processing apparatus 100 sets an interval (interval) from the end of use of the content (target content) including the part (target part) for which the quality score is to be known to the start of the next use of the content. Defined as withdrawal period. The withdrawal period is not limited to the time, and may be, for example, the number of contents used by the user U or the number of other parts from the time of using the latest target content to the time of using the next target content. .. That is, the withdrawal period is the withdrawal period information indicating the period from the use of the target content by the user U to the re-use of the target content.

Unless the user U next uses the target content, the withdrawal period will continue to increase. If the user U never uses the target content again, the withdrawal period approaches infinity. In this case, an upper limit may be set for the withdrawal period. For example, when the withdrawal period reaches a predetermined threshold value, the information processing device 100 fixes the withdrawal period to the threshold value and determines that the user U has completely withdrawn from the target content (is no longer used). You may do so. Further, the binary classification may be performed depending on whether the user U is not using the target content next or not.

In addition, the withdrawal period is an index indicating whether or not the user U is satisfied / attractive with the target content including the target part. For example, when the withdrawal period is short compared to the relative / predetermined threshold value, the user U is actively using the target content, and the user U is satisfied / attractive with the target content. It is presumed that you are feeling. On the contrary, when the withdrawal period is long, it is presumed that the user U does not use the target content so much and the user U is not dissatisfied / attractive with the target content. That is, the withdrawal period can also be index information that serves as an index of the user's impression of the target content.

[1-2. How to get the quality score of the content component]
A method of acquiring a quality score of a component of the content will be described with reference to FIG. FIG. 2 is an explanatory diagram showing an outline of a method of acquiring a quality score of a component of a content.

For example, as shown in FIG. 2, when the WEB search result page is used as the content, the information processing apparatus 100 inputs a set of vector data into the model for the image vector of the entire screen of the content, and correctly answers the content withdrawal period. Learn as data. Then, the information processing apparatus 100 inputs the vector data including the part for which the quality score is to be acquired into the trained model, and acquires the estimated withdrawal period. For example, if you want to obtain the quality score of the part "Direct Display (DD)" placed at the position "2" corresponding to the third row of the WEB search result page, generate an image vector of the entire screen of the content including that part. And enter. Specifically, a certain percentage of the training data is set aside as test data in advance, and a set of vector data including the parts for which the quality score is to be obtained is input to the model from the test data and output. The average value of the withdrawal period is the quality score. In addition, instead of extracting a set of data from the test data, a method of generating a plurality of data in which parts other than the target parts are randomly input and estimating based on the data may be used. In addition, the data used for learning may be used as the data to be input when acquiring the quality score. The average value may be another method such as the median value.

For example, the information processing apparatus 100 may use the acquired withdrawal period as the quality score of the parts. Further, the average value or the median value of the withdrawal period for each access log (for each user U, for each session) may be used as the quality score of the above parts. In addition, the quality score may be a value (80 points, 90 points, etc.) that is scored according to the length of the withdrawal period. That is, the quality score may be a calculated value obtained according to the value of the withdrawal period.

Here, the interval (interval) until the start of use of the next target content is defined as the withdrawal period, and the quality score (≈ withdrawal period) is calculated. However, the interval (interval) until the start of use of the next target part is obtained. ) May be defined as the withdrawal period to obtain the quality score. That is, the interval (interval) is measured in different units depending on the purpose of use, such as measuring in content units, measuring in parts, or measuring in action units of parts (clicking / not clicking, etc.). Interval) may be defined as the withdrawal period to obtain the quality score. For example, the interval (interval) measured for each content may be defined as the content withdrawal period, and the interval (interval) measured for each part may be defined as the part withdrawal period, and the quality score may be obtained for each.

As described above, in the present embodiment, the "vector data of the image of the entire screen of the WEB search result page" of the part for which the quality score is to be known is input, and the withdrawal period estimated by the model is defined as the "quality score". Here, it is assumed that the smaller the value (the shorter the withdrawal period) is, the better the quality score is.

For example, for the part "Direct Display (DD)" placed at the position "2" corresponding to the third row of the WEB search result page, the images and names of sightseeing spots (famous places, etc.) in Kyoto are displayed on the screen. It is assumed that the quality score is not good. In this case, the part "direct display (DD)" may be replaced with the one in which the image and the name of the gourmet guide or lunch guide (restaurant, etc.) in Kyoto are displayed on the screen. Then, when the quality score is improved, it is presumed that the user's satisfaction is higher after the replacement. In addition, the effect of improvement can be objectively evaluated by comparing the quality scores before and after the replacement.

[2. Information processing system configuration example]
Next, the configuration of the information processing system 1 including the information processing device 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the information processing system 1 according to the embodiment. As shown in FIG. 3, the information processing system 1 according to the embodiment includes a terminal device 10, an information processing device 100, and an information providing device 200. These various devices are connected via network N so as to be communicable by wire or wirelessly. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

Further, the number of each device included in the information processing system 1 shown in FIG. 3 is not limited to that shown in the figure. For example, in FIG. 3, for simplification of the illustration, only one terminal device 10 and one information providing device 200 are shown, but this is merely an example and is not limited to two or more. May be good.

The terminal device 10 is an information processing device used by the user U. For example, the terminal device 10 is a smart device such as a smartphone or tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a car navigation system, a wearable device such as a smart watch or a head-mounted display (Wearable Device). , Smart glasses, etc.

Further, the terminal device 10 includes a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), 5G (5th Generation: 5th generation mobile communication system), Bluetooth (registered trademark), and wireless LAN (Local). It can be connected to the network N via short-range wireless communication such as Area Network) and communicate with the information processing device 100 and the information providing device 200.

The information processing device 100 and the information providing device 200 are, for example, a PC, a server, a mainframe, a workstation, or the like. The information processing device 100 may be realized by cloud computing.

[3. Information processing device configuration example]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram showing a configuration example of the information processing device 100 according to the embodiment. As shown in FIG. 4, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Further, the communication unit 110 is connected to the network N by wire or wirelessly.

(Memory 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 (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 4, the storage unit 120 has a user database 121, an image vector database 122, and a quality score database 123.

(User database 121)
The user database 121 stores various information about the user U. For example, the user database 121 stores various information such as the attributes of the user U. FIG. 5 is a diagram showing an example of the user database 121. In the example shown in FIG. 5, the user database 121 has items such as "user ID (Identifier)", "age", "gender", "home", "work location", "content", and "access log". Has.

The "user ID" indicates identification information for identifying the user U. Further, "age" indicates the age of the user U identified by the user ID. The "age" may be the specific age of the user U identified by the user ID, such as 35 years old. Further, "gender" indicates the gender of the user U identified by the user ID.

Further, "home" indicates the location information of the home of the user U identified by the user ID. In the example shown in FIG. 5, "home" is illustrated with an abstract code such as "LC11", but may be latitude / longitude information or the like. Further, for example, "home" may be an area name or an address.

Further, the "work location" indicates the location information of the work location (school in the case of a student) of the user U identified by the user ID. In the example shown in FIG. 5, the “work location” is illustrated by an abstract code such as “LC12”, but may be latitude / longitude information or the like. Further, for example, the "work location" may be a region name or an address.

Further, the "content" indicates the content currently used by the user U identified by the user ID. The number of "contents" may be plural. That is, one user U may use a plurality of contents at the same time.

Further, the "access log" is an access log of the user U identified by the user ID, and indicates the access log of the user U acquired from each information providing device 200.

For example, in the example shown in FIG. 5, the age of the user U identified by the user ID "U1" is "20's", and the gender is "male". Further, for example, the user U identified by the user ID "U1" indicates that his / her home is "LC11". Further, for example, the user U identified by the user ID "U1" indicates that the work location is "LC12". Further, for example, it indicates that the user U identified by the user ID "U1" is using the "content # 1" and the access log is recorded in the "log # 1".

Here, in the example shown in FIG. 5, abstract values such as "U1", "LC11", "LC12", "content # 11", and "log # 1" are used for illustration, but "U1", "U1", " It is assumed that information such as specific character strings and numerical values is stored in "LC11", "LC12", "content # 1", and "log # 1". Hereinafter, abstract values may be illustrated also in figures relating to other information.

The user database 121 is not limited to the above, and various information may be stored depending on the purpose. For example, the user database 121 may store various information about the terminal device 10 of the user U. In addition, the user database 121 has attributes such as demographic (demographic attribute), psychographic (psychological attribute), geographic (geographical attribute), and behavioral (behavioral attribute) of user U. May remember information about. For example, the user database 121 includes name, family structure, occupation, position, income, qualification, living style (detached house, apartment, etc.), presence / absence of a car, commuting time, commuting route, commuter pass section (station). , Routes, etc.), Frequently used stations (other than the nearest station to your home / work location), Behavior history (Frequently used location information), Lessons (location, time zone, etc.), Hobbies, interests, lifestyle, etc. Information may be stored. In addition, the user database 121 may store information about a search query (search keyword) entered by the user U into a search engine or the like.

(Image vector database 122)
The image vector database 122 stores various information regarding vector data of an image of the entire screen of the content. FIG. 6 is a diagram showing an example of the image vector database 122. In the example shown in FIG. 6, the image vector database 122 has items such as "user ID", "target content", "position", "parts", "incidental information", "action", and "presence / absence".

The "user ID" indicates identification information for identifying the user U. Further, the "target content" indicates the content that is the target of vector data conversion. Further, the "position" indicates the position of the content in the layout of the entire screen. Further, the "part" is a part (part) constituting the content, and indicates a part arranged at that position. In addition, "incidental information" indicates various information incidental to the part.

Further, "action" indicates the action of the user U with respect to the part. Further, "presence / absence" indicates whether or not the action is executed. That is, "presence or absence" indicates whether or not the action has been performed.

For example, in the example shown in FIG. 6, it is shown that the user U identified by the user ID "U1" has used the target content "WEB search result page". In addition, the part "search window" is arranged at the position "0" of the target content "WEB search result page", the part "search window" has the incidental information "DQNN vector", and the action "click" is present. Show that it was done.

Further, in the example shown in FIG. 6, it is shown that the user U identified by the user ID "U1" has used the target content "map search result page". In addition, the part "map area" is arranged at the position "1" of the target content "map search result page", the part "map area" has the incidental information "center coordinates", and the action "scroll" is performed. Indicates that it has been ("Yes"). The part "map area" may be a scroll bar or the like for scrolling the screen. Further, the action "scroll" may be an operation of a scroll bar, or may be a flick, a swipe, or the like.

Similarly, the part "scale" is placed at the position "2" of the target content "map search result page", the part "scale" has the incidental information "display range", and the action "enlargement / reduction" is performed. Indicates that it has been ("Yes"). The part "scale" may be a scale bar, a enlargement / reduction button, or the like for enlarging / reducing the screen. Further, the action "enlargement / reduction" may be an operation of a scale bar or an enlargement / reduction button, or may be a pinch (pinch out / pinch in), a drag, or the like.

For example, in the target content "map search result page", if the actions "scroll" or "enlarge / reduce" are performed immediately after displaying the map ("Yes"), the search results etc. are received and displayed first. Since the center coordinates and display range of the map were not appropriate, it is possible that the user U operated and adjusted them. In this case, it is presumed that there is room for improvement in the processing related to map display.

In addition, in the target content "map search result page", if there is an action "click" on the marker (symbol mark) of the part "POI" (Point of interest) displayed on the map, the marker of that POI. A balloon containing information about the location / facility corresponding to is displayed.

In addition, if there is an action "click" on the part "gourmet icon" displayed outside the frame of the map in the target content "map search result page", an icon indicating restaurants, etc. in the vicinity will appear on the map. Is displayed. When there is an action "click" on these icons, a balloon containing information about restaurants and the like corresponding to these icons is displayed.

The image vector database 122 is not limited to the above, and various information may be stored depending on the purpose. For example, the image vector database 122 may store information regarding the date and time and order in which the actions were performed. For example, the image vector database 122 may store sequence data in which each part is arranged in the order in which actions are performed.

(Quality score database 123)
The quality score database 123 stores various information regarding the quality score of the parts constituting the content. FIG. 7 is a diagram showing an example of the quality score database 123. In the example shown in FIG. 7, the quality score database 123 has items such as "target content", "content withdrawal period", "target part", "part withdrawal period", and "quality score".

“Target content” indicates content that has been converted into vector data. In addition, the "content withdrawal period" indicates an interval (interval) from the end of use of the content to the start of use of the next content. In addition, the "target part" is a part (part) that constitutes the content, and indicates a part for which the quality score is to be known. In addition, the "parts withdrawal period" indicates an interval (interval) from the end of use of the part to the start of use of the next part. The "quality score" indicates the quality score of the part.

For example, in the example shown in FIG. 7, the content withdrawal period of the target content "WEB search result page" is "content withdrawal period # 11", and the part withdrawal period of the parts "search window" included in the content is "parts withdrawal". Period # 11 ”indicates that the quality score is“ score # 11 ”.

The quality score database 123 is not limited to the above, and various information may be stored depending on the purpose. For example, the quality score database 123 may store information indicating a quality score for each content. Further, the quality score database 123 may store information indicating the quality score of each action of the part. Further, the quality score database 123 may store sequence data in which each target part is arranged in the order of use.

(Control unit 130)
Returning to FIG. 4, the description will be continued. The control unit 130 is a controller, and is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field Programmable Gate Array). As a result, various programs (corresponding to an example of the information processing program) stored in the storage device inside the information processing device 100 are executed by executing a storage area such as a RAM as a work area. In the example shown in FIG. 4, the control unit 130 includes a reception unit 131, an acquisition unit 132, a creation unit 133, a learning unit 134, a calculation unit 135, and a provision unit 136.

(Reception Department 131)
The reception unit 131 receives the registration of various information about each user U from the terminal device 10 of each user U via the communication unit 110, and stores the registration in the user database 121.

(Acquisition unit 132)
The acquisition unit 132 acquires the access log of each user U from each information providing device 200 via the communication unit 110.

(Creation unit 133)
The creation unit 133 converts the image of the entire screen of the content into vector data from the acquired access log of each user U, and creates vector data of the image of the entire screen of the content.

(Learning Department 134)
The learning unit 134 inputs vector data of an image of the entire screen of the content into the model, and learns the content withdrawal period (Absence Time) as correct answer data. The learning unit 134 creates a model for predicting the withdrawal period based on the learning result. Any type of model can be adopted as the model.

The learning is, for example, deep learning (deep learning) using a deep neural network (DNN). Data mining and other machine learning algorithms may also be used. The learning unit 134 learns the model by the various learning methods described above.

Further, the learning unit 134 inputs the vector data of the image of the entire screen of the content into the trained model, and acquires the estimated withdrawal period.

(Calculation unit 135)
The calculation unit 135 calculates the quality score of the target part from the acquired withdrawal period. For example, the calculation unit 135 calculates the average value or the median value of the withdrawal period for each access log (for each user U, for each session) as the quality score of the target part. Further, the calculation unit 135 may calculate a scored value according to the length of the withdrawal period as the quality score of the target part. Alternatively, the calculation unit 135 may use the acquired withdrawal period as it is as the quality score of the target part.

In reality, the learning unit 134 and the calculation unit 135 may be linked / integrated. For example, the learning unit 134 sets a certain percentage of the training data as test data in advance, inputs a set of vector data including parts for which a quality score is to be obtained from the test data into the model, and inputs the data to the model. Output the withdrawal period. The calculation unit 135 calculates the average value of the withdrawal period, which is the output, and uses it as a quality score. In addition, instead of extracting a set of data from the test data, a method of generating a plurality of data in which parts other than the target parts are randomly input and estimating based on the data may be used. In addition, the data used for learning may be used as the data to be input when acquiring the quality score. The average value may be another method such as the median value.

(Providing Department 136)
The providing unit 136 provides information on the quality score of the target part to the outside via the communication unit 110. For example, the providing unit 136 may provide a report on the quality score of the target part in response to an external request. The providing unit 137 may function as a display control unit that displays information on the quality score of the target part on a display device such as a display.

Further, the providing unit 136 may provide information regarding the withdrawal period of the target part to the outside via the communication unit 110. For example, the providing unit 136 may provide information regarding the withdrawal period of the target part to the outside in response to a request from the outside.

Further, the providing unit 136 may provide various services to the terminal device 10 of each user U via the communication unit 110.

[4. Processing procedure]
Next, the processing procedure by the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 8 is a flowchart showing a processing procedure according to the embodiment. The processing procedure shown below is repeatedly executed by the control unit 130 of the information processing apparatus 100.

The control unit 130 of the information processing device 100 acquires the access log of each user U from each information providing device 200 via the communication unit 110 (step S101).

The control unit 130 converts the image of the entire screen of the content into vector data from the access log of each user U, and creates vector data of the image of the entire screen of the content (step S102).

The control unit 130 inputs vector data of an image of the entire screen of the content into the model, trains the content withdrawal period (Absence Time) as correct answer data, and predicts the withdrawal period based on the learning result. Create (step S103).

The control unit 130 inputs the vector data of the image of the entire screen of the content into the trained model, and acquires the estimated withdrawal period (step S104).

The control unit 130 calculates the quality score of the target part from the acquired withdrawal period (step S105).

The control unit 130 provides information regarding the quality score of the target part to the outside via the communication unit 110 (step S106).

[5. Modification example]
The terminal device 10 and the information processing device 100 described above may be implemented in various different forms other than the above-described embodiment. Therefore, a modified example of the embodiment will be described below.

In the above embodiment, not only the image (image) of the entire screen of the content but also the image of each component of the content may be individually converted into vector data. In this case, the information processing apparatus 100 inputs vector data of an image of each component of the content into the model, and learns the withdrawal period of each component as correct answer data by deep learning (deep learning).

Further, in the above embodiment, each image (a plurality of images of the search result, etc.) constituting the component may be converted into vector data. For example, a plurality of images displayed as search results of an image search, a plurality of images of search results directly displayed on a screen as a direct display (DD), and the like may be individually converted into vector data. In this case, the information processing apparatus 100 inputs the vector data of each image constituting the component into the model, and learns the withdrawal period of each image as correct answer data by deep learning (deep learning).

Further, in the above embodiment, a WEB search result page or the like is given as an example of the content, but in reality, it may be a GUI (graphical user interface), an operation screen of application software (application), or the like. .. For example, the information processing device 100 inputs vector data of an image of the entire operation screen of the application installed in the terminal device 10 into the model, and deep learning (deep learning) causes the components of the operation screen of the application to be removed. Learn the period as correct answer data. Then, the information processing apparatus 100 obtains the quality score of the component from the withdrawal period of the component. The component of the operation screen of the application may be each function of the application.

Further, in the above embodiment, the component of the content may be a function of the content itself. For example, audio, video, etc. that automatically flow when a website is opened may be treated as a component, and the withdrawal period of the component may be learned as correct answer data. Since user U may be dissatisfied with such audio and video and may not use the website, it is useful to learn the withdrawal period and obtain a quality score using them as components. be. In addition, functions such as pop-ups and redirects may be treated as components.

Further, in the above embodiment, the component of the content may be a clickable icon, an image, or the like. Further, the object is not limited to the object that can be operated by the user U, and the background of the content screen, CSS (Cascading Style Sheets), and the like may be treated as components.

Further, in the above embodiment, when the content screen has a layer structure (hierarchical structure) such as a map screen in which various data are layered on a basic map, each layer (layer) is used as a component. You may handle it.

[6. effect〕
As described above, the information processing apparatus 100 according to the present application includes a creation unit, a learning unit, and a calculation unit. The creation unit creates vector data of the screen image of the content used by the user. The learning unit trains the model to output the withdrawal period information indicating the withdrawal period from the use of the content to the reuse of the content from the vector data. The calculation unit calculates the quality score of the component of the content from the withdrawal period information. This makes it possible to evaluate the quality of the components of the content. The component of the content is, for example, a part that constitutes the content.

In addition, the calculation unit calculates the average value or the median value of the withdrawal period as the quality score of the component. Alternatively, the calculation unit calculates a scored value according to the length of the withdrawal period as the quality score of the component. This makes it possible to give a quantitative score to the components of the content.

The vector data includes information indicating the position of the content on the screen, information indicating the type of the component arranged at the position, and information indicating the presence or absence of the user's action with respect to the component. This makes it possible to evaluate the quality of the components of the content.

The learning unit may train the model to output the withdrawal period information indicating the withdrawal period from the use of the component of the content to the reuse of the component from the vector data. As a result, a quality score can be obtained from the withdrawal period information (parts withdrawal period) for each part.

In addition, the learning unit outputs the withdrawal period information indicating the withdrawal period from the vector data to the time when the user performs the predetermined action on the component of the content until the predetermined action is performed again. You may let them learn. As a result, a quality score can be obtained from the withdrawal period information for each action of the part.

In addition, the learning unit sets a certain percentage of the training data as test data in advance, inputs vector data including the component for which the quality score is to be obtained from the test data into the model, and leaves. Output the period. In this way, the withdrawal period is acquired from the trained model by inputting the vector data.

The learning unit trains the model to output the withdrawal period information with the function of the content as a component. As a result, for example, audio, video, etc. that automatically flow when a website is opened can be treated as a component, and the withdrawal period of the component can be learned as correct answer data.

The learning unit uses the operation screen of the application as the screen of the content, and trains the model to output the withdrawal period information with each function of the application as a component. This makes it possible to evaluate the quality of the parts that make up the operation screen of the application.

[7. Hardware configuration]
Further, the terminal device 10 and the information processing device 100 according to the above-described embodiment are realized by, for example, a computer 1000 having a configuration as shown in FIG. Hereinafter, the information processing device 100 will be described as an example. FIG. 9 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to the output device 1010 and the input device 1020, and the arithmetic unit 1030, the primary storage device 1040, the secondary storage device 1050, the output I / F (Interface) 1060, the input I / F 1070, and the network I / F 1080 are bused. It has a form connected by 1090.

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

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

The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various information such as a display, a projector, and a printer. For example, USB (Universal Serial Bus) or DVI. (Digital Visual Interface), HDMI (registered trademark) (High Definition Multimedia Interface), etc. are realized by standard connectors. The input I / F 1070 is an interface for receiving information from various input devices 1020 such as a mouse, keyboard, keypad, buttons, and a scanner, and is realized by, for example, USB.

Further, 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.

Further, the output device 1010 and the input device 1020 may be integrated like a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated as input / output I / F.

The input device 1020 is, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), or a tape. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like.

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

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.

For example, when the computer 1000 functions as the information processing device 100, the arithmetic unit 1030 of the computer 1000 realizes the function of the control unit 130 by executing the program loaded on the primary storage device 1040. Further, the arithmetic unit 1030 of the computer 1000 may load a program acquired from another device via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. Further, the arithmetic unit 1030 of the computer 1000 may cooperate with other devices via the network I / F 1080 to call the functions and data of the program from other programs of the other devices and use them.

[8. others〕
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. In addition, the above-mentioned components include those that can be easily assumed by those skilled in the art, those that are substantially the same, that is, those in a so-called equal range. Furthermore, the components described above can be combined as appropriate. Further, various omissions, replacements or changes of the components can be made without departing from the gist of the above-described embodiment.

Further, among the processes described in the above-described embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or part of it can be done automatically by a known method. In addition, the processing procedure, specific name, 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 illustrated information.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically dispersed / physically distributed in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

For example, the information processing device 100 described above may be realized by a plurality of server computers, or may be realized by calling an external platform or the like by API (Application Programming Interface), network computing, or the like depending on the function. Can be changed flexibly.

Further, the above-described embodiments and modifications can be appropriately combined as long as the processing contents do not contradict each other.

Further, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as an acquisition means or an acquisition circuit.

1 Information processing system 10 Terminal equipment 100 Information processing equipment 110 Communication unit 120 Storage unit 121 User database 122 Image vector database 123 Quality score database 130 Control unit 131 Reception unit 132 Acquisition unit 133 Creation unit 134 Learning unit 135 Calculation unit 136 Providing unit 200 Information provider

Claims (12)

A creation unit that creates vector data of the screen image of the content used by the user,
A learning unit that trains a model to output withdrawal period information indicating a withdrawal period from the use of the content to the reuse of the content from the vector data.
An information processing device including a calculation unit that calculates a quality score of a component of the content from the withdrawal period information. The information processing apparatus according to claim 1, wherein the calculation unit calculates an average value of the withdrawal period as a quality score of the component. The information processing apparatus according to claim 1, wherein the calculation unit calculates a median value of the withdrawal period as a quality score of the component. The information processing according to any one of claims 1 to 3, wherein the calculation unit calculates a scored value according to the length of the withdrawal period as the quality score of the component. Device. The vector data includes information indicating the position of the content on the screen, information indicating the type of the component arranged at the position, and information indicating the presence or absence of the user's action with respect to the component. The information processing apparatus according to any one of claims 1 to 4. The learning unit trains the model to output from the vector data the withdrawal period information indicating the withdrawal period from the use of the component of the content to the reuse of the component. The information processing apparatus according to any one of claims 1 to 5. From the vector data, the learning unit outputs withdrawal period information indicating a withdrawal period from the user performing a predetermined action on the component of the content to performing the predetermined action again. The information processing apparatus according to any one of claims 1 to 5, wherein the model is trained. The learning unit sets a certain percentage of the training data as test data in advance, and inputs the vector data including the component for which the quality score is to be obtained from the test data into the model. The information processing apparatus according to any one of claims 1 to 7, wherein the withdrawal period is output. The information processing apparatus according to any one of claims 1 to 8, wherein the learning unit trains a model to output the withdrawal period information by using a function of the content as a component. .. The learning unit has the operation screen of the application as the screen of the content, and each function of the application as a component, and trains the model to output the withdrawal period information. The information processing device according to any one of them. It is an information processing method executed by an information processing device.
The process of creating vector data of the screen image of the content used by the user, and
A learning process in which a model is trained to output withdrawal period information indicating a withdrawal period from the use of the content to the reuse of the content from the vector data.
An information processing method including a calculation step of calculating a quality score of a component of the content from the withdrawal period information. The creation procedure to create vector data of the screen image of the content used by the user, and
A learning procedure for training a model to output withdrawal period information indicating a withdrawal period from the use of the content to the reuse of the content from the vector data.
An information processing program for causing a computer to execute a calculation procedure for calculating a quality score of a component of the content from the withdrawal period information.

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