JP2018152010A - Information processing device and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain an advertisement closer to the preference of a user in a short time when repeatedly obtaining advertisements.SOLUTION: A server 4 includes a CPU 41 that creates a predetermined number of POP candidate data pieces; makes a terminal display unit 24 display the created predetermined number of POP candidate data pieces; selects a POP candidate data piece desired by a user from among the predetermined number of POP candidate data pieces displayed on the terminal display unit 24, on the basis of a user's input; and derives a learning value indicating the preference of the user on the basis of the time until the desired candidate data piece is selected after the predetermined number of POP candidate data pieces are displayed. The CPU 41 displays the predetermined POP candidate data pieces on the basis of the derived learning value.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an information processing creation apparatus and a program.

  2. Description of the Related Art Conventionally, development of an advertisement creating apparatus that creates an advertisement such as a POP (Point of purchase advertising) has been promoted for the purpose of enhancing a sales promotion effect. As the advertisement creation device, for example, a printed matter creation system that creates one POP based on the layout, recipe example, logo, comment input by the user, price of sale, etc. selected by the user has been proposed ( For example, see Patent Document 1).

JP 2005-126877 A

  However, in the above-described prior art, the user needs to select the above-mentioned various information each time a POP is created, and each time desired information is selected from options including items that are not preferred by the user. Need to find and select this. For this reason, every time a POP is created, it takes time and is complicated.

  Therefore, an object of the present invention is to provide an information processing apparatus and program that can obtain an advertisement closer to a user's preference in a short time when repeatedly obtaining an advertisement.

In order to solve the above problems, an information processing apparatus according to claim 1 is provided:
Creating means for creating a predetermined number of ad candidates;
Display control means for causing the display means to display the predetermined number of advertisement candidates created by the creation means;
Selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means, based on user input;
Learning means for deriving a learning value indicating a user's preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means; Prepared,
The creating means creates the predetermined number of advertisement candidates based on the learning value derived by the learning means.

  According to the present invention, it is possible to provide an information processing apparatus and program capable of obtaining an advertisement closer to the user's preference in a short time when repeatedly obtaining an advertisement.

It is a schematic block diagram which shows the information processing system of this embodiment. It is a block diagram which shows the function structure of a server. It is a figure which shows an example of the layout data table memorize | stored in the memory | storage part. It is a figure which shows an example of the eye catch data table memorize | stored in the memory | storage part. It is a figure which shows an example of the learning value data table memorize | stored in a memory | storage part and updated whenever a POP data creation process is performed. It is a block diagram which shows the function structure of a portable terminal. It is a flowchart which shows an example of the POP data creation process performed by a server. It is a figure which shows an example of the input screen before information input. It is a figure which shows an example of the input screen after information input. It is a figure which shows an example of a preview screen. It is a figure which shows the example of 25 types of POP candidate data produced. It is a flowchart which shows an example of the 1st acquisition process performed by the server. It is a flowchart which shows an example of the 2nd acquisition process performed by the server. It is a figure which shows the morpheme characteristic value total obtained from the information input by the user. It is a flowchart which shows an example of the learning process performed by a server. It is a figure which shows the POP data component contained in three types of POP candidate data.

  EMBODIMENT OF THE INVENTION Below, the form for implementing this invention is demonstrated using drawing. However, although various technically preferable limitations for implementing the present invention are given to the embodiments described below, the scope of the invention is not limited to the following embodiments and illustrated examples.

<< Configuration of Information Processing System 1 >>
First, the configuration of the present embodiment will be described with reference to FIG. FIG. 1 is a schematic configuration diagram of an information processing system 1 according to the present embodiment.

  The information processing system 1 according to the present embodiment is a system that creates POP data as an advertisement based on input information, learns user preferences by repeating the creation of POP data, and responds to user preferences. This system can create POP data.

  As shown in FIG. 1, an information processing system 1 includes a server (information processing device) 4, mobile terminals 2 a and 2 b such as smartphones and tablet terminals that are user terminals, PCs (personal computers) 3 a and 3 b, printing And a device 11, and each device is communicably connected via a communication network N. Hereinafter, the portable terminals 2a and 2b and the PCs 3a and 3b may be collectively referred to as user terminals. The mobile terminals 2a and 2b and the PCs 3a and 3b are connected to the server 4 for communication. However, the present invention is not limited to this, and a plurality of user terminals may be provided depending on the number of users who receive service provided by the server 4. They exist and are in communication connection with the server 4. The server 4 may be connected to a POS (Point Of Sales) terminal as a user terminal.

  The server 4 creates POP data as an advertisement for sales promotion, and manages the created POP data. In addition, the server 4 distributes an input screen 5 for inputting information for creating POP data, a preview screen 6 of the created POP candidate data (advertisement candidate), and the like to the user terminal in response to a request from the user terminal. To do.

  User terminals such as the portable terminals 2 a and 2 b receive and display various display information transmitted from the server 4, receive user operation inputs, and transmit the operation information to the server 4.

  The printing device 11 is a printer for home use or business use such as an electrophotographic type or an ink jet type, and prints POP data or the like on a sheet in accordance with an inputted print instruction to create a POP or the like as an advertisement. To do.

  The communication network N is assumed to be configured by the Internet, but may include a LAN (Local Area Network), a WAN (Wide Area Network), and the like, such as a telephone line, a dedicated line, a mobile communication network, a communication satellite network. A CATV (Cable Television) line or the like may be included.

  The information processing system 1 may be configured to execute other application programs used in various user terminals. Such application programs include, for example, sales management operations for daily and monthly sales aggregation and analysis of stores, accounting management operations for tax payment processing such as property tax and personal business tax, and employee salary calculation. Payroll management business that performs processing, employment management business that manages personnel shift management and employee attendance management in stores, etc., and document management business that creates and manages notification documents for related businesses and employees It is also possible that the information processing system 1 functions as a management support system by making these executable.

<< Configuration of Server 4 >>
Next, the internal configuration of the server 4 will be described. FIG. 2 shows the internal configuration of the server 4. 3 to 5 show examples of the layout data table 451, the eye catch data table 452, and the learning value data table 453, respectively.

  As shown in FIG. 2, the server 4 includes a CPU (Central Processing Unit) 41, an operation input unit 42, a RAM (Random Access Memory) 43, a server display unit 44, a storage unit (storage unit) 45, A communication unit 46 and a time measuring unit 47 are provided, and each unit is connected via a bus 48.

  CPU41 reads the system program memorize | stored in the memory | storage part 45, expand | deploys to the work area of RAM43, and controls each part according to the said system program. Further, the CPU 41 reads out the processing program stored in the storage unit 45 and develops it in the work area, and processes on the server 4 side such as POP data creation processing, first acquisition processing, second acquisition processing, and learning processing described later. And the like, and functions as a creation unit, a display control unit, a selection unit, and a learning unit.

  The operation input unit 42 includes a keyboard having cursor keys, characters, numeric input keys, various function keys, and the like. The operation input unit 42 receives pressing input of each key by the user and outputs the operation information to the CPU 41. The operation input unit 42 may include a pointing device such as a mouse, and may receive a position input and output it to the CPU 41 as operation information.

  The RAM 43 is a volatile memory. The RAM 43 has a work area for storing various programs to be executed and data related to these various programs.

  The server display unit 44 is configured by an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube), or the like, and performs screen display according to a display control signal from the CPU 41.

The storage unit 45 includes an HDD (Hard Disk Drive) having a magnetic recording medium. The storage unit 45 stores a system program executed by the CPU 41, a processing program, an application program such as a Web server program, data necessary for executing these programs, and the like. The processing program includes a program for executing various processes including processing on the server 4 side such as POP data creation processing, first acquisition processing, second acquisition processing, and learning processing described later.
These programs are stored in the storage unit 45 in the form of computer-readable program codes. The CPU 41 sequentially executes operations according to the program code.

  The storage unit 45 stores various data used for POP data creation processing and the like in advance. That is, the storage unit 45 stores POP data components (advertisement components) such as layout of POP data, eye catches included in the POP data, typeface, font color, background, frame / texture, and illustration. In addition, the storage unit 45 stores user information including the business type / business condition of the store or company that the user manages or belongs to.

  The storage unit 45 also includes a layout data table 451 (see FIG. 3), an eye catch data table 452 (see FIG. 4), and learning value data used in a first acquisition process, a second acquisition process, and a learning process, which will be described later. A table 453 (see FIG. 5) is stored. The storage unit 45 also stores similar data tables (not shown) for POP data components other than the layout and eye catch, that is, the typeface, font color, background, frame / texture, and illustration.

  The layout data table 451 is a table in which layout types L1 to L20 and index values set for each item according to each layout type are stored in association with each other. Examples of the item include “vertical / horizontal”, “photo size”, “number of photos”, “eye catch size”, “number of illustrations”, and the like, but are not limited thereto. In the item “vertical / horizontal”, an index value of “0” is set when the layout type is vertical, and “1” is set when the layout type is horizontal. In the items of “photo size” and “eye catch size”, the closer the index value is to “1”, the larger the size of image data and eye catch with respect to the entire generated POP data. .

  The eye catch data table 452 is a table in which the eye catch types i1 to i20 and index values set for each item according to each eye catch type are stored in association with each other. Examples of the item include “vertical / horizontal”, “for Japanese food”, “for Western food”, “for cafe”, “for Chinese”, and the like, but are not limited thereto. In the “vertical / horizontal” item, an index value of “0” is set when the eye catch type is vertical, and “1” is set when the eye catch type is horizontal. In addition, in the items “for Japanese food”, “for Western food”, “for cafe” and “for Chinese”, “1” is appropriate for those items, which is suitable for those items. If not, an index value of “0” is set.

  The learning value data table 453 is a table in which a user who receives the service provided by the server 4 and a learning value indicating the user's preference for the POP data component calculated and updated for each user are stored in association with each other. The learning value is calculated and updated for each item set in advance for each POP data component. Examples of such items include “vertical / horizontal”, “photo size”, and “eye catch size” as layout items, and “Japanese food”, “Western food”, “cafe”, “ “Type A”, “Type B”, “Type C”, and the like are included in the typeface items such as “For Chinese”, but are not limited thereto. Such a learning value data table 453 is updated every time POP data creation processing is executed. In the item “vertical / horizontal”, the closer the learning value is to “1”, the more the user prefers the horizontal layout. In addition, in the items of “photo size” and “eye catch size”, the closer the learning value is to “1”, the more the user prefers image data and eye catch that are large in size to the entire POP data to be created. Is shown.

  The storage unit 45 stores characteristic values set in advance for each word. The characteristic value is set for each item, and is used when acquiring the morpheme characteristic value total in the second acquisition process described later. Items overlap with some of the items included in the learning value data table 453 described above, for example, “vertical / horizontal”, “for Japanese food”, “for western food”, “for cafe”, “for Chinese”, etc. Is mentioned. In the “vertical / horizontal” item, a characteristic value of “0” is set when the word is suitable for vertical arrangement, and “1” is set when the word is suitable for horizontal arrangement. Yes. In addition, in the items “for Japanese food”, “for Western food”, “for cafe” and “for Chinese”, if the word is suitable for those items, “1”, for those items If it is not suitable, a characteristic value of “0” is set. In addition, among the words, the characteristic values are set to “0” for the parts of speech other than nouns, adverbs, verbs, and adjectives, and symbols. For example, for the noun “Yuzu”, the “vertical / horizontal” characteristic value is “1”, the “Japanese food” characteristic value is “1”, the “Western food” characteristic value is “0”, The characteristic value for “cafe” is set to “1”, and the characteristic value for “Chinese food” is set to “0”. Further, for example, for the word “no”, the characteristic value of each item is set to “0”.

  The storage unit 45 stores various data input by the user in the POP data creation process or the like. That is, the storage unit 45 stores, for example, image data input on the input screen 5, creation history of POP data created in the past, and the like.

  The communication unit 46 includes a modem, a TA (Terminal Adapter), a router, a network card, and the like. The communication unit 46 communicates with an external device such as a user terminal on the communication network N to perform data transmission / reception.

  The timer unit 47 has a built-in timer circuit, measures the current time and date, and outputs the current time information.

<< Configuration of Mobile Terminal 2a >>
Next, the internal configuration of the user terminal will be described using the portable terminal 2a as an example. FIG. 6 shows an internal configuration of the mobile terminal 2a.

As shown in FIG. 6, the mobile terminal 2 a includes a CPU 21, an operation input unit 22, a RAM 23, a terminal display unit (display unit) 24, a storage unit 25, a communication unit 26, and a time measuring unit 27. Each part is connected via a bus 28.
Note that the portable terminal 2b and the PCs 3a and 3b are configured in substantially the same manner.

  The CPU 21, the operation input unit 22, the RAM 23, the terminal display unit 24, the storage unit 25, the communication unit 26, and the time measuring unit 27 are respectively the CPU 41 of the server 4, the operation input unit 42, the RAM 43, the server display unit 44, the storage unit 45, It is substantially the same as the structure of the communication part 46 and the time measuring part 47, and a different part is mainly demonstrated.

  CPU21 reads the system program memorize | stored in the memory | storage part 25, expand | deploys to the work area of RAM23, and controls each part according to the said system program. In addition, the CPU 21 reads out the processing program stored in the storage unit 25 and expands it in the work area, and performs the POP data creation process, the first acquisition process, the second acquisition process, the learning process, and the like described later on the mobile terminal 2a side. Various processes including the process are executed.

  The operation input unit 22 includes a push button switch, a touch sensor provided on the terminal display unit 24, and the like. The operation input unit 22 receives a user input operation, converts the operation content into an electric signal, and outputs the electric signal to the CPU 21. .

  The terminal display unit 24 is configured by an LCD, a CRT, or the like, and performs screen display according to a display control signal from the CPU 21. The touch sensor is provided on the display screen of the terminal display unit 24, and functions as a touch panel type operation display means.

  The storage unit 25 includes an HDD having a magnetic recording medium. The storage unit 25 stores system programs and processing programs executed by the CPU 21, application programs such as a Web browser, data necessary for executing these programs, and the like. The processing program includes programs for executing various processes such as POP data creation processing, first acquisition processing, second acquisition processing, and learning processing, which will be described later, including processing on the mobile terminal 2a side.

  The communication unit 26 communicates with an external device such as the server 4 on the communication network N.

<< Operation of Server 4 >>
(POP data creation process)
POP data creation processing performed by the server 4 of the present embodiment will be described with reference to FIGS. FIG. 7 is a flowchart illustrating an example of POP data creation processing. FIG. 8 is a diagram showing the input screen 5 before information input displayed on the terminal display unit 24. FIG. 9 is a diagram showing the input screen 5 after information input displayed on the terminal display unit 24. FIG. 10 is a diagram illustrating an example of the preview screen 6 displayed on the terminal display unit 24. FIG. 11 is a diagram illustrating an example of 25 types of POP candidate data to be created.

  The server 4 is configured to perform POP data creation processing for creating POP data that introduces a target product or service based on text or image data input by a user via a user terminal.

  Specifically, as shown in FIG. 7, the CPU 41 of the server 4 displays the input screen 5 shown in FIG. 8 on the terminal display unit 24 when the user terminal requests execution of a program for creating POP data. (Step S101). The input screen 5 is a screen for inputting information necessary for creating POP data. The necessary information includes the name of the target product (product name) introduced in the POP data to be created, the price of the target product, a catch phrase of the target product, and the image data of the target product. Therefore, the input screen 5 is provided with a product name input field 51, a price input field 52, a catch phrase input field 53, and an image data input field 54 as input fields for inputting each piece of information. For example, as shown in FIG. 9, the product name input field 51 is “Yuzu salt ramen”, the price input field 52 is “700”, and the catch phrase input field 53 is “a refreshing ramen with a flavor of citron”. "In the image data input field 54, the image data of ramen is input.

  In addition to the input fields 51 to 54, the input screen 5 includes a product addition button 55 selected by the user when adding the product to be introduced to the POP data, and a user when creating POP data after information input. The creation button 56 selected by the user, the cancel button 57 selected by the user when the display of the input screen 5 is terminated, and information that serves as a reference for catch phrase input (for example, catch phrase samples and trend words) are displayed. A reference button 58 or the like selected by the user at the time is provided.

  When the product addition button 55 is selected by the user on the input screen 5, a product name input field 51, a price input field 52, a catch phrase input field 53, and an image data input field 54 are added to the input screen 5 one by one. The When the user inputs information in each of the added input fields 51 to 54, the CPU 41 can create POP data including introductions of two types of products. Therefore, when the product addition button 55 is selected a plurality of times and information is input to the input fields 51 to 54, the CPU 41 can create POP data including introductions of a plurality of types of products.

  In addition, at least one of the product name and image data of the target product may be input to each input field 51 to 54 of the input screen 5, but the more information that is input is created In order to improve the accuracy of the POP data, it is preferable that information is input to all of the input fields 51 to 54.

  Next, as shown in FIG. 7, the CPU 41 determines whether or not the input is completed (step S102). Specifically, for example, the CPU 41 selects at least the product name in the product name input field 51 by the user or the image data input field 54 and the creation button 56 is selected. If it is determined that the input has been completed.

  If it is determined that the input has not been completed (step S102; NO), the CPU 41 performs the process of step S102 again. When it is determined that the input has been completed (step S102; YES), the CPU 41 selects and acquires five types of layouts used for creating the POP data based on the information input in the image data input field 54, and acquires the first acquisition. Processing is performed (step S103). Details of the first acquisition process will be described later.

  Next, based on the information input in the product name input field 51 and the catch phrase input field 53, the CPU 41 uses POP data components (eye catch, typeface, font color, background, A second acquisition process for selecting and acquiring five types of frames / textures and illustrations is performed (step S104). Details of the second acquisition process will be described later.

  Next, the CPU 41 creates 25 types of POP candidate data based on the acquired POP data components (step S105). That is, the CPU 41 creates five types of designs by combining the five types of eye catch, typeface, font color, background, frame / texture, and illustration obtained in step S104. Then, the CPU 41 creates 25 types of POP candidate data by combining the 5 types of layouts acquired in step S103, the 5 types of created designs, and the information input in the input fields 51 to 54. The 25 types of POP candidate data to be created include the information input in the input fields 51 to 54 in common.

  Next, the CPU 41 displays the generated 25 types of POP candidate data on the terminal display unit 24 (step S106). Specifically, as shown in FIG. 10, the CPU 41 causes the terminal display unit 24 to display a preview screen 6 on which one of the created plural types of POP candidate data is displayed.

On the preview screen 6, the CPU 41 sequentially displays other data among the 25 types of POP candidate data created in step S <b> 105 when the user performs a swipe operation.
For example, the case where POP candidate data a1 to a5, b1 to b5, c1 to c5, d1 to d5, and e1 to e5 shown in FIG. 11 are generated as 25 types of POP candidate data will be described. In FIG. 11, POP candidate data arranged in the horizontal direction has a common layout, and POP candidate data arranged in the vertical direction has a common design. When the user inputs a horizontal or vertical swipe operation, the POP candidate data displayed on the terminal display unit 24 is arranged adjacent to the horizontal or vertical direction in the arrangement shown in FIG. POP candidate data are sequentially displayed. In addition, in the arrangement of each POP candidate data shown in FIG. 11, the POP candidate data is configured to be loop-displayed in both the horizontal direction and the vertical direction. For example, when a left swipe operation is input in a state where the POP candidate data a1 is displayed on the terminal display unit 24, the POP candidate data a5 is displayed on the terminal display unit 24, and an upward swipe operation is input. Then, the POP candidate data e1 is displayed on the terminal display unit 24. Further, POP candidate data including a POP data component having a higher evaluation value, which will be described later, is arranged in the upper left in FIG. Therefore, it is preferable that the POP candidate data that is first displayed on the terminal display unit 24 in the process of step S106 is the POP candidate data a1. Thereby, the user can find out POP candidate data close to his / her preference in a shorter time.

  Next, as shown in FIG. 7, the CPU 41 determines whether or not a selection operation for selecting desired POP candidate data has been input by the user (step S107). Specifically, for example, when the user selects the selection button 61 in a state where the user-desired POP candidate data is displayed on the terminal display unit 24 among the 25 types of POP candidate data, the CPU 41 It is determined that a selection operation for selecting candidate data has been input.

  If it is determined that the selection operation is not input (step S107; NO), the CPU 41 determines whether or not a cancel operation for ending the display of the preview screen 6 is input by the user (step S108). Specifically, for example, the CPU 41 determines that a cancel operation has been input when the cancel button 62 is selected by the user.

  If it is determined that a cancel operation has been input (step S108; YES), the CPU 41 ends the display of the preview screen 6 and performs the process of step S101 again. If it is determined that the cancel operation is not input (step S108; NO), the CPU 41 performs the process of step S107 again.

  If it is determined that a selection operation has been input (step S107; YES), the CPU 41 selects one POP candidate data from the plurality of types of POP candidate data created in step S105 based on the selection operation. (Step S109). At this time, the CPU 41 counts the time from when 25 types of POP candidate data are displayed on the terminal display unit 24 in the process of step S106 until the user desired POP candidate data is selected in the process of step S109. Count by. In addition, the CPU 41 displays each POP candidate data displayed on the terminal display unit 24 after 25 types of POP candidate data are displayed on the terminal display unit 24 until the user desired POP candidate data is selected. Each time is counted by the timer 47.

Next, the CPU 41 learns the user's preference for the POP data component based on the time from when 25 types of POP candidate data are displayed on the terminal display unit 24 until the user desired POP candidate data is selected. A learning process for calculating is performed (step S110). Details of the learning process will be described later.
The POP data creation process is performed as described above.

  After completion of the POP data creation process, the CPU 41 may store the created POP candidate data in the storage unit 45 as a completed version, or when a predetermined operation is performed by the user, the POP data editing screen. (Not shown) may be displayed on the terminal display unit 24. Accordingly, each POP data component included in the POP data can be further changed as desired by the user, and the POP data closer to the user's preference after the change can be stored in the storage unit 45 as a completed version. . The CPU 41 outputs the POP data to the printing apparatus 11 via the communication unit 46 and causes the printing apparatus 11 to print when the user terminal requests printing of the completed version of the POP data. It is also good.

  In the process of step S105, 25 types of POP candidate data are created from 5 types of layouts and 5 types of designs. However, the present invention is not limited to this. For example, any number of layouts and designs may be used for creating POP candidate data, and any combination thereof may be used. Further, for example, the number of POP candidate data created may be 24 or less, or may be 26 or more.

(First acquisition process)
Next, the 1st acquisition process performed by the server 4 of this embodiment is demonstrated with reference to FIG. FIG. 12 is a flowchart illustrating an example of the first acquisition process.

  As shown in FIG. 12, first, the CPU 41 obtains the number n of image data input by the user in the image data input field 54 of the input screen 5 (step S201). For example, when no image data is input in the image data input field 54 on the input screen 5, the number of image data n = 0, and when image data is input in the image data input field 54, the number of image data n = 1. For example, when the product addition button 55 is selected once and image data is input to the two image data input fields 54, the number of image data is n = 2.

  Next, the CPU 41 refers to the layout data table 451 shown in FIG. 3 and extracts a layout having “number of photos” equal to or larger than the number of image data n acquired in step S201 from the layout types L1 to L20 (step S201). S202). For example, if the number of image data n acquired in step S201 is 1, layout types L1, L2, L3, etc. are extracted.

  Next, as shown in FIG. 12, the CPU 41 determines whether or not the number of extracted layouts is 5 or more (step S203). If it is determined that the number of extracted layouts is not 5 or more (step S203; NO), the CPU 41 also extracts a layout whose “number of photos” is less than the number of image data n (step S204), and in the next step S205. Process. Therefore, when the layout extracted in step S202 is less than 5, all the layout types L1 to L20 are extracted.

On the other hand, if it is determined that the number of extracted layouts is 5 or more (step S203; YES), the CPU 41 calculates an evaluation value for each of the extracted layouts (step S205). Specifically, for example, the CPU 41 performs the following based on the index value (see FIG. 3) set for each item according to the extracted layout and the learning value (see FIG. 5) associated with each user. An evaluation value is calculated according to equation (1).
Expression (1): Evaluation value = “Vertical / Horizontal” index value of the extracted layout × “Vertical / horizontal” learning value in the user layout + “Photo size” index value of the extracted layout × User's Learning value of “photo size” + index value of “eye catch size” of extracted layout × learning value of user “eye catch size”

  Note that items such as “number of photos” and “number of illustrations” included in the layout are not stored in the learning value data table 453 as shown in FIG.

For example, when the layout extracted in step S202 or S204 is the layout type L1 and the user code of the user is “000001”, the evaluation value of the layout type L1 is as follows.
Evaluation value of layout type L1 = 1 × 0.3 + 1.0 × 0.4 + 0.7 × 0.5 = 1.05

For example, when the layout extracted in step S202 or S204 is the layout type L2 and the user code of the user is “000001”, the evaluation value of the layout type L2 is as follows.
Evaluation value of layout type L2 = 1 × 0.3 + 0.4 × 0.4 + 0.6 × 0.5 = 0.76

Next, as illustrated in FIG. 12, the CPU 41 acquires a layout from the storage unit 45 based on the evaluation value (step S206). Specifically, for example, the CPU 41 ranks the layout types extracted in step S202 or S204 in descending order of the evaluation values calculated in step S205. Then, the CPU 41 acquires, from the storage unit 45, two layout types with the highest ranking, two layout types with the middle ranking, and one layout type with the lowest ranking. For example, when all 20 layout types are extracted, the layout types of the first, second, tenth, eleventh, and twentieth evaluation values are acquired.
The first acquisition process is performed as described above.

  In the process of step S205, the evaluation value is calculated based only on the items “vertical / horizontal”, “photo size”, and “eye catch size”, but the present invention is not limited to this. For example, any combination may be used as long as it is common between the index value item associated with each layout and the learning value item associated with each user.

  In the process of step S206, two highest layout types, two intermediate layout types, and one lowest layout type are acquired. However, the present invention is not limited to this. As long as a high layout type is included, any number of layout types in any order may be acquired.

(Second acquisition process)
Next, the second obtaining process performed by the server 4 of the present embodiment will be described with reference to FIGS. FIG. 13 is a flowchart illustrating an example of the second acquisition process. FIG. 14 is a diagram illustrating an example of a morpheme characteristic value total obtained based on information input to the product name input field 51 and the catch phrase input field 53 by the user.

  First, as shown in FIG. 13, the CPU 41 performs morphological analysis on the product name input in the product name input field 51 and the catch phrase input in the catch phrase input field 53, and extracts morphemes contained in those texts. (Step S301). For example, as shown in FIG. 9, when “Yuzu Shio Ramen” is input in the product name input field 51, the CPU 41 performs morphological analysis on the “Yuzu Shio Ramen” morpheme. To extract. As shown in FIG. 9, when “a refreshing ramen with a flavor of citron” is entered in the catch phrase input field 53, the CPU 41 performs a morphological analysis to obtain “Yuzu”, “ ”,“ Flavor ”,“ ga ”,“ fragrance ”,“ fresh ”,“ shi ”,“ ta ”,“ ramen ”,“ is ”and“. ”Are extracted.

  If neither the product name nor the catch phrase is input in the product name input field 51 and the catch phrase input field 53 and the image data of the target product is input in the image data input field 54, the CPU 41 Then, image analysis is performed on the input image data, and the CPU 41 may specify the name (word) of the product indicated by the image data. For example, as shown in FIG. 9, when “ramen” image data is input to the image data input field 54, the CPU 41 analyzes the image, and “ramen” is the name of the product indicated by the image data. It is good also as what specifies the word of "."

Next, as illustrated in FIG. 13, the CPU 41 acquires a morpheme characteristic value sum from the morpheme extracted by the morpheme analysis (step S <b> 302). Specifically, for example, the CPU 41 stores “Yuzu”, “Salt” and “Ramen” extracted from the product name of “Yuzu salt ramen” input in the product name input field 51, and the catch phrase input field 53. "Yuzu", "no", "flavor", "ga", "scent", "smooth", "shi" For each morpheme of “ta”, “ramen”, “is”, and “.”, Characteristic values set in advance for each word are acquired from the storage unit 45 as shown in FIG. As described above, the storage unit 45 stores characteristic values set in advance for each item in association with words.
CPU41 totals the characteristic value set to each word for every item, and obtains the morpheme characteristic value total for every item. For the item “vertical / horizontal”, the total value of characteristic values is divided by the total number of nouns, adverbs, verbs, and adjectives to calculate the total morpheme characteristic value. For example, since the total value of the characteristic values of the item “vertical / horizontal” is “6” and the total number of nouns, adverbs, verbs and adjectives is “9”, the morphological characteristic value of the item “vertical / horizontal” The total is 6 ÷ 9 = 0.667.

  Next, as shown in FIG. 13, the CPU 41 selects one unacquired POP data component from among the POP data components of eye catch, typeface, font color, background, frame / texture and illustration (step S303). Hereinafter, a case where eye catch is selected among the respective POP data components will be described.

Next, the CPU 41 calculates the total morpheme characteristic value acquired in step S302 (see FIG. 14), the index value set for each item for the eye catch (see FIG. 4), and the learning value associated with each user (see FIG. 4). Based on the following formula (2), an evaluation value is calculated based on (see FIG. 5) (step S304). Here, evaluation values are calculated for all eye catch species stored in the storage unit 45.
Formula (2): Evaluation value = index value of “vertical / horizontal” of eye catch × total morpheme characteristic value of “vertical / horizontal” + index value of “for Japanese food” of eye catch × morphological characteristic value of “for Japanese food” Total x User's "For Japanese food" learning value + Eye catch "For Western food" index value * "For Western food" morpheme characteristic value x User's "For Western food" learning value + Eye catch "For cafe" Index value x "Cafe" total morpheme characteristic value x User's "Cafe" learning value + Eye catch "Chinese" index value x "Chinese" morpheme characteristic value total x User's "For Chinese" Learning value

For example, when the user code of the user is “000001”, the evaluation value of the eye catch type i1 is as follows.
Evaluation value of eye catch species i1 = 0 × 0.667 + 1 × 6 × 0.2 + 1 × 2 × 0.9 + 1 × 5 × 0.7 + 0 × 2 × 0.5 = 6.5

For example, when the user code of the user is “000001”, the evaluation value of the eye catch type i2 is as follows.
Evaluation value of eye catch species i2 = 0 × 0.667 + 1 × 6 × 0.2 + 0 × 2 × 0.9 + 1 × 5 × 0.7 + 1 × 2 × 0.5 = 5.7

  Next, as shown in FIG. 13, the CPU 41 acquires an eye catch from the storage unit 45 based on the evaluation value (step S305). Specifically, for example, the CPU 41 ranks the eye catch species in descending order of the evaluation value calculated in step S304. Then, the CPU 41 acquires, from the storage unit 45, two eye catch species with the highest ranking, two eye catch species with the middle ranking, and one eye catch species with the lowest ranking. Since 20 types of eye-catching species are stored in the storage unit 45, eye-catching species whose evaluation values are ranked first, second, tenth, eleventh and twentieth are acquired.

Next, the CPU 41 determines whether or not there is an unacquired POP data component among the POP data components of eye catch, typeface, font color, background, frame / texture, and illustration (step S306). If it is determined that there is an unacquired POP data component (step S306; YES), the CPU 41 performs the process of step S303 again. On the other hand, if it is determined that there is no unacquired POP data component (step S306; NO), the CPU 41 ends the second acquisition process.
The second acquisition process is performed as described above.

  In step S302, the total morpheme characteristic value is calculated based on the information input in the product name input field 51 and the catch phrase input field 53. However, the present invention is not limited to this. For example, the CPU 41 may further calculate the total morpheme characteristic value based on the user's business type / business condition stored as user information in the storage unit 45, the current season / event specified by the time measuring unit 47, and the like. . In this case, the storage unit 45 stores characteristic values for each item in advance in association with the type of business / business category, season, and event. For example, for the business type / business type of “Chinese restaurant” The characteristic value may be set to “1”, and the characteristic value of “1” may be added when calculating the total morpheme characteristic value. For example, for the “Christmas” season / event, the characteristic value of the item “For Western food” is set to “1”, and the characteristic value of “1” is added when calculating the total morpheme characteristic value. Also good.

  In the process of step S304, the evaluation value is calculated based only on the items “vertical / horizontal”, “for Japanese food”, “for western food”, “for cafe”, and “for Chinese”. It is not limited to. For example, any combination is possible as long as it is common between the item of the morpheme characteristic value total, the item of the index value associated with each eye catch, and the item of the learning value associated with each user. There may be.

  In the process of step S305, two highest-level POP data components, two middle-level POP data components, and one lowest-level POP data component are acquired. However, the present invention is not limited to this. However, as long as POP data components having higher ranks are included, any number of POP data components may be acquired in any quantity.

(Learning process)
Next, the learning process performed by the server 4 of this embodiment will be described with reference to FIGS. 15 and 16. FIG. 15 is a flowchart illustrating an example of the learning process. FIG. 16 is a diagram showing POP data components included in each of the three types of POP candidate data.

  First, as shown in FIG. 15, the CPU 41 performs processing in step S <b> 109 after 25 types of POP candidate data are displayed on the terminal display unit 24 in the processing in step S <b> 106 in the POP data creation processing counted by the timer unit 47. The time until the user desired POP candidate data is selected is acquired (step S401).

  Next, the CPU 41 determines whether or not the time acquired in step S401 is less than 10 seconds (step S402).

  If it is determined that the time is less than 10 seconds (step S402; YES), the CPU 41 sets the coefficient used when calculating the learning value in the process of step S410 described later to “3” (step S403). As a result, when the POP candidate data is selected in a short time of less than 10 seconds, weighting corresponding to the selection of “3” times of the POP candidate data is performed.

  If it is determined that it is not less than 10 seconds, that is, the time acquired in step S401 is 10 seconds or more (step S402; NO), the CPU 41 determines whether or not the time acquired in step S401 is less than 20 seconds. Is determined (step S404).

  If it is determined that the time is less than 20 seconds (step S404; YES), the CPU 41 sets the coefficient used when calculating the learning value in the process of step S410 described later to “2” (step S405). Thereby, when the POP candidate data is selected in 10 seconds or more and less than 20 seconds, the weighting corresponding to the selection of “2” times of the POP candidate data is performed.

  If it is determined that it is not less than 20 seconds, that is, the time acquired in step S401 is 20 seconds or more (step S404; NO), the CPU 41 is used when calculating a learning value in the process of step S410 described later. The coefficient is set to “1” (step S406). Thereby, when the POP candidate data is selected over a long time of 20 seconds or more, weighting corresponding to the selection of the POP candidate data for “1” times is performed.

  After the process of step S405 or S406, the CPU 41 determines whether there is POP candidate data whose display time displayed on the terminal display unit 24 is a predetermined value or more among the 25 types of POP candidate data (step S407). .

  If it is determined that there is no POP candidate data whose display time is equal to or greater than the predetermined value (step S407; NO), the CPU 41 omits steps S408 and S409, which will be described later, and performs step S410.

  If it is determined that there is POP candidate data whose display time is greater than or equal to the predetermined value (step S407; YES), the CPU 41 shares the same with the user-desired POP candidate data selected in step S109. It is determined whether there is a POP data component (step S408). Specifically, among the 25 types of POP candidate data shown in FIG. 11, the POP candidate data a2 selected in the process of step S109 is the POP candidate data c3 whose display time is a predetermined value or more. In the case of the two types e5, the POP candidate data a2, c3, and e5 each include, for example, POP data components shown in FIG. In this case, the POP candidate data a2, c3, and e5 all include “typeface C” as “typeface” and “background5” as “background”. Therefore, it is determined that the POP candidate data c3 and e5 have a POP data component common to the user-desired POP candidate data a2.

  If it is determined that there is no common POP data component (step S408; NO), the CPU 41 omits the process of step S409, which will be described later, and performs the process of step S410.

  When it is determined that there is a common POP data component (step S408; YES), the CPU 41 calculates a learning value for the common POP data component determined to be present in the process of step S410 described later. The coefficient used at this time is set to “3” (step S409).

After the process of step S403 or S409, the CPU 41 calculates a learning value based on the selection result in the process of step S109 of the POP data creation process (step S410). Specifically, the CPU 41 learns each item (see FIG. 5) associated with each user calculated at the time of the previous POP data creation, and the number of times POP data was created in the past including this time (POP creation). The learning value is calculated according to the following equation (3) based on the number of times), the selection result in step S109, and the coefficients set in steps S403, S405, S406, and S409. Calculation of the learning value by the following equation (3) is executed for each item included in the learning value data table 453 shown in FIG.
Expression (3): Learning value = (Learning value of user up to previous time × POP creation count + POP data constituent element index value included in selected POP candidate data × coefficient) / (POP creation count + coefficient)

For example, when the number of POP creations is 20, the eye catch included in the POP candidate data selected in step S109 is the eye catch type i1, the user code of the user is “000001”, and the coefficient is “3”. The learning values for “Japanese food” and “Chinese food” in eye catch are as follows.
Learning value for “Japanese food” = (0.2 × 20 + 1 × 3) / (20 + 3) = 0.304
Learning value for “Chinese” = (0.5 × 20 + 0 × 3) / (20 + 3) = 0.435

Next, as illustrated in FIG. 15, the CPU 41 updates the learning value stored in the learning value data table 453 to the learning value calculated in step S410 (step S411).
The learning process is performed as described above.

  Note that the determination is made based on 10 seconds in step S402 and 20 seconds in step S404. However, the determination is not limited to this, and the reference may be set to any value. In addition, the reference may be changed by user input.

  Further, in steps S403, S405, and S406, the coefficients are set to “3”, “2”, and “1”, respectively, but the present invention is not limited to this, and the coefficient is set to any value. May be. The coefficient values set in steps S403, S405, and S406 may be changed by user input.

  Further, in the above steps S407 and S408, if there is POP candidate data whose display time is a predetermined value or more, whether or not POP data components common to the selected user-desired POP candidate data are included. However, the present invention is not limited to this. For example, among 25 types of POP candidate data displayed on the terminal display unit 24, a predetermined number of POP candidate data is specified in order from the longest display time, and the selected user-desired POP candidate is selected among them. It may be determined whether or not a POP data component common to the data is included.

In addition, although the processing of steps S407 to S409 is performed when the coefficient is set to “2” or “1” in the processing of steps S405 and S406, the present invention is not limited to this. For example, even when the coefficient is set to “3” in the process of step S403, the processes of steps S407 to S409 may be performed.
Moreover, it is good also as what does not perform the process of said step S407-S409.

  In step S409, the coefficient is set to “3” for the common POP data component, but the present invention is not limited to this, and the coefficient may be set to any value. The coefficient value set in step S409 may be changed by a user input.

<< Technical Effects of this Embodiment >>
According to the present embodiment, the server 4 creates a predetermined number of POP candidate data, displays the generated predetermined number of POP candidate data on the terminal display unit 24, and displays a predetermined number of POP candidate data displayed on the terminal display unit 24. Based on the user input, the user's desired POP candidate data is selected from the POP candidate data, and the user is based on the time from when a predetermined number of POP candidate data is displayed until the desired POP candidate data is selected. A CPU 41 for deriving a learning value indicating the preference of the user, and the CPU 41 creates a predetermined number of POP candidate data based on the derived learning value, so that repeated POP data is generated to obtain repeated POP data. In addition, POP data closer to the user's preference can be obtained in a short time.

  Further, since the CPU 41 derives and updates the learning value every time desired POP candidate data is selected, POP data closer to the user's preference can be obtained.

  In addition, when the time until the desired POP candidate data is selected is less than a predetermined value, the CPU 41 increases the amount of change in the learning value compared to when the time is greater than or equal to the predetermined value. It can be determined that the POP candidate data is closer to the user's preference as the length is shorter, and POP data closer to the user's preference can be obtained at the next POP data creation.

  In addition, when the time until the desired POP candidate data is selected is equal to or greater than a predetermined value, the CPU 41 makes the change amount of the learning value smaller than when the time is less than the predetermined value. It can be determined that the POP candidate data is farther from the user's preference as the length is longer, and POP data closer to the user's preference can be obtained when the next POP data is created.

  Further, the CPU 41 displays any one of the predetermined number of POP candidate data on the terminal display unit 24, and during the time from when the predetermined number of POP candidate data is displayed until the desired POP candidate data is selected, the POP Since the learning value is derived based on the display time of the candidate data, a more accurate learning value can be derived.

  In addition, when the POP candidate data whose display time is equal to or greater than a predetermined value includes a POP data component common to the desired POP candidate data, the CPU 41 has a common POP for the common POP data component. Since the amount of change in the learning value is larger than when the data component is not included, it can be determined that the common POP data component is the user's preference, and a more accurate learning value can be derived.

  In addition, the storage unit 45 that stores the learning value associated with each user is provided, and the CPU 41 stores the derived learning value in the storage unit 45 and acquires the learning value from the storage unit 45. POP data close to the user's preference can be obtained.

<Others>
The description in the above embodiment is an example of a suitable information processing apparatus according to the present invention, and the present invention is not limited to this.

  For example, in the above-described embodiment, the server 4 stores various data in the storage unit 45 and displays various display screens on the terminal display unit 24 functioning as a display unit. However, the present invention is not limited to this. Absent. For example, the server 4 may be configured to store various data in the storage unit 45 and display various display screens on the server display unit 44 functioning as a display unit. The mobile terminal 2a may be configured to store various data in the storage unit 25 and display various display screens on the terminal display unit 24. In this case, the mobile terminal 2a itself serves as an information processing device. Function.

  In the above-described embodiment, the generated plural types of POP candidate data are displayed one by one on the preview screen 6 one by one. However, the present invention is not limited to this. For example, the generated plural types of POP candidate data are displayed. The POP data may be displayed in order, or may be displayed as a list.

  In the above-described embodiment, information related to a product is input to the input screen 5 and POP data that introduces the product is created. However, the present invention is not limited to this. Information may be input and POP data introducing the service may be created.

  In the embodiment described above, the learning value is derived for each item of the POP data component, but the present invention is not limited to this. That is, a learning value may be derived for each POP data component. For example, learning values are derived for layout types L1, L2,..., L20, eye catch types i1, i2,..., I20, respectively, and each of font type, font color, background, frame / texture, and illustration. Similarly, a learning value may be derived for the POP data component.

  In the above embodiment, the learning value is derived according to the above equation (3). However, the present invention is not limited to this, and the learning value is derived based on the time until the POP candidate data is selected. If so, the learning value may be derived under any condition. For example, according to the time until the POP candidate data is selected, points are given to the POP data components included in the POP candidate data selected by the user, and the total value of points given in the past is learned It may be derived as a value.

  In the above-described embodiment, the above formula is based on the time from when a predetermined number of POP candidate data is displayed on the terminal display unit 24 until the desired POP candidate data is selected and the display time by the terminal display unit 24. Although the coefficient in (3) is set and the learning value is derived, the present invention is not limited to this. For example, the coefficient may be set based on the breakdown of the time from when a predetermined number of POP candidate data is displayed on the terminal display unit 24 until the desired POP candidate data is selected, that is, the display time by the terminal display unit 24. Alternatively, the POP candidate data may be ranked in the order in which the display time is long, and the coefficient may be set for the POP data components included in the POP candidate data according to the rank.

In the above-described embodiment, the CPU 41 acquires the POP data component based on the name of the target product input by the user, and the name of the target product input by the user. Etc., a predetermined number of POP candidate data is created and displayed on the terminal display unit 24. However, the present invention is not limited to this.
For example, the CPU 41 may create a predetermined number of POP candidate data and display them on the terminal display unit 24 by appropriately inputting information or selecting POP data components.
Further, for example, the CPU 41 may extract a predetermined number of POP candidate data from the created POP data, and display the extracted predetermined number of POP candidate data on the terminal display unit 24. Thereby, when repeating POP data is obtained by repeatedly extracting POP data, POP data closer to the user's preference can be obtained in a short time. In this case, the CPU 41 functions as an extraction unit. The POP candidate data may be extracted from the storage unit 45 or an external device (not shown).

  In the above-described embodiment, the CPU 41 derives a learning value for each POP data component based on the time from when a predetermined number of POP candidate data is displayed until the desired POP candidate data is selected. However, it is not limited to this. For example, the learning value may be derived for each POP data based on the time from when a predetermined number of POP candidate data is displayed until the desired POP candidate data is selected. In that case, a predetermined number of POP candidate data may be extracted based on the derived learning value. For example, POP candidate data having a high learning value may be preferentially extracted.

  In the above-described embodiment, an example in which a hard disk, a semiconductor nonvolatile memory, or the like is used as a computer-readable medium of the program according to the present invention is disclosed, but the present invention is not limited to this example. As another computer-readable medium, a portable recording medium such as a CD-ROM can be applied. A carrier wave is also used as a medium for providing program data according to the present invention via a communication line.

Although several embodiments of the present invention have been described, the scope of the present invention is not limited to the above-described embodiments, but includes the scope of the invention described in the claims and equivalents thereof.
The invention described in the scope of claims attached to the application of this application will be added below. The item numbers of the claims described in the appendix are as set forth in the claims attached to the application of this application.
[Appendix]
<Claim 1>
Creating means for creating a predetermined number of ad candidates;
Display control means for causing the display means to display the predetermined number of advertisement candidates created by the creation means;
Selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means, based on user input;
Learning means for deriving a learning value indicating a user's preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means; Prepared,
The information processing apparatus characterized in that the creation means creates the predetermined number of advertisement candidates based on the learning value derived by the learning means.
<Claim 2>
The information processing apparatus according to claim 1, wherein the learning unit derives and updates the learning value every time the desired advertisement candidate is selected by the selection unit.
<Claim 3>
When the time until the desired advertisement candidate is selected is less than a predetermined value, the learning means increases the amount of change in the learning value as compared with a case where the time is greater than or equal to a predetermined value. The information processing apparatus according to claim 1 or 2.
<Claim 4>
When the time until the desired advertisement candidate is selected is greater than or equal to a predetermined value, the learning means makes the amount of change in the learned value smaller than when the time is less than the predetermined value. The information processing apparatus according to any one of claims 1 to 3.
<Claim 5>
The display control means causes the display means to display any one of the predetermined number of advertisement candidates,
In the time from when the predetermined number of advertisement candidates are displayed by the display control means to when the desired advertisement candidate is selected by the selection means, the learning means determines the display time of the advertisement candidates by the display control means. The information processing apparatus according to claim 1, wherein the learning value is derived based on the information.
<Claim 6>
In the case where an advertisement component common to the desired advertisement candidate is included in the advertisement candidates whose display time is equal to or greater than a predetermined value, the learning means may include the common advertisement for the common advertisement component. The information processing apparatus according to claim 5, wherein the amount of change in the learning value is made larger than when no component is included.
<Claim 7>
Storage means for storing the learning value associated with each user;
The learning means stores the derived learning value in the storage means,
The information processing apparatus according to claim 1, wherein the creation unit acquires the learning value from the storage unit.
<Claim 8>
Extracting means for extracting a predetermined number of advertisement candidates from a plurality of created advertisements;
Display control means for causing the display means to display the predetermined number of advertisement candidates extracted by the extraction means;
Selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means, based on user input;
Learning means for deriving a learning value indicating a user's preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means; Prepared,
The information processing apparatus, wherein the extraction unit extracts the predetermined number of advertisement candidates based on the learning value derived by the learning unit.
<Claim 9>
Computer
Creating means for creating a predetermined number of ad candidates,
Display control means for causing the display means to display the predetermined number of advertisement candidates created by the creation means;
A selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means based on a user's input;
Learning means for deriving a learning value indicating a user preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means to when the desired advertisement candidate is selected by the selection means;
Is a program for functioning as
The creation unit creates the predetermined number of advertisement candidates based on the learning value derived by the learning unit.
<Claim 10>
Computer
Extraction means for extracting a predetermined number of advertisement candidates from a plurality of created advertisements;
Display control means for causing the display means to display the predetermined number of advertisement candidates extracted by the extraction means;
A selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means based on a user's input;
Learning means for deriving a learning value indicating a user preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means to when the desired advertisement candidate is selected by the selection means;
Is a program for functioning as
The extraction means extracts the predetermined number of advertisement candidates based on the learning value derived by the learning means.

4 servers (information processing equipment)
24 Terminal display (display means)
41 CPU (creation means, display control means, selection means, learning means, extraction means)
45 Storage unit (storage means)

Claims (10)

Creating means for creating a predetermined number of ad candidates;
Display control means for causing the display means to display the predetermined number of advertisement candidates created by the creation means;
Selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means, based on user input;
Learning means for deriving a learning value indicating a user's preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means; Prepared,
The information processing apparatus characterized in that the creation means creates the predetermined number of advertisement candidates based on the learning value derived by the learning means.   The information processing apparatus according to claim 1, wherein the learning unit derives and updates the learning value every time the desired advertisement candidate is selected by the selection unit.   When the time until the desired advertisement candidate is selected is less than a predetermined value, the learning means increases the amount of change in the learning value as compared with a case where the time is greater than or equal to a predetermined value. The information processing apparatus according to claim 1 or 2.   When the time until the desired advertisement candidate is selected is greater than or equal to a predetermined value, the learning means makes the amount of change in the learned value smaller than when the time is less than the predetermined value. The information processing apparatus according to any one of claims 1 to 3. The display control means causes the display means to display any one of the predetermined number of advertisement candidates,
In the time from when the predetermined number of advertisement candidates are displayed by the display control means to when the desired advertisement candidate is selected by the selection means, the learning means determines the display time of the advertisement candidates by the display control means. The information processing apparatus according to claim 1, wherein the learning value is derived based on the information.   In the case where an advertisement component common to the desired advertisement candidate is included in the advertisement candidates whose display time is equal to or greater than a predetermined value, the learning means may include the common advertisement for the common advertisement component. The information processing apparatus according to claim 5, wherein the amount of change in the learning value is made larger than when no component is included. Storage means for storing the learning value associated with each user;
The learning means stores the derived learning value in the storage means,
The information processing apparatus according to claim 1, wherein the creation unit acquires the learning value from the storage unit. Extracting means for extracting a predetermined number of advertisement candidates from a plurality of created advertisements;
Display control means for causing the display means to display the predetermined number of advertisement candidates extracted by the extraction means;
Selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means, based on user input;
Learning means for deriving a learning value indicating a user's preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means; Prepared,
The information processing apparatus, wherein the extraction unit extracts the predetermined number of advertisement candidates based on the learning value derived by the learning unit. Computer
Creating means for creating a predetermined number of ad candidates,
Display control means for causing the display means to display the predetermined number of advertisement candidates created by the creation means;
A selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means based on a user's input;
Learning means for deriving a learning value indicating a user preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means to when the desired advertisement candidate is selected by the selection means;
Is a program for functioning as
The creation unit creates the predetermined number of advertisement candidates based on the learning value derived by the learning unit. Computer
Extraction means for extracting a predetermined number of advertisement candidates from a plurality of created advertisements;
Display control means for causing the display means to display the predetermined number of advertisement candidates extracted by the extraction means;
A selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means based on a user's input;
Learning means for deriving a learning value indicating a user preference based on a time from when the predetermined number of advertisement candidates are displayed by the display control means to when the desired advertisement candidate is selected by the selection means;
Is a program for functioning as
The extraction means extracts the predetermined number of advertisement candidates based on the learning value derived by the learning means.

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