Patent Publication Number: US-2017364946-A1

Title: Determination device, determination method, and non-transitory computer readable storage medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     The present application claims priority to and incorporates by reference the entire contents of Japanese Patent Application No. 2016-120584 filed in Japan on Jun. 17, 2016. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a determination device, a determination method, and a non-transitory computer readable storage medium. 
     2. Description of the Related Art 
     Conventionally, a technology to determine content to be distributed to a terminal device used by a user has been provided. For example, a technology to check app products already installed in the terminal device, to exclude the installed app products from advertisement targets, and to display advertisements of only app products that have not yet been installed is provided. 
     However, appropriate determination of the content to be distributed to the terminal device by the above-described conventional technology may be difficult. For example, only the determination of excluding the already installed app products from the advertisement targets, and displaying the advertisements of the app products that have not yet been installed cannot necessarily determine an appropriate advertisement to be distributed to individual users. 
     SUMMARY OF THE INVENTION 
     It is an object of the present invention to at least partially solve the problems in the conventional technology. 
     According to one aspect of an embodiment, a determination device includes an acquisition unit that acquires user information that is information regarding a user who uses a terminal device that becomes a providing destination of content, a calculation unit that calculates scores regarding a probability of the user performing a predetermined behavior for a plurality of pieces of the content on the basis of the user information acquired by the acquisition unit, and a determination unit that determines distribution content to be distributed to the terminal device on the basis of the scores of the plurality of pieces of content calculated by the calculation unit. 
     The above and other objects, features, advantages and technical and industrial significance of this invention will be better understood by reading the following detailed description of presently preferred embodiments of the invention, when considered in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating an example of determination processing according to an embodiment; 
         FIG. 2  is a diagram illustrating an example of determination processing according to an embodiment; 
         FIG. 3  is a diagram illustrating a configuration example of a determination device according to an embodiment; 
         FIG. 4  is a diagram illustrating an example of a user attribute information storage unit according to an embodiment; 
         FIG. 5  is a diagram illustrating an example of a terminal information storage unit according to an embodiment; 
         FIG. 6  is a diagram illustrating an example of an app information storage unit according to an embodiment; 
         FIG. 7  is a diagram illustrating an example of an individual score information storage unit according to an embodiment; 
         FIG. 8  is a diagram illustrating an example of an advertising information storage unit according to an embodiment; 
         FIG. 9  is a flowchart illustrating an example of determination processing according to an embodiment; 
         FIG. 10  is a flowchart illustrating an example of determination processing according to an embodiment; 
         FIG. 11  is a diagram illustrating an example of ranking change according to an embodiment; 
         FIG. 12  is a diagram illustrating an example of ranking change according to an embodiment; 
         FIG. 13  is a diagram illustrating an example of ranking change according to an embodiment; 
         FIG. 14  is a diagram illustrating an example of ranking change according to an embodiment; 
         FIG. 15  is a diagram illustrating an example of ranking change according to an embodiment; 
         FIG. 16  is a flowchart illustrating an example of determination processing by ranking change according to an embodiment; 
         FIG. 17  is a diagram illustrating an example of determination of an advertisement based on rarity of a user in apps according to an embodiment; and 
         FIG. 18  is a hardware configuration diagram illustrating an example of a computer that realizes functions of the determination device. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Hereinafter, embodiments for implementing a determination device, a determination method, and a determination program according to the present application (hereinafter, called “embodiment”) will be described in detail with reference to the drawings. Note that the determination device, the determination method, and the determination program according to the present application are not limited by the embodiments. Further, the same portion in the embodiments is denoted with the same reference sign, and overlapping description is omitted. 
     EMBODIMENT 
     1. Determination Processing 
     First, an example of determination processing according to an embodiment will be described using  FIGS. 1 and 2 .  FIGS. 1 and 2  are diagrams illustrating an example of determination processing according to an embodiment. In the example illustrated in  FIGS. 1 and 2 , a case in which an application (hereinafter, simply referred to as “app”) is content, an advertisement of the app is distribution content, and installation of the app is a predetermined behavior will be described. That is, in  FIGS. 1 and 2 , a determination device  100  calculates a score regarding a probability of a user performing installation for each of a plurality of apps, and determines the advertisement of the app to be distributed to a terminal device on the basis of the calculated scores. Further, in the example illustrated in  FIGS. 1 and 2 , a case in which users U 1  to U 4  respectively use terminal devices  10 - 1  to  10 - 4  will be described. When description is given without distinguishing the terminal devices  10 - 1  to  10 - 4 , these terminal devices are collectively referred to as terminal device  10 . 
     As illustrated in  FIGS. 1 and 2 , a determination system  1  includes the terminal device  10  and the determination device  100 . The terminal device  10  and the determination device  100  are communicatively connected by wired or wireless means through a predetermined communication network (not illustrated). Note that the determination system  1  illustrated in  FIG. 1  may include a plurality of the terminal devices  10  and a plurality of the determination devices  100 . 
     The terminal device  10  is an information processing device used by the user. The terminal device  10  is realized by a smart phone, a tablet-type terminal, a note-type personal computer (PC), a desktop PC, a mobile phone device, a personal digital assistant (PDA), or the like.  FIGS. 1 and 2  illustrate a case in which the terminal device  10  is a smart phone. 
     Further, the terminal device  10  receives an operation by the user. In the example illustrated in  FIG. 2 , the terminal device  10  requests the determination device  100  of content to be displayed in a predetermined app (for example, a browser). Note that, hereinafter, the terminal device  10  may be described as user. That is, hereinafter, the user can be read as terminal device  10 . 
     The determination device  100  is an information processing device that calculates the score regarding a probability of a user performing installation for each of the plurality of apps on the basis of user information. Further, the determination device  100  is an information processing device that determines the advertisement of the app to be distributed to the terminal device  10  on the basis of the scores of the apps. For example, the determination device  100  is an information processing device that distributes the determined advertisement of the app to the terminal device  10 . 
     The determination device  100  collects the user information of the users from the terminal devices  10  of the users. Note that the user information referred to here may include any information as long as the information is related to the user. For example, the user information may include any information as long as the information is characteristic information indicating a characteristic of the user. For example, the user information may include various types of information such as information regarding demographic attributes of the user, information regarding psychographic attribute, and information regarding the terminal device  10  used by the user. Further, for example, the information regarding the terminal device  10  may include information regarding a specification of the terminal device  10 , information regarding the app installed in the terminal device  10 , and the like. Note that, hereinafter, an example in which the determination device  100  collects the user information of the users from the terminal devices  10  of the users will be described. However, the determination device  100  may acquire the user information of the users from an external device other than the terminal devices  10  of the users. 
     In the example of  FIG. 1 , the determination device  100  acquires the user information of the user U 1  from the terminal device  10 - 1  (step S 11 - 1 ). Further, the determination device  100  acquires the user information of the user U 2  from the terminal device  10 - 2  (step S 11 - 2 ). Further, the determination device  100  acquires the user information of the user U 3  from the terminal device  10 - 3  (step S 11 - 3 ). Further, the determination device  100  acquires the user information of the user U 4  from the terminal device  10 - 4  (step S 11 - 4 ). 
     Note that steps S 11 - 1  to S 11 - 4  are processes for describing the processing. Any of steps S 11 - 1  to S 11 - 4  may be performed first, and steps S 11 - 1  to S 11 - 4  may be performed a plurality of times. For example, steps S 11  are performed a plurality of times at predetermined timing, so that the determination device  100  may acquire the user information of the users. Hereinafter, when description is given without distinguishing steps S 11 - 1  to S 11 - 4 , these steps are collectively referred to as step S 11 . For example, steps S 11  may be performed together with advertisement requests (see step S 13  in  FIG. 2 ) that are distribution requests of the advertisement from the corresponding terminal devices  10  of the users. For example, the determination device  100  may acquire the user information of the user U 1  from the terminal device  10 - 1  when the advertisement request is made from the terminal device  10 - 1  of the user U 1 . 
     Then, the determination device  100  stores the user information of the users collected in step S 11  to a storage unit  120  (see  FIG. 3 ). Note that the user information stored to the storage unit  120  by the determination device  100  may be user information estimated from the user information of the users collected in step S 11 . For example, the determination device  100  may estimate a residential area of the user U 1  on the basis of position information of the user U 1  collected in step S 11 , and store the estimated residential area to a user attribute information storage unit  121 . 
     In the example of  FIG. 1 , the determination device  100  stores information regarding user attributes of the users (hereinafter, also referred to as “user attribute information”) to the user attribute information storage unit  121 . For example, the determination device  100  stores the user attribute information indicating that the sex of the user U 1  is “male”, the age is “30&#39;s”, and the residential area is “A prefecture” to the user attribute information storage unit  121  on the basis of the user information collected in step S 11 . 
     Further, in the example of  FIG. 1 , the determination device  100  stores information regarding the terminal devices  10  used by the users (hereinafter, also referred to as “terminal information”) to a terminal information storage unit  122 . For example, the determination device  100  stores the terminal information indicating that the model number is “XX-YY01”, the (terminal) brand name is “AAA”, the number of days elapsed since the terminal device was on sale is “336 days”, and the like about the terminal device  10 - 1  used by the user U 1 , to the terminal information storage unit  122 , on the basis of the user information collected in step S 11 . For example, the determination device  100  may calculate the number of days elapsed since the terminal device was on sale on the basis of the model number and the like acquired from the terminal device  10 . Further, for example, the determination device  100  may inquire of an external device about the model number and the like acquired from the terminal device  10 , and acquire the number of days elapsed since the terminal device was on sale from the external device. 
     Although not illustrated, the determination device  100  collects, beyond the above-described information, various types of user information such as information regarding the app already installed in the terminal device  10  (see  FIG. 6 ). 
     Then, the determination device  100  generates information regarding an individual score (may also be simply referred to as “individual score information” or “individual score”) on the basis of the user information acquired in step S 11  (step S 12 ). For example, the determination device  100  generates the individual score information for each app. In the example of  FIG. 1 , the determination device  100  generates the individual score information for each of an app A, an app B, an app C, and the like, and stores the individual score information to an individual score information storage unit  124 . To be specific, the determination device  100  generates individual score information  124 - 1  regarding the app A, and stores the individual score information  124 - 1  to the individual score information storage unit  124 . Further, the determination device  100  generates individual score information  124 - 2  regarding the app B, and stores the individual score information  124 - 2  to the individual score information storage unit  124 . Further, the determination device  100  generates individual score information  124 - 3  regarding the app C, and stores the individual score information  124 - 3  to the individual score information storage unit  124 . 
     Further, as illustrated in the individual score information storage unit  124  in  FIG. 1 , the determination device  100  generates the individual score information for each user information. For example, the determination device  100  generates the individual score information indicating that the individual score in a case where a category “terminal brand name” is “AAA” is “0.5”, the individual score in a case of “BBB” is “0.1”, and the individual score in a case of “CCC” is “0.8”, about the app A. 
     Further, the determination device  100  may generate the individual score information by learning, using the user information as features, weights of the features, that is, the individual scores. For example, the determination device  100  learns the weights (individual scores) of the features, using “AAA”, “BBB”, “CCC”, and the like in the category “terminal brand name”, or “system A”, “system B”, “system C”, and the like in a category “OS”, as features. For example, the determination device  100  learns the weights (individual scores) of the features, using “A prefecture”, “B prefecture”, “C prefecture”, and the like in a category “area”, as features. 
     For example, the determination device  100  may generate the individual score information regarding the apps, by leaning the weights (individual scores) of the features (user information), using the user information of the users who have installed the apps, of the user information collected in step S 11 , as positive examples. For example, the determination device  100  may generate the individual score information regarding the app A by leaning the weight (individual score) of the features (user information), using the user information of the users who have installed the app A, of the user information collected in step S 11 , as positive examples. 
     Further, for example, the determination device  100  may generate the individual score information regarding the apps by learning the weights (individual scores) of the features (user information), using the user information of the users who have not installed the apps, of the user information collected in step S 11 , as negative examples of the apps. For example, the determination device  100  may generate the individual score information regarding the app A by learning the weight (individual score) of the features (user information), using the user information of the users who have not installed the app A, of the user information collected in step S 11 , as negative examples. 
     For example, the determination device  100  may generate the individual score information regarding the apps by learning the weights (individual scores) of the features (user information), using the following formula (1). 
         y   i =ω 1   ·x   1 +ω 2   ·x   2 +ω 3   ·x   3  . . . ω N   ·x   N   (1)
 
     “N” of “ω N ” and “x N ” in the right side in the above formula (1) represents an arbitrary number. Further, “i” of “y i ” in the left side of the above formula (1) represents which app is targeted. For example, “y 1 ” represents a phenomenon as to whether the app A is to be installed into the terminal device  10 . In other words, “y 1 ” represents a probability of the app A being installed into the terminal device  10 . Further, for example, “y 2 ” represents a phenomenon as to whether the app B is to be installed into the terminal device  10 . For example, “i” of “y i ” in the left side of the above formula (1) corresponds to the number of apps, the scores of which are to be calculated. 
     Further, in the above formula (1), “x” corresponds to the user information (feature). For example, “x 1 ” in the above formula (1) corresponds to “AAA” in the category “terminal brand name”. Further, for example, “x 2 ” in the above formula (1) corresponds to “BBB” in the category “terminal brand name”, and “x 3 ” corresponds to “CCC” in the category “terminal brand name”. Further, in the above formula (1), “ω” represents a coefficient of “x”, and indicates the weight. For example, in the above formula (1), “ω 1 ” represents a weight value of “x 1 ”, “ω 2 ” is a weight value of “x 2 ”, and “ω 3 ” is a weight value of “x 3 ”. 
     Note that the above description is an example, and the determination device  100  may generate the individual score information by any method as long as the determination device  100  generates information regarding the individual scores on the basis of the user information. 
     Next, an example of determining the advertisement to be distributed to the terminal device  10  of the user, using the individual score information generated by the determination device  100 , will be described using  FIG. 2 .  FIG. 2  illustrates a case of determining the advertisement of the app to be distributed to the terminal device  10 - 1  used by the user U 1 . 
     First, the determination device  100  acquires the advertisement request from the terminal device  10 - 1  used by the user U 1  (step S 13 ). In  FIG. 2 , the terminal device  10 - 1  requests the determination device  100  of the advertisement displayed in content CT 11 . 
     The determination device  100 , which has acquired the advertisement request from the terminal device  10 - 1 , calculates the scores of the apps (step S 14 ). For example, the determination device  100  calculates the score of the app A, the score of the app B, and the score of the app C about the user U 1 . To be specific, the determination device  100  calculates the score of the app A on the basis of the individual score information  124 - 1  regarding the app A and the user information of the user U 1 . Assume that, in the example of  FIG. 2 , the apps A, B, C, and the like have not been installed in the terminal device  10 - 1 . Further, the determination device  100  may not include, to the target to which the advertisement is distributed, the app installed in the terminal device  10  that requests the advertisement. In this case, the determination device  100  may not calculate the score about the app installed in the terminal device  10  that requests the advertisement. For example, the determination device  100  may not calculate the score about the app B in a case where the app B has already been installed in the terminal device  10  that requests the advertisement. 
     In the example illustrated in  FIG. 2 , the terminal brand name of the user U 1  is “AAA” (see  FIG. 1 ), and thus the individual score “0.5” of “AAA” in the category “terminal brand name” in the individual score information  124 - 1  is added to the score of the app A. Further, in the example illustrated in  FIG. 2 , the residential area of the user U 1  is “A prefecture” (see  FIG. 1 ), and thus the individual score “0.3” of “A prefecture” in the category “area” in the individual score information  124 - 1  is added to the score of the app A. In this way, the determination device  100  calculates the score of the app A by adding up the individual scores corresponding to the user information of the user U 1  in the individual score information  124 - 1 . 
     In the example of  FIG. 2 , the determination device  100  calculates the score of the app A as “3.1 (=0.5+0.3+ . . . )”. Further, in the example of  FIG. 2 , the determination device  100  calculates the score of the app B as “5.4 (=1.2+0.5+ . . . )”, by adding the individual score “1.2” of “AAA” and the individual score “0.5” of “A prefecture” to the score of the app B on the basis of the individual score information  124 - 2 , about the app B. In this way, the determination device  100  calculates the scores of the apps (apps A, B, C, and the like) by performing the above-described processing for the apps (apps A, B, C, and the like). 
     For example, the determination device  100  may calculate the scores of the apps, using the above formula (1). Note that the determination device  100  may calculate the scores of the apps, using the individual scores in any manner, as long as the determination device  100  can calculate the scores of the apps. For example, the determination device  100  may change the weights (individual scores) of the features (user information) on the basis of various types of information. For example, in a case where the determination device  100  has acquired information indicating a tendency for the user who installs a certain app to use the terminal device  10  of a specific terminal brand name “X”, the determination device  100  may make the weight (individual score) of the feature corresponding to the terminal brand name “X” large. That is, the determination device  100  may adjust the score of the app on the basis of the information regarding an affinity between the user and the app. Accordingly, the determination device  100  can appropriately calculate the scores of the apps even if the user information acquirable from the users is limited. Note that the determination device  100  may perform, in a case where normalization is required among the scores of the apps, the normalization of the scores of the apps, and then perform processing below on the basis of the scores after the normalization. In the case where the normalization is required among the scores of the apps, the determination device  100  may perform normalization of the individual scores of the user information about the apps. 
     Then, the determination device  100  generates a ranking of the apps on the basis of the calculated scores of the apps. In the example of  FIG. 2 , the determination device  100  generates ranking information LL 11  of the apps on the basis of the calculated scores of the apps. For example, the determination device  100  generates the ranking information LL 11  of the apps by ranking the calculated scores of the apps in descending order (hereinafter, the ranking may also be referred to as “rank”). To be specific, the determination device  100  generates the ranking information LL 11  indicating that the app B is ranked “first”, an app K is ranked “second”, an app Z is ranked “third”, and the app A is ranked “fourth”. 
     Then, the determination device  100  determines the advertisement to be distributed to the terminal device  10 - 1  of the user U 1  on the basis of the ranking information LL 11  generated in step S 14 , and advertising information stored in an advertising information storage unit  125  (step S 15 ). In the example of  FIG. 2 , the determination device  100  determines an advertisement C 11  of the app B in the highest rank in the ranking information LL 11  as the advertisement to be distributed to the terminal device  10 - 1  of the user U 1 , as illustrated in a distribution advertisement list DA 11 . 
     After that, the determination device  100  distributes the advertisement C 11  of the app B to the terminal device  10 - 1  (step S 16 ). Then, the terminal device  10 - 1  that has received the advertisement C 11  of the app B displays the advertisement C 11  of the app B (step S 17 ). In  FIG. 2 , the terminal device  10 - 1  displays the advertisement C 11  of the app B in the content CT 11 . 
     As described above, the determination device  100  calculates the scores regarding a probability of a user performing installation, for the plurality of apps, and determines the advertisement of the app to be distributed to the terminal device on the basis of the calculated scores. Accordingly, the determination device  100  can appropriately determine the content to be distributed to the terminal device. That is, the determination device  100  can distribute the advertisement of the app having a high probability of being installed by the user, to the terminal device  10  of the user, and thereby to improve an advertisement effect. 
     2. Configuration of Determination Device 
     Next, a configuration of the determination device  100  according to the embodiment will be described using  FIG. 3 .  FIG. 3  is a diagram illustrating a configuration example of the determination device  100  according to an embodiment. As illustrated in  FIG. 3 , the determination device  100  includes a communication unit  110 , a storage unit  120 , and a control unit  130 . Note that the determination device  100  may include an input unit (for example, a keyboard or a mouse) that receives various operations from an administrator or the like of the determination device  100 , and a display unit (for example, a liquid crystal display) for displaying various types of information. 
     Communication Unit  110   
     The communication unit  110  is realized by a network interface card (NIC) and the like, for example. The communication unit  110  is connected with a network by wired or wireless means, and transmits/receives information to/from the terminal device  10 . 
     Storage Unit  120   
     The storage unit  120  is realized by, for example, a random access memory (RAM), a semiconductor memory element such as a flash memory, or a storage device such as a hard disk or an optical disk. As illustrated in  FIG. 3 , the storage unit  120  according to the embodiment includes the user attribute information storage unit  121 , the terminal information storage unit  122 , an app information storage unit  123 , the individual score information storage unit  124 , and the advertising information storage unit  125 . 
     User Attribute Information Storage Unit  121   
     The user attribute information storage unit  121  according to the embodiment stores various type of information regarding the user attribute. For example, the user attribute information storage unit  121  stores the user attribute information.  FIG. 4  is a diagram illustrating an example of a user attribute information storage unit according to an embodiment. The user attribute information storage unit  121  illustrated in  FIG. 4  includes items such as a “user ID”, a “sex”, an “age”, and an “area”. 
     The “user ID” indicates identification information for identifying the user. For example, the user identified by a user ID “U 1 ” corresponds to the user U 1  illustrated in the example of  FIG. 1 . The “sex” indicates a sex of the user who uses the terminal device  10 . The “age” indicates an age of the user who uses the terminal device  10 . Note that the “age” may be a specific age of the user identified by the user ID, such as 35 years old, for example. The “area” indicates a residential area of the user who uses the terminal device  10 . Note that, in the “area”, an area name that indicates a fixed range corresponding to the residential area of the user (Kanto region, for example), a country name, or the like may be stored, rather than a specific address. 
     For example, in the example illustrated in  FIG. 4 , the sex identified by the user ID “U 1 ” is “male”, and the age of the user is “30&#39;s”. Further, for example, the user identified by the user ID “U 1 ” indicates that the residential area is “A prefecture”. 
     Note that the user attribute information storage unit  121  may store various types of information according to intended use beyond the above-described information. For example, the user attribute information storage unit  121  may store information regarding demographic attributes of the user, information regarding psychographic attributes. For example, the user attribute information storage unit  121  may store information such as a name, a family makeup, an income, an interest, and a life style. 
     Terminal Information Storage Unit  122   
     The terminal information storage unit  122  according to the embodiment stores various types of information regarding the terminal device used by the user. For example, the terminal information storage unit  122  stores the terminal device information.  FIG. 5  is a diagram illustrating an example of a terminal information storage unit according to an embodiment. For example, the terminal information storage unit  122  stores behavior information of the user to content distributed to the terminal devices  10  of the users. The terminal information storage unit  122  illustrated in  FIG. 5  includes items such as a “user ID”, a “terminal ID”, a “model number”, a “brand name”, the “number of days elapsed since the terminal device was on sale”, a “communication carrier”, a “maker&#39;s name”, and a “resolution”. 
     The “user ID” indicates identification information for identifying the user. The “terminal ID” is identification information for identifying the terminal device  10 . For example, the terminal device  10  identified by a terminal ID “F 11 ” corresponds to the terminal device  10 - 1  used by the user U 1  illustrated in the example of  FIG. 1 . The “model number” indicates a model number of the terminal device  10 . The “brand name” indicates a brand name provided to the terminal device  10 . The “number of days elapsed since the terminal device was on sale” indicates the number of days elapsed since the terminal device  10  was on sale. The “communication carrier” indicates a name of a company of a communication carrier that provides communication line to the terminal device  10 . The “resolution” indicates a resolution of a screen of the terminal device  10 . 
     For example, in the example illustrated in  FIG. 5 , the terminal device  10  identified by the terminal ID “F 11 ” indicates that the model number is “XX-YY01” and the brand name is “AAA”. Further, the terminal device  10  identified by the terminal ID “F 11 ” indicates that “336 days” have passed after sale, the communication carrier is “BBB company”, the manufacture that manufactures the terminal device  10  is “CCC company”, and the resolution is “1280×720”. Note that the terminal information storage unit  122  may store various types of information according to intended use beyond the above-described information. 
     App Information Storage Unit  123   
     The app information storage unit  123  according to the embodiment stores various types of information regarding the app. For example, the app information storage unit  123  stores information regarding the app installed in the terminal device  10 .  FIG. 6  is a diagram illustrating an example of an app information storage unit according to an embodiment. The app information storage unit  123  illustrated in  FIG. 6  includes items such as a “user ID”, a “terminal ID”, the “number of installed apps”, the “number of non-game apps, the “number of game apps”, the “number of new apps”, the “number of old apps”, a “name of installed app”. 
     The “user ID” indicates identification information for identifying the user. The “terminal ID” is identification information for identifying the terminal device  10 . The “number of installed apps” indicates the total number of apps installed in the terminal device  10 . The “number of non-game apps” indicates the number of apps other than game apps, of the installed apps. The “number of game apps” indicates the number of game apps, of the installed apps. The “number of new apps” indicates the number of apps that are relatively recently provided (for example, within one year), of the installed apps. The “number of old apps” indicates the number of apps other than the new apps, of the installed apps. The “name of installed app” indicates the app installed in the terminal device  10 . 
     For example, in the example illustrated in  FIG. 6 , the terminal device  10  identified by the terminal ID “F 11 ” indicates that “35” apps have been installed. Further, the terminal device  10  identified by the terminal ID “F 11 ” indicates that the number of non-game apps is “22” and the number of game apps is “13”, of the installed apps. Further, the terminal device  10  identified by the terminal ID “F 11 ” indicates that the number of new apps is “15” and the number of old apps is “20”, of the installed apps. Further, the terminal device  10  identified by the terminal ID “F 11 ” indicates that the installed apps are an “app V”, an “app W”, an “app X”, an “app Y”, an “app Z”, and the like. Note that the “name of installed app” may store identification information for identifying the app. Note that the app information storage unit  123  may store various types of information according to intended use beyond the above-described information. 
     Individual Score Information Storage Unit  124   
     The individual score information storage unit  124  according to the embodiment stores the individual score information regarding the apps. For example, the individual score information storage unit  124  stores the individual score information regarding the apps generated from the user information.  FIG. 7  is a diagram illustrating an example of an individual score information storage unit according to an embodiment. The individual score information storage unit  124  illustrated in  FIG. 7  includes the individual score information  124 - 1  regarding the app A, the individual score information  124 - 2  regarding the app B, the individual score information  124 - 3  regarding the app C, and the like. The individual score information  124 - 1  to  124 - 3  includes items such as a “category”, a “target”, and an “individual score”. 
     The “category” indicates a category of the user information. The “target” indicates a specific target corresponding to the category, that is, a target of which the individual score is generated. The “individual score” indicates the individual score of the targets (user information). 
     For example, in  FIG. 7 , as illustrated in individual score information  124 - 1 , the individual score information about the app A is generated, which indicates that the individual score in a case where the category “area” is “A prefecture” is “0.3”, the individual score in a case of “B prefecture” is “0.1”, and the individual score in a case of “C prefecture” is “0.2”. Note that the individual score information storage unit  124  may store various types of individual score information according to intended use beyond the above-described information. 
     Advertising Information Storage Unit  125   
     The advertising information storage unit  125  according to the embodiment stores various types of information regarding the advertisement. For example, the advertising information storage unit  125  stores various types of information regarding the advertisement mainly submitted by an advertiser. Note that the “advertiser” referred to here is a concept including not only the advertiser but also an advertising agency.  FIG. 8  is a diagram illustrating an example of an advertising information storage unit according to an embodiment. The advertising information storage unit  125  illustrated in  FIG. 8  includes items such as an “advertiser ID”, an “advertisement ID”, and an “app name”. 
     The “advertiser ID” indicates identification information for identifying the advertiser. The “advertisement ID” indicates identification information for identifying the advertisement submitted by the advertiser. The “app name” indicates the app corresponding to the advertisement. For example, the advertisement identified by an advertisement ID “C 10 ” may be described as “advertisement C 10 ”. Note that the app may be associated with a plurality of advertisements, instead of being associated with the advertisement on a one-on-one basis. 
     Further, the advertising information storage unit  125  may store information regarding various advertisements according to intended use beyond the above-described information. For example, the advertising information storage unit  125  may store a condition of a distribution destination specified for each advertisement, the number of distribution (a specified impression number) specified for each advertisement, and the like. Further, the advertising information storage unit  125  may store an index value that indicates the advertisement effect. For example, the advertising information storage unit  125  may store the index values such as a cost per install (CPI) and a click through rate (CTR) for each advertisement. 
     Further, the advertising information storage unit  125  may store information regarding rates of the advertisements, for example, tender prices (hereinafter, also referred to as “bid prices”). Further, the advertising information storage unit  125  may store information regarding stock such as quantities in stock of the advertisements. Further, the advertising information storage unit  125  may store information regarding categories of the advertisements. 
     Control Unit  130   
     Referring back to the description of  FIG. 3 , the control unit  130  is a controller, and is realized by, for example, the various programs (corresponding to an example of distribution programs) stored in the storage device inside the determination device  100  being executed by a central processing unit (CPU) or a micro processing unit (MPU), using the RAM as a work area. Further, the control unit  130  is a controller, and is realized by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). 
     As illustrated in  FIG. 3 , the control unit  130  includes an acquisition unit  131 , a generation unit  132 , a calculation unit  133 , a determination unit  134 , and a distribution unit  135 , and realizes or executes functions and actions of information processing described below. Note that the internal configuration of the control unit  130  is not limited to the configuration illustrated in  FIG. 3 , and another configuration may be employed as long as the configuration performs the information processing described below. Further, connection relationship among the processing units included in the control unit  130  is not limited to that illustrated in  FIG. 3 , and another connection relationship may be employed. 
     Acquisition Unit  131   
     The acquisition unit  131  acquires various types of information. For example, the acquisition unit  131  acquires the user information that is the information regarding the user who uses the terminal device  10  that becomes a providing destination of content. For example, the acquisition unit  131  acquires the user information regarding the user who uses the terminal device  10  that becomes the providing destination of an application. For example, the acquisition unit  131  acquires the user attribute information. For example, the acquisition unit  131  acquires the terminal information regarding the terminal device  10  used by the user. For example, the acquisition unit  131  acquires the app information. Further, the acquisition unit  131  acquires the advertisement request from the terminal device  10 . 
     Generation Unit  132   
     The generation unit  132  generates the individual score information on the basis of the user information. For example, the generation unit  132  generates the individual score information for each app. In the example of  FIG. 1 , the generation unit  132  generates the individual score information for each of the app A, the app B, the app C, and the like. To be specific, the generation unit  132  generates the individual score information  124 - 1  regarding the app A. Further, the generation unit  132  generates the individual score information  124 - 2  regarding the app B. Further, the generation unit  132  generates the individual score information  124 - 3  regarding the app C. 
     Further, the generation unit  132  generates the individual score information for each user information. For example, the generation unit  132  generates the individual score information indicating that the individual score in a case where the category “terminal brand name” is “AAA” is “0.5”, the individual score in a case of “BBB” is “0.1”, and the individual score in a case of “CCC” is “0.8” about the app A. 
     Further, the generation unit  132  may generate the individual score information by learning the weights of the features, that is, the individual scores, using the user information as features. For example, the generation unit  132  generates the individual score information regarding the apps by learning the weights (individual scores) of the features (user information), using the user information of the users who have installed the apps, of the user information collected in step S 11 , as positive examples of the apps. For example, the generation unit  132  generates the individual score information regarding the app A by learning the weights (individual scores) of the features (user information), using the user information of the users who have installed the app A, of the user information collected in step S 11 , as positive examples. 
     Further, for example, the generation unit  132  may generate the individual score information regarding the apps by learning the weights (individual scores) of the features (user information), using the user information of the users who have not installed the apps, of the user information collected in the step S 11 , as negative examples of the apps. For example, the generation unit  132  may generate the individual score information regarding the app A by learning the weights (individual scores) of the features (user information), using the user information of the users who have not installed the app A, of the user information collected in step S 11 , as negative examples. Note that the generation unit  132  may generate the individual score information by any method as long as the generation unit  132  generates the information regarding the individual scores on the basis of the user information. 
     Further, for example, the generation unit  132  may generate information regarding the ranking (rank) of the apps on the basis of the scores of the apps calculated by the calculation unit  133 . In the example of  FIG. 2 , the generation unit  132  generates the ranking information LL 11  of the apps on the basis of the scores of the apps calculated by the calculation unit  133 . 
     Calculation Unit  133   
     The calculation unit  133  calculates a score regarding a probability of the user performing a predetermined behavior for each of a plurality of pieces of content on the basis of the user information acquired by the acquisition unit  131 . The calculation unit  133  calculates the score of each of the plurality of pieces of content on the basis of the individual score generated for each user information. The calculation unit  133  calculates the score of each of the plurality of pieces of content on the basis of the individual score generated for each user information including the user attribute information. The calculation unit  133  calculates the score of each of the plurality of pieces of content on the basis of the individual score generated for each user information including the terminal information. 
     Further, the calculation unit  133  calculates the score regarding a probability of a user performing installation for each of a plurality of applications that is the plurality of pieces of content. For example, the calculation unit  133  calculates the scores of the apps. The calculation unit  133  calculates the scores of the apps on the basis of the individual score information and the user information. For example, the calculation unit  133  may calculate the scores of the apps, using the above formula (1). For example, the calculation unit  133  calculates the score of the app A, the score of the app B, the score of the app C, and the like. In the example of  FIG. 2 , the calculation unit  133  calculates the score of the app A about the user U 1  on the basis of the individual score information  124 - 1  regarding the app A and the user information of the user U 1 . In the example illustrated in  FIG. 2 , since the terminal brand name of the user U 1  is “AAA, the calculation unit  133  adds the individual score “0.5” of “AAA” in the category “terminal brand name” in the individual score information  124 - 1  to the score of the app A. Further, in the example illustrated in  FIG. 2 , since the residential area of the user U 1  is “A prefecture”, the calculation unit  133  adds the individual score “0.3” of “A prefecture” in the category “area” in the individual score information  124 - 1  to the score of the app A. The calculation unit  133  calculates the score of the app A by adding up the individual scores corresponding to the user information of the user U 1  in the individual score information  124 - 1 . In the example of  FIG. 2 , the calculation unit  133  calculates the score of the app A as “3.1 (=0.5+0.3+ . . . )”. The calculation unit  133  calculates the scores of the apps (apps A, B, C, and the like) by performing the above processing for the apps (apps A, B, C, and the like). 
     Determination Unit  134   
     The determination unit  134  determines the distribution content to be distributed to the terminal device  10  on the basis of the scores of the pieces of content calculated by the calculation unit  133 . The determination unit  134  determines the distribution content to be distributed to the terminal device  10 , of the plurality of applications, on the basis of the scores of the applications calculated by the calculation unit  133 . The determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking of the applications according to the scores of the applications. 
     In the example of  FIG. 2 , the determination unit  134  determines advertisement to be distributed to the terminal device  10 - 1  of the user U 1  on the basis of the ranking information LL 11  and the advertising information stored in the advertising information storage unit  125 . For example, the determination unit  134  determines the advertisement C 11  of the app B in the highest rank in the ranking information LL 11  as the advertisement to be distributed to the terminal device  10 - 1  of the user U 1 . 
     Further, for example, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the information regarding prices of the applications in advertisement distribution. For example, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the bid prices of the advertisements of the apps. Further, for example, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the information regarding the stock of the advertisements of the applications. Further, for example, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to times to distribute the advertisements. Further, for example, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to information regarding a user operation to the advertisements of the applications. For example, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to a click through rate of the advertisements of the apps. Further, for example, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the content including an advertisement display area where the advertisement is displayed. Note that details of these points will be described below. 
     Further, the determination unit  134  may determine the distribution content to be distributed to the terminal device  10  on the basis of the degree of rarity of the user to one piece of content, the degree of rarity being calculated on the basis of the score of the one piece of content in the user who uses the terminal device  10 , and the score of the one piece of content in another user. For example, the determination unit  134  may determine the distribution content to be distributed to the terminal device  10  on the basis of the degree of rarity of the user to one piece of content, the degree of rarity being calculated on the basis of a difference between the score of the one piece of content in the user who uses the terminal device  10 , and an average of the scores of the one piece of content in the plurality of users. Note that details of this point will be described below. 
     Distribution Unit  135   
     The distribution unit  135  distributes various types of information to the terminal device  10 . For example, the distribution unit  135  distributes the advertisement to the terminal device  10 . Further, the distribution unit  135  distributes the advertisement determined by the determination unit  134 . In  FIG. 2 , the distribution unit  135  distributes the advertisement C 11  of the app B to the terminal device  10 - 1 . Note that the distribution unit  135  may distribute the content CT 11  to the terminal device  10 - 1 . 
     3. Flow of Determination Processing 
     Next, a procedure of the determination processing by the determination system  1  according to the embodiment will be described using  FIG. 9 .  FIG. 9  is a flowchart illustrating an example of determination processing according to an embodiment. To be specific,  FIG. 9  is a flowchart illustrating an example of the determination processing regarding generation of the individual score information. 
     As illustrated in  FIG. 9 , the acquisition unit  131  of the determination device  100  acquires the user information of the users (step S 101 ). For example, the acquisition unit  131  acquires the user information of the users from the terminal devices  10  used by the users. 
     Further, the generation unit  132  of the determination device  100  generates the individual score information on the basis of the acquired user information (step S 102 ). For example, the generation unit  132  generates the individual score information for each app. 
     Next, a procedure of the determination processing by the determination system  1  according to the embodiment will be described using  FIG. 10 .  FIG. 10  is a flowchart illustrating an example of determination processing according to an embodiment. To be specific,  FIG. 10  is a flowchart illustrating an example of the determination processing, using the individual scores. 
     As illustrated in  FIG. 10 , the acquisition unit  131  of the determination device  100  acquires the advertisement request from the terminal device  10  (step S 201 ). After that, the calculation unit  133  of the determination device  100  calculates the scores of the apps on the basis of the user information corresponding to the terminal device  10  and the individual scores (step S 202 ). For example, the calculation unit  133  calculates the score of the app A about the user U 1  on the basis of the individual score information  124 - 1  regarding the app A and the user information of the user U 1 . 
     After that, the determination unit  134  of the determination device  100  determines the advertisement on the basis of the scores of the apps (step S 203 ). For example, the determination unit  134  determines that the advertisement C 11  of the app B in the highest rank in the ranking information LL 11  as the advertisement to be distributed to the terminal device  10 - 1  of the user U 1 . 
     After that, the distribution unit  135  of the determination device  100  distributes the determined advertisement to the terminal device  10  (step S 204 ). For example, the distribution unit  135  distributes the advertisement C 11  of the app B to the terminal device  10 - 1 . 
     4. Change of Ranking 
     In the above-described example, an example of generating the ranking, using the scores calculated on the basis of the individual scores has been described. However, the ranking may be varied according to another information. This point will be described using  FIGS. 11 to 15 .  FIGS. 11 to 15  are diagrams illustrating an example of ranking change according to an embodiment. 
     4-1. Change of Ranking Based on Bid Price 
     First, a case of varying the ranking according to the information regarding the prices of the apps in the advertisement distribution will be described using  FIG. 11 . The example of  FIG. 11  illustrates a case in which the determination device  100  changes the ranking, using the bid prices of the advertisements of the apps as the information regarding the prices of the apps in the advertisement distribution. 
     Ranking information LL 21 - 1  illustrated in  FIG. 11  indicates ranking information before the ranking is changed. The ranking information LL 21 - 1  indicates a case in which an app E having the largest score is ranked “first”, an app F having the next largest score to the app E is ranked “second”, an app G having the next largest score to the app F is ranked “third”, and an app H having the next largest score to the app G is ranked “fourth”. Further, the ranking information LL 21 - 1  indicates a case in which the advertisement of the app E is “advertisement C 12 ”, the advertisement of the app F is “advertisement C 13 ”, the advertisement of the app G is “advertisement C 14 ”, and the advertisement of the app H is “advertisement C 15 ”. 
     Further, the ranking information LL 21 - 1  indicates a case in which the bid price of the advertisement of the app E is “50”, the bid price of the advertisement of the app F is “100”, the bid price of the advertisement of the app G is “60”, and the bid price of the advertisement of the app H is “40”. Note that the information regarding the bid prices of the advertisements may be stored in the advertising information storage unit  125 . 
     Here, the determination device  100  changes the ranking on the basis of the information regarding the bid prices of the advertisements (step S 21 ). For example, the determination device  100  generates ranking information LL 21 - 2  by changing the ranking indicated in the ranking information LL 21 - 1  on the basis of the information regarding the bid prices of the advertisements. 
     In the example of  FIG. 11 , the bid price of the advertisement of the app F ranked second is larger than the bid price of the advertisement of the app E ranked first. Therefore, the determination device  100  changes the score of the advertisement of the app F ranked second. To be specific, the determination device  100  increases the score of the advertisement of the app F ranked second by “2”. Accordingly, the score of the advertisement of the app F ranked second becomes “6.2”, and becomes larger than “5.4” of the advertisement of the app E ranked first. Therefore, the determination device  100  switches the rank of the advertisement of the app E and the rank of the advertisement of the app F, as illustrated in the ranking information LL 21 - 2 . To be specific, the determination device  100  changes the ranking by causing the advertisement of the app E to be ranked “second”, and the advertisement of the app F to be ranked “first”. In this case, the determination device  100  distributes the advertisement of the app F ranked “first” in distributing one advertisement on the basis of the ranking. 
     Note that, in  FIG. 11 , an example of changing the score of the advertisement of the app F ranked “second” has been illustrated for simplification of description. However, the score of the advertisement of the app G having a higher bid price than the app E ranked first may be changed. For example, the determination device  100  may increase the score of the advertisement of the app G ranked “third” by “1”. In this case, the determination device  100  increases the score of the advertisement of the app H by “1” that is smaller than the increase “2” of the score of the advertisement of the app F because the app G is ranked lower than the app F, and the bid price is “60”, which is small. In this way, the determination device  100  may vary the score according to the bid prices and the ranks. 
     Further,  FIG. 11  illustrates a case in which the score of the advertisement of the app F becomes “6.2” due to the increase in the score by “2”, and the rank is changed to “first”. However, the determination device  100  may change the ranking by any method. For example, the determination device  100  may switch the first advertisement and the second advertisement in a case where the bid price of the second advertisement is larger than the bid price of the first advertisement, and a difference in the score of the first advertisement and the score of the second advertisement is within a predetermined threshold. For example, the determination device  100  may switch the first advertisement and the second advertisement in a case where the bid price of the second advertisement is larger than the bid price of the first advertisement by a predetermined threshold or more. Further, in the above-described example, a case of switching the first advertisement and the second advertisement has been described. However, the determination device  100  may switch the first advertisement and the third advertisement. 
     Further, for example, in a case of selecting a plurality of the advertisements, the determination device  100  may switch the second advertisement and the third advertisement. For example, in a case of distributing two advertisements, the determination device  100  distributes the first advertisement and the second advertisement, which is switched from third. 
     4-2. Change of Ranking Based on Quantities in Stock 
     Next, a case of varying the ranking according to the information regarding the stock of the advertisements of the apps will be described using  FIG. 12 .  FIG. 12  illustrates a case in which the determination device  100  changes the ranking, using quantities in stock of the advertisements of the apps as the information regarding the stock of the advertisements of the apps. Note that description of similar points to the example illustrated in  FIG. 11  is appropriately omitted. 
     Ranking information LL 22 - 1  illustrated in  FIG. 12  indicates that the quantity in stock of the advertisement of the app E is “small”, the quantity in stock of the advertisement of the app F is “small”, the quantity in stock of the advertisement of the app G is “large”, and the quantity in stock of the advertisement of the app H is “middle”. Note that the information regarding the quantities in stock of the advertisements may be stored in the advertising information storage unit  125 . Further, in the example illustrated in  FIG. 12 , the quantities in stock of the advertisements are conceptually illustrated as “large”, “middle”, and “small”. However, the quantities in stock of the advertisements may be numerical values that indicate specific quantities in stock. Further, in the example illustrated in  FIG. 12 , the quantities in stock having the quantity in stock “large” are largest, the quantities in stock having the quantity in stock “middle” are second largest next to the quantity in stock “large”, and the quantities in stock having the quantity in stock “small” are small. 
     Here, the determination device  100  changes the ranking on the basis of the information regarding the quantities in stock of the advertisements (step S 22 ). For example, the determination device  100  generates ranking information LL 22 - 2  by changing the ranking illustrated in the ranking information LL 22 - 1  on the basis of the information regarding the quantities in stock of the advertisements. 
     In the example of  FIG. 12 , the quantity in stock of the advertisement of the app G ranked third is larger than the quantity in stock of the advertisement of the app E ranked first and the quantity in stock of the advertisement of the app F ranked second. Therefore, the determination device  100  changes the score of the advertisement of the app G ranked third. To be specific, the determination device  100  increases the score of the advertisement of the app G ranked third by “2”. Accordingly, the score of the advertisement of the app G ranked third becomes “5.6”, and becomes larger than the score “5.4” of the advertisement of the app E ranked first and the score “4.2” of the advertisement of the app F ranked second. Therefore, the determination device  100  switches the rank of the advertisement of the app E, the rank of the advertisement of the app F, and the rank of the advertisement of the app G, as illustrated in the ranking information LL 22 - 2 . To be specific, the determination device  100  changes the ranking by causing the advertisement of the app G to be ranked “first”, the advertisement of the app E to be ranked “second”, and the advertisement of the app F to be ranked “third”. In this case, the determination device  100  distributes the advertisement of the app G ranked “first” in distributing one advertisement on the basis of the ranking. 
     4-3. Change of Ranking Based on Distribution Time 
     Next, a case of varying the ranking according to information regarding times to distribute the advertisements will be described using  FIG. 13 . In the example of  FIG. 13 , a case in which the determination device  100  changes the ranking, using information regarding distribution times of the advertisements will be described. Note that description of similar points to the example illustrated in  FIGS. 11 and 12  is appropriately omitted. 
     Ranking information LL 23 - 1  illustrated in  FIG. 13  indicates a case in which the category of the advertisement of the app E is “news”, the category of the advertisement of the app F is “game”, the category of the advertisement of the app G is “finance”, and the category of the advertisement of the app H is “shopping”. Note that information regarding the categories of the advertisements may be stored in the advertising information storage unit  125 . 
     Here, the determination device  100  changes the ranking on the basis of the information regarding the distribution times of the advertisements (step S 23 ). For example, the determination device  100  generates ranking information LL 23 - 2  by changing the ranking indicated in the ranking information LL 23 - 1  on the basis of the information regarding the distribution times of the advertisements. 
     In the example of  FIG. 13 , assume that the distribution time of the advertisement is 23:00. Further, the determination device  100  acquires information indicating that the probability of the app of the category “game” being installed is higher than other apps in a late-night time zone. In this case, the determination device  100  changes the score of the advertisement of the app F of the category “game”. To be specific, the determination device  100  increases the score of the advertisement of the app F ranked second by “2”. Accordingly, the score of the advertisement of the app F ranked second becomes “6.2”, and becomes larger than the score “5.4” of the advertisement of the app E ranked first. Therefore, the determination device  100  switches the rank of the advertisement of the app E and the rank of the advertisement of the app F, as illustrated in the ranking information LL 23 - 2 . To be specific, the determination device  100  changes the ranking by causing the advertisement of the app E to be ranked “second” and the advertisement of the app F to be ranked “first”. In this case, the determination device  100  distributes the advertisement of the app F ranked “first” in distributing one advertisement on the basis of the ranking. 
     4-4. Change of Ranking Based on CTR 
     Next, a case of varying the ranking according to the information regarding a user operation to the advertisements of the apps will be described using  FIG. 14 . In the example of  FIG. 14 , a case in which the determination device  100  changes the ranking, using a click through rate (CTR) of the advertisements of the apps, as the information regarding the user operation to the advertisements of the apps will be described. Note that description of similar points to the examples illustrated in  FIGS. 11 to 13  is appropriately omitted. 
     Ranking information LL 24 - 1  in  FIG. 14  illustrates a case in which the CTR of the advertisement of the app E is “5”, the CTR of the advertisement of the app F is “3”, the CTR of the advertisement of the app G is “10”, and the CTR of the advertisement of the app H is “6”. The CTRs of the advertisements illustrated in  FIG. 14  are expressed in percentage. Note that the information regarding the user operation to the advertisements of the apps may be stored in the advertising information storage unit  125 . 
     Here, the determination device  100  changes the ranking on the basis of the information regarding user operation to the advertisements of the apps (step S 24 ). For example, the determination device  100  generates ranking information LL 24 - 2  by changing the ranking indicated in the ranking information LL 24 - 1  on the basis of the CTRs of the advertisement of the apps. 
     In the example of  FIG. 14 , the CTR of the advertisement of the app G ranked third is larger than the CTR of the apps E ranked first and the CTR of the advertisement of the app F ranked second. Therefore, the determination device  100  changes the score of the advertisement of the app G ranked third. To be specific, the determination device  100  increases the score of the advertisement of the app G ranked third by “3”. Accordingly, the score of the advertisement of the app G ranked third becomes “6.6”, and becomes larger than the score “5.4” of the advertisement of the app E ranked first and the score “4.2” of the advertisement of the app F ranked second. Therefore, the determination device  100  switches the rank of the advertisement of the app E, the rank of the advertisement of the app F, and the rank of the advertisement of the app G, as illustrated in the ranking information LL 24 - 2 . To be specific, the determination device  100  changes the ranking by causing the advertisement of the app G to be ranked “first”, the advertisement of the app E to be ranked “second”, and the advertisement of the app F to be ranked “third”. In this case, the determination device  100  distributes the advertisement of the app G ranked “first” in distributing one advertisement on the basis of the ranking. 
     4-5. Change of Ranking Based on Content where Advertisement is Displayed 
     Next, a case of varying the ranking according to content including an advertisement space that is the advertisement display area where the advertisement is displayed will be described using  FIG. 15 . In the example of  FIG. 15 , a case in which the determination device  100  changes the ranking according to information included in the content including the advertisement space where the advertisement is displayed and the categories of the advertisements will be described. That is, the example of  FIG. 15  illustrates a case in which the determination device  100  changes the ranking according to relationship between the content (display surface) of a web page or the like where the advertisement is displayed and the categories of the advertisements. Note that description of similar points to the example illustrated in  FIGS. 11 to 14  is appropriately omitted. 
     Ranking information LL 25 - 1  illustrated in  FIG. 15  indicates a case in which the category of the advertisement of the app E is “news”, the category of the advertisement of the app F is “game”, the category of the advertisement of the app G is “finance”, and the category of the advertisement of the app H is “shopping”. 
     Here, the determination device  100  changes the ranking according to content CT 25  including an advertisement space AR 25  where the advertisement is displayed (step S 25 ). For example, the determination device  100  generates ranking information LL 25 - 2  by changing the ranking indicated in the ranking information LL 25 - 1  on the basis of an affinity between the content of the content CT 25  and the categories of the advertisements. 
     In the example of  FIG. 15 , the content CT 25  including the advertisement space AR 25  is content regarding stock news. Therefore, the determination device  100  changes the advertisement of the app G of the category “finance”. To be specific, the determination device  100  increases the score of the advertisement of the app G ranked third by “3”. Accordingly, the score of the advertisement of the app G ranked third becomes “6.6”, and becomes larger than the score “5.4” of the advertisement of the app E ranked first and the score “4.2” of the advertisement of the app F ranked second. Therefore, the determination device  100  switches the rank of the advertisement of the app E, the rank of the advertisement of the app F, and the rank of the advertisement of the app G as illustrated in the ranking information LL 25 - 2 . To be specific, the determination device  100  changes the ranking by causing the advertisement of the app G to be ranked “first”, the advertisement of the app E to be ranked “second”, and the advertisement of the app F to be ranked “third”. In this case, the determination device  100  distributes the advertisement of the app G ranked “first” in distributing one advertisement on the basis of the ranking. 
     Note that the ranking change illustrated in  FIGS. 11 to 15  is an example, and the ranking may be changed on the basis of any information. For example, the determination device  100  may change the ranking on the basis of budgets of the advertisements or distribution periods. For example, the determination device  100  may change the ranking such that the ranking of the advertisement having a larger budget is ranked higher. Further, for example, the determination device  100  may change the ranking such that the advertisement with the distribution period coming to an end is ranked higher. 
     5. Flow of Determination Processing by Ranking Change 
     Next, a procedure of determination processing including ranking change based on various types of information will be described using  FIG. 16 .  FIG. 16  is a flowchart illustrating an example of determination processing by ranking change according to an embodiment. 
     As illustrated in  FIG. 16 , the acquisition unit  131  of the determination device  100  acquires the advertisement request from the terminal device  10  (step S 301 ). After that, the calculation unit  133  of the determination device  100  calculates the scores of the apps on the basis of the user information corresponding to the terminal device  10  and the individual scores (step S 302 ). For example, the calculation unit  133  calculates the score of the app A about the user U 1  on the basis of the individual score information  124 - 1  regarding the app A and the user information of the user U 1 . 
     After that, the determination unit  134  of the determination device  100  changes the ranking of the advertisements based on the scores of the apps (step S 303 ). For example, the determination unit  134  changes the ranking of the advertisements based on the scores of the apps on the basis of various types of information. For example, the determination unit  134  changes the ranking of the advertisements based on the scores of the apps on the basis of various types of information, as illustrated in  FIGS. 11 to 15 . After that, the determination unit  134  determines the advertisement on the basis of the ranking (step S 304 ). After that, the distribution unit  135  of the determination device  100  distributes the determined advertisement to the terminal device  10  (step S 305 ). 
     6. Determination of Advertisement Based on Rarity of User 
     In the above example, an example of determining the advertisement to be distributed according to the scores of the apps for each user has been described. However, the determination device  100  may determine the advertisement to be distributed on the basis of the rarity in the apps, of the user who uses the terminal device  10 . This point will be described using  FIG. 17 .  FIG. 17  is a diagram illustrating an example of determination of an advertisement on the basis of rarity of a user in apps according to an embodiment. 
     Ranking information LL 31  illustrated in  FIG. 17  indicates ranking information of the user U 1 . The ranking information LL 31  indicates a case in which the app J having the largest score “5.4” is ranked “first”, the app K having the next largest score “4.2” to the app J is ranked “second”, the app L having the next largest score “3.6” to the app K is ranked “third”, and the app M having the next largest score “3.1” to the app L is ranked “fourth”. 
     Further, tendency information LL 32  illustrated in  FIG. 17  indicates information based on the scores calculated for a plurality of users. To be specific, the tendency information LL 32  indicates averages of the scores of the apps (hereinafter, also referred to as “average scores”) calculated for the plurality of users. Note that the tendency information LL 32  may be information targeting any users as long as the information indicates a tendency of many users. For example, the tendency information LL 32  may include information of all the users who are targets to which the advertisement is distributed, or may include information of a user similar to the user U 1 . Further, the tendency information LL 32  may include the information of the user U 1 . 
     In the example of  FIG. 17 , the entire information LL 32  indicates a case in which the average score of the app J is “6.2”, the average score of the app K is “4.1”, the average score of the app L is “0.7”, and the average score of the app M is “3.8”. 
     The determination device  100  calculates the degrees of rarity of the user U 1  in the apps (step S 31 ). For example, the determination device  100  calculates the degrees of rarity of the user U 1  in the apps on the basis of the ranking information LL 31  and the tendency information LL 32 . In the example of  FIG. 17 , the determination device  100  calculates the degrees of rarity according to differences between the scores of the apps of the user U 1  in the ranking information LL 31 , and the average scores of the corresponding apps in the tendency information LL 32 , as illustrated in a degree of rarity list RL 31 . 
     For example, the determination device  100  calculates the degree of rarity “−0.8” of the user U 1  in the app J by subtracting the average score “6.2” of the app J in the tendency information LL 32  from the score “5.4” of the app J in the ranking information LL 31 . In  FIG. 17 , the score “5.4” of the app J of the user U 1  is smaller than the average score “6.2” of the app J. That is, the degree of rarity “−0.8” of the user U 1  in the app J indicates that the degree of rarity of the user U 1  in the app J is low. That is, the degree of rarity “−0.8” indicates that the possibility of the user U 1  installing the app J is smaller than the tendency of all the users included in the targets of the tendency information LL 32 . Further, similarly, the degree of rarity “−0.7” of the user U 1  in the app M indicates that the degree of rarity of the user U 1  in the app M is low. 
     Further, for example, the determination device  100  calculates the degree of rarity “0.1” of the user U 1  in the app K by subtracting the average score “4.1” of the app K in the tendency information LL 32  from the score “4.2” of the app K in the ranking information LL 31 . In  FIG. 17 , the score “4.2” of the app K of the user U 1  approximates to the average score “4.1” of the app J. That is, the degree of rarity “0.1” of the user U 1  in the app K indicates that the degree of rarity of the user U 1  in the app K is average. That is, the degree of rarity “0.1” indicates that the possibility of the user U 1  installing the app J conforms to the tendency of all the users included in the targets of the tendency information LL 32 . 
     Further, for example, the determination device  100  calculates the degree of rarity “2.9” of the user U 1  in the app L by subtracting the average score “0.7” of the app L in the tendency information LL 32  from the score “3.6” of the app L in the ranking information LL 31 . In  FIG. 17 , the score “3.6” of the user U 1  in the app L is larger than the average score “0.7” of the app L. That is, the degree of rarity “2.9” of the user U 1  in the app L indicates that the degree of rarity of the user U 1  in the app L is high. That is, the degree of rarity “2.9” indicates that the possibility of the user U 1  installing the app L is higher than the tendency of all the users included in the targets of the tendency information LL 32 . 
     Therefore, in the example illustrated in  FIG. 17 , the determination device  100  determines the advertisement of the app L as the advertisement to be distributed to the terminal device  10  of the user U 1  (step S 32 ). That is, the determination device  100  determines the advertisement of the app L ranked “third” and having a high degree of rarity, as the advertisement to be distributed to the terminal device  10  of the user U 1 , instead of the advertisement of the app J and the advertisement of the app K, because the degrees of rarity in the app J ranked “first” and the app K ranked “second” are not high. Accordingly, the determination device  100  can determine the advertisement to be distributed on the basis of not only simple comparison between the scores of the content (apps) in the user, but also relative comparison with other users. 
     For example, the app L having a low average score in the tendency information LL 32  is an app having a low probability of being installed in the tendency of all the users included in the targets of the tendency information LL 32 . Therefore, the determination device  100  has a low probability to determine the advertisement of the app L as the advertisement to be distributed. Meanwhile, the score of the app L in the ranking information LL 31  regarding the user U 1  is “3.6”, and is ranked third, which is high. Therefore, the user U 1  deviates from the tendency of all the users included in the targets of the tendency information LL 32 , and is a user having a relatively high probability to install the app L in all the users. Therefore, the determination device  100  can increase the probability of distributing the advertisement of the app L by distributing the advertisement of the app L to the terminal device  10  of the user U 1 , instead of the app J or the app K having a high score, in all the users included in the targets of the tendency information LL 32 . Accordingly, the determination device  100  can optimize the entire advertisement distribution. 
     Note that the calculation of the degree of rarity and the determination of the advertisement based on the degree of rarity are examples, and the degree of rarity may be calculated and the advertisement to be distributed may be determined by various methods. For example, the determination device  100  may determine the advertisement to be distributed on the basis of the degrees of rarity according to the degrees of rarity and the advertisement stocks of the apps. For example, the determination device  100  may determine, in a case where large quantities in stock of the advertisement of the app having a high degree of rarity remain, the advertisement of the app calculated to have a high degree of rarity as the advertisement to be distributed on the basis of the degree of rarity. In this case, the determination device  100  can distribute the appropriate advertisement on the basis of various conditions not only by simply comparing the scores of the content in the user, but also by determining the advertisement to be distributed to the user by balancing the degree of rarity and the advertisement stock. 
     7. Effect 
     As described above, the determination device  100  according to the embodiment includes the acquisition unit  131 , the calculation unit  133 , and the determination unit  134 . The acquisition unit  131  acquires the user information that is the information regarding the user who uses the terminal device  10  that becomes the providing destination of the content. Further, the calculation unit  133  calculates the scores regarding the probability of the user performing a predetermined behavior for the plurality of pieces of content on the basis of the user information acquired by the acquisition unit  131 . The determination unit  134  determines the distribution content to be distributed to the terminal device  10  on the basis of the scores of the pieces of content calculated by the calculation unit  133 . 
     Accordingly, the determination device  100  according to the embodiment calculates the scores regarding the probability of the user performing a predetermined behavior for the plurality of pieces of content, and determines the advertisement of the app to be distributed to the terminal device  10  on the basis of the calculated scores, thereby to appropriately determine the distribution content to be distributed to the terminal device  10 . That is, the determination device  100  can distribute the advertisement of the app having a high probability of being installed by the user, to the terminal device  10  of the user, thereby to improve the advertisement effect. 
     Further, in the determination device  100  according to the embodiment, the calculation unit  133  calculates the scores of the plurality of pieces of content on the basis of the individual score for each user information. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  according to the scores on the basis of the individual scores generated for each user information. 
     Further, in the determination device  100  according to the embodiment, the calculation unit  133  calculates the scores of the plurality of pieces of content on the basis of the individual scores generated for each user information including the user attribute information that is the information indicating the attributes of the user. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  according to the scores on the basis of the individual scores generated for each user information including the user attribute information. 
     Further, in the determination device  100  according to the embodiment, the calculation unit  133  calculates the scores of the plurality of pieces of content on the basis of the individual scores generated for each user information including the terminal information that is the information regarding the terminal device  10 . 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  according to the scores on the basis of the individual scores generated for each user information including the terminal information. 
     Further, in the determination device  100  according to the embodiment, the determination unit  134  determines the distribution content to be distributed to the terminal device  10  on the basis of the degree of rarity of the user to one piece of content calculated on the basis of the score of the one piece of content in the user who uses the terminal device  10 , and the scores of the one piece of content in other users. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  on the basis of the rarity of the user who uses the terminal device  10  in the pieces of content. 
     Further, in the determination device  100  according to the embodiment, the determination unit  134  determines the distribution content to be distributed to the terminal device  10  on the basis of the degree of rarity of the user to one piece of content calculated on the basis of the difference between the score of the one piece of content in the user who uses the terminal device  10 , and an average of the scores of the one piece of content in the plurality of users. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  on the basis of the rarity indicating how much the user who uses the terminal device  10  deviates from the average of all the users, about the pieces of content. 
     Further, in the determination device  100  according to the embodiment, the calculation unit  133  calculates the scores regarding the probability of the user performing installation for the plurality of applications that is the plurality of pieces of content. The determination unit  134  determines the distribution content to be distributed to the terminal device  10 , of the plurality of applications, on the basis of the scores of the applications calculated by the calculation unit  133 . 
     Accordingly, the determination device  100  according to the embodiment calculates the scores regarding the probability of the user performing installation for the plurality of apps, and determines the distribution content to be distributed to the terminal device  10  on the basis of the calculated scores, thereby to appropriately determine the content to be distributed to the terminal device  10 . That is, the determination device  100  can distribute the advertisement of the app having a high probability of being installed to the user, to the terminal device  10  of the user, thereby to improve the advertisement effect. 
     Further, in the determination device  100  according to the embodiment, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking of the applications according to the scores of the applications. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the advertisement of the app to be distributed to the terminal device  10  on the basis of the ranking of the applications according to the scores of the plurality of apps. 
     Further, in the determination device  100  according to the embodiment, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the information regarding the prices of the applications in the advertisement distribution. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  on the basis of the ranking varied according to the information regarding the prices of the applications in the advertisement distribution. That is, the determination device  100  can perform flexible advertisement distribution by enabling the distribution of the advertisement in consideration of the information regarding the stock such as the quantities in stock of the apps. 
     Further, in the determination device  100  according to the embodiment, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the information regarding the stock of the advertisements of the applications. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  on the basis of the ranking varied according to the information regarding the stock of the advertisements of the applications. That is, the determination device  100  can perform flexible advertisement distribution by enabling the distribution of the advertisement in consideration of the information regarding the prices such as the bid prices of the advertisements of the apps. 
     Further, in the determination device  100  according to the embodiment, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the time to distribute the advertisement. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  on the basis of the ranking varied according to the time to distribute the advertisement. That is, the determination device  100  determines the advertisement to be distributed on the basis of the ranking in consideration of the time to distribute the advertisement, thereby to further improve the advertisement effect. 
     Further, in the determination device  100  according to the embodiment, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the information regarding the user operation to the advertisements of the applications. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  on the basis of the ranking varied according to the information regarding the user operation to the advertisements of the applications. That is, the determination device  100  determines the advertisement to be distributed on the basis of the ranking in consideration of the information regarding the user operation to the advertisements of the apps, such as the CTR, thereby to further improve the advertisement effect. 
     Further, in the determination device  100  according to the embodiment, the determination unit  134  determines the advertisement of the application to be distributed to the terminal device  10  on the basis of the ranking varied according to the content including the advertisement display area where the advertisement is displayed. 
     Accordingly, the determination device  100  according to the embodiment can appropriately determine the content to be distributed to the terminal device  10  on the basis of the ranking varied according to the content including the advertisement display area (advertisement space) where the advertisement is displayed. That is, the determination device  100  determines the advertisement to be distributed on the basis of the ranking in consideration of the content including the advertisement space, thereby to further improve the advertisement effect. 
     8. Hardware Configuration 
     The above-described determination device  100  according to the embodiment is realized by a computer  1000  having a configuration as illustrated in  FIG. 18 , for example.  FIG. 18  is a hardware configuration diagram illustrating an example of the computer that realizes functions of the determination device. The computer  1000  includes a CPU  1100 , a RAM  1200 , a ROM  1300 , an HDD  1400 , a communication interface (I/F)  1500 , an input/output interface (I/F)  1600 , and a media interface (I/F)  1700 . 
     The CPU  1100  is operated on the basis of programs stored in the ROM  1300  or the HDD  1400 , and controls units. The ROM  1300  stores a boot program executed by the CPU  1100  at the time of start of the computer  1000 , a program depending on the hardware of the computer  1000 , and the like. 
     The HDD  1400  stores a program executed by the CPU  1100 , data used by the program, and the like. The communication interface  1500  receives data from another device through a network N and sends the data to the CPU  1100 , and transmits data determined by the CPU  1100  to another device through the network N. 
     The CPU  1100  controls output devices such as a display and a printer, and input devices such as keyboard and a mouse through the input/output interface  1600 . The CPU  1100  acquires data from the input device through the input/output interface  1600 . Further, the CPU  1100  outputs determined data to the output device through the input/output interface  1600 . 
     The media interface  1700  reads a program or data stored in a recording medium  1800 , and provides the program or data to the CPU  1100  through the RAM  1200 . The CPU  1100  loads the program from the recording medium  1800  onto the RAM  1200  through the media interface  1700 , and executes the loaded program. The recording medium  1800  is, for example, an optical recording medium such as a digital versatile disc (DVD) or a phase change rewritable disk (PD), a magneto-optical recording medium such as magneto-optical disk (MO), a tape medium, a magnetic recording medium, or a semiconductor memory. 
     For example, in a case where the computer  1000  functions as the determination device  100  according to the embodiment, the CPU  1100  of the computer  1000  controls the functions of the control unit  130  by executing the programs loaded on the RAM  1200 . The CPU  1100  of the computer  1000  reads the programs from the recording medium  1800  and executes the programs. However, as another example, the CPU  1100  may acquire the programs from another device through the network N. 
     As described above, some embodiments and modifications of the present application have been described in detail on the basis of the drawings. However, these embodiments and modifications are examples, and the present invention can be implemented in other forms to which various alternations and improvements are applied on the basis of the knowledge of a person skilled in the art, starting with the aspects described in the disclosed rows of the invention. 
     9. Others 
     All or a part of the processing described as those automatically performed, of the processing described in the embodiments and modifications, can be manually performed, or all or a part of the processing described as those manually performed, of the processing described in the embodiments and modifications, can be automatically performed by a known method. In addition, the processing processes, the specific names, and the information including the various data and parameters described and illustrated in the documents and the drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in the drawings are not limited thereto. 
     Further, the illustrated configuration elements of the devices are functionally and conceptually illustrated, and are not necessarily physically configured as illustrated in the drawings. That is, the specific forms of distribution/integration of the devices are not limited to the illustrated forms, and all or a part of the forms can be functionally or physically configured in a distributed/integrated manner in arbitrary units according to various loads and a status of use. 
     Further, the above-described embodiments and modifications can be arbitrarily combined within a range where the processing content is consistent. 
     Further, the above-described “unit (or section or module”) can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or acquisition circuit. 
     According to one aspect of an embodiment, an effect to appropriately determine content to be distributed to a terminal device is exerted. 
     Although the invention has been described with respect to specific embodiments for a complete and clear disclosure, the appended claims are not to be thus limited but are to be construed as embodying all modifications and alternative constructions that may occur to one skilled in the art that fairly fall within the basic teaching herein set forth.