WO2014137000A1

WO2014137000A1 - Method for setting data index, and apparatus therefor

Info

Publication number: WO2014137000A1
Application number: PCT/KR2013/001764
Authority: WO
Inventors: 심성화; 김형석; 전형주; 김형은; 정웅경; 육은정; 노명호
Original assignee: 주식회사 랭크웨이브
Priority date: 2013-03-05
Filing date: 2013-03-05
Publication date: 2014-09-12

Abstract

Disclosed is a method for setting a data index comprising: identifying an action of a second user for data uploaded by a first user to a network service; determining an influence index of the second user; determining an action index for the action of the second user; and determining the data index for the data on the basis of the influence index and the action index.

Description

Data index setting method and apparatus

The present invention relates to a data index setting method and apparatus, and more particularly, to a method and apparatus for setting an index of data using an action index for data uploaded in a network service and an influence index in a network service will be.

Due to the increase of internet and smart phone, many network services are being developed and used. Recently, network services, especially social network services, have been used by many users. Due to the large number of users, the amount of data exposed to individuals is very broad. Therefore, the user has a great inconvenience to view meaningless data to himself, which is a problem.

According to an aspect of the present invention, there is provided a method and apparatus for setting an index of data using an action index according to a response behavior of another user to data uploaded to a network service and a user influence index in a network service Furthermore, the present invention is to provide a method and apparatus for providing data with a higher data index in preference.

According to an aspect of the present invention, there is provided a method of setting a data index of data uploaded to a network service by a first user includes verifying a behavior of a second user with respect to data, Extracts a predetermined value, adds a probability related to the occurrence of the behavior to a predetermined value to determine an action index, and determines a data index of the data based on the action index.

According to another aspect of the present invention, there is provided a method of setting a data index of data uploaded to a network service by a first user, comprising: confirming a behavior of a second user with respect to data; Determines a behavior index for a behavior of the second user based on a preset value and a predetermined weight, determines a data exponent of the data based on the behavior index, do.

According to another aspect of the present invention, there is provided a method of setting a data index of data uploaded to a network service by another first user, comprising: confirming a second user's behavior with respect to data; determining an influence index of the second user; , Determines the behavior index for the behavior of the second user, and determines the data exponent of the data based on the influence index and the behavior index.

According to the present invention, it is possible to accurately determine data that can increase the user's interest by setting the data index using the response action of the other party to the uploaded data and / or the influence index of the other party. In addition, by providing the user with data having a high data index ranking in priority, the user can receive data that all users are interested in, without exposure to meaningless data.

1 is a flowchart illustrating a method of setting a data index of data uploaded to a network service according to an exemplary embodiment of the present invention.

Figs. 2 to 5 are views showing respective histograms for Tables 2 to 5. Fig.

6 is a flowchart illustrating a method of setting a data index of data uploaded to a network service according to another embodiment of the present invention.

7 is a diagram illustrating an example of an interface for displaying uploaded data according to a data order according to an embodiment of the present invention.

8 is a block diagram illustrating an apparatus for setting a data index of data uploaded to a network service according to an exemplary embodiment of the present invention.

According to an embodiment of the present invention, a method of setting a data index of data uploaded to a network service by a first user includes confirming a behavior of a second user with respect to the data; Extracting a preset value for an action of the second user; Determining a behavior index by adding a probability associated with the behavior occurrence to the preset value; And determining a data exponent of the data based on the behavior index.

According to another embodiment of the present invention, a method of setting a data index of data uploaded to a network service by a first user includes confirming a behavior of a second user with respect to the data; Extracting a preset value for an action of the second user; Adding a preset weight to the preset value; Determining an action index for the action of the second user based on the predetermined value and a predetermined weight; And determining a data exponent of the data based on the behavior index.

According to another embodiment of the present invention, a method of setting a data index of data uploaded to a network service by a first user includes confirming a behavior of a second user with respect to the data; Determining an influence index of the second user; Determining an action index for an action of the second user; And determining a data index of the data based on the influence index and the behavior index.

Wherein the step of determining the influence index of the second user comprises the steps of: extracting a previous influence index of the stored second user; Determining a data index of data uploaded by the second user up to a predetermined point in time; And determining an influence index of the second user based on the previous influence index of the second user and the data index of the data uploaded by the second user.

Wherein the determining the behavior index for the action of the second user comprises: extracting a preset value for the action of the second user; Adding a preset weight to the preset value; And determining an action index for the action of the second user based on the predetermined value and a predetermined weight.

Wherein the determining the behavior index for the action of the second user comprises: extracting a preset value for the action of the second user; And determining a behavior index by adding a probability associated with the behavior occurrence to the preset value.

The method of setting a data index may further include determining a ranking of the data according to the determined data index.

The data index setting method comprises: ranking the data according to the determined data index; And displaying the uploaded data in priority according to the determined data rank.

According to another embodiment of the present invention, an apparatus for setting data ranking of data uploaded to a network service by a first user includes: a communication unit for receiving information on a behavior of a second user with respect to the data from the network service; A storage unit for storing a predetermined value for an action of the second user; And a control unit for extracting a preset value for the behavior of the second user from the storage unit, determining a behavior index by adding a probability related to the behavior occurrence to the preset value, And a control unit for determining a data index.

According to another embodiment of the present invention, an apparatus for setting a data index of data uploaded to a network service by a first user includes: a communication unit for receiving information on a behavior of a second user with respect to the data from the network service; A storage unit for storing a predetermined value for an action of the second user; And a control unit for extracting a preset value for the behavior of the second user from the storage unit, adding a preset weight to the preset value, And a controller for determining an action index, determining a data index of the data based on the action index, and determining a rank of the data according to the determined data index.

According to another embodiment of the present invention, an apparatus for setting a data index of data uploaded to a network service by a first user includes: a communication unit for receiving information on a behavior of a second user with respect to the data from the network service; A storage unit for storing a previous influence index of the second user; And a control unit for extracting a previous influence index of the second user stored in the storage unit and determining a data index of data uploaded by the second user up to a predetermined time point, 2) determining an influence index of the second user based on the data index of the data uploaded by the user, determining an action index for the behavior of the second user, and based on the influence index and the behavior index, And a control unit for determining a data index of the data.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Referring to FIG. 1, in step 110, a device for setting a data index of data uploaded to a network service (hereinafter referred to as a 'data index setting device' 2 Check the user's behavior. The data index setting device confirms the behavior of the second user with respect to data uploaded by the first user to the network service to which the first user is subscribed or registered. A user means a user who registers or subscribes to at least one network service and uses the service. For example, the network service may be a social network service. Preferably, the data index setting device exists separately from the network service to which the user subscribes. Data refers to all entities that are exchanged between a user and a user in a network service. For example, the data may be information, news, images, video, URL, location information, and so on. The above data are examples, but are not limited thereto. An action of a user means a behavior of a user performed in a network service. Examples of user actions include, but are not limited to, writing on a network service, such as Facebook, commenting on writing, clicking on "like" clicks, uploading data such as images or video, sharing on uploaded data, Service tweets, tweets about other tweets, and so on. The above user action is also an example, but is not limited thereto. The behavior of the first user means that the first user uploads the data to the network service. In the example of the above user action, writing, data upload, and tweet can be regarded as the actions of the first user. The action of the second user means an action of responding to the action of the first user. That is, the action of the second user is an action of responding to the data uploaded by the first user. In the example of the above user action, a comment on a writing, a 'liking' click, a sharing on uploaded data, and a retweet can be regarded as a second user's action.

The data exponent setting device can use the access token of at least one network service to which the user is subscribed to grasp the user's activity in the network service, that is, the behavior and the behavior of another user with respect to the behavior of the user. The data exponent setting device may use the access token to access the resources of the network service to which the user subscribes. The access token is used to request the network service API on behalf of the user. Data index setting Various information regarding the user in the network service can be obtained. An example of a network service, Facebook, is taken as an example. For Facebook, it provides OAuth-based Open API. The key to OAuth authentication is to have the user enter his or her ID and password on Facebook pages, and if the username and password match, issue an access token instead. The issued access token can also be retrieved at any time when the user desires. If you do not use the OAuth method, when you create an interlocking page to log in to Facebook, you enter the user's Facebook ID and password within the network service you want to connect to and use the Server to Server interface And confirms whether the ID and the password are correct. When using this method, the problem is that since the user's password can be obtained from the network service to be interlocked, a security problem arises, so that the trust of the interworking service must be based on, and the authentication information The method is also unclear. To solve this problem, the user's ID and password are entered on the service page provided by Facebook, and instead, an encrypted token is issued to use the Open API. The access token contains information about the user being authenticated, along with information about which APIs are accessible, and in some cases, setting the token to invalid so that it is no longer usable. If the user directly enters the ID and password on the page in Facebook and authenticates and has the issued access token, the following information will be accessible. Examples of information that can be accessed on Facebook include 'accounts' information, which is page information held by an account, 'activities' information, which is profile information of an activity, 'adaccounts', which is advertisement management account information, , 'Apprequests' information of app request information, 'books' information of a book, 'checkins' information of a region based check in, 'cover' information of a photograph used in a cover, 'events' Friend "information," friendrequests ", friend information, and friend information, which are information of the family, which is family information, family information, activity information of the friend, 'friends' information about a game, 'games' information about a game, 'groups' information about group information on Facebook, 'home' information about postings occurring on my network, information about 'inbox' , Tube Links' information, 'movie' information, which is profile information about the movie, 'profile' information about music, 'interest' information, 'Notifications' information, 'outbox' information, 'payment' information, and 'payment information', which are payment information, in the present access 'Photos' information as tag information, 'picture' information as profile picture information, 'posts' information as article information, 'game information', 'information' scores' information, 'statuses' information that I posted on the wall,' tagged 'information that is tagged information,' television 'information that is TV related profile information,' updates' information that is update information, and videos 'Information. All. The data index setting apparatus can confirm the behavior of the first user and the behavior of the second user by using some or all of the information accessible by using the access token as described above. The data index setting device accesses the network service periodically or aperiodically to receive information, and based on this, confirms the behavior of the users and obtains the influence index and the data index to be described below.

In step 120, the data exponent setting device determines the behavior index for the behavior of the second user. The behavior index is a value set for a response behavior to a user action. Equation 1 is an equation for calculating the behavior index.

Equation 1

In Equation (1), SA () denotes an action index, and x denotes a second user who has performed an action. Also, r indicates the number of times the user has made a comment, l indicates whether or not the user likes the user, i indicates the time at which the second user starts the first activity, and s indicates whether or not the user is sharing. The action of responding to an action of a certain user expressed in Equation (1) is merely an example, but is not limited thereto. It can be applied to various response behaviors according to the service aspect of the network service.

According to one embodiment of the present invention, the behavior index may be determined based on a preset value according to an aspect of the response behavior. The equation for calculating the behavior index is shown in Equation (2).

Equation 2

In Equation (2), the behavior index is the sum of predetermined values according to an aspect of the response behavior. The fact that the sum is used in the behavior index is only an example, and the formulas may vary from case to case. Referring to Equation (2), for example, if r = n, r = 0 if there are n comments, l = 1, and 'good' when there are 'good' I = 0 if not, l = 0 if there is no sharing, s = 0 if no sharing, l = 0 if there is no sharing, l = Can be set. The preset value is only an example, and other settings can be made depending on the behavior index provider. In this case, for example, if the second user's action with respect to the uploaded data of the first user is only 'shared with' the likelihood, the action index is 2 (1 + 1).

According to another embodiment of the present invention, the behavior index may be obtained by weighting a predetermined value according to an aspect of the response behavior. The equation for calculating the behavior index is shown in Equation (3).

Equation 3

Equation (3) is obtained by adding a weight to a predetermined value in Equation (2). Referring to Equation (3), a, b, c, and d are weighting coefficients. The weights can be set differently depending on the setting of the provider providing the behavior index. For example, in the case of a predetermined value, for example, if there are n comments, r = n, r = 0 if there is no comment, l = 1 if there is a comment, i = 0 if i = 1, i = 0 if s = 1, s = 1 if there is a share, s = 0 if there is no share, If the second user's action with respect to the uploaded data of the first user is only 'shared with the likes' when the weights a = 2, b = 0.5, c = 1 and d = 0.5 x 1 + 2 x 1).

According to another embodiment of the present invention, the behavior index may be obtained by adding a probability associated with the occurrence of a behavior to a predetermined value according to an aspect of the response behavior. The equation for calculating the behavior index is shown in Equation (4).

[Correction according to Rule 26.03.2013]
Equation 4

In Equation (4), K ₁ to k ₄ are weights for respective probability items. The weight for this probability item can also be changed according to the setting of the configurator. E _RE (r) represents the probability of running comment is more than a predetermined number of, E _LK (l) denotes the probability of the "likes", E _RT (t) is the probability that the second user's behavior in a given time , And E _SH (s) represents the probability of sharing. The above equations are merely examples, but the present invention is not limited thereto. In Equation (4), it is not considered when there is no action of the second user. For example, if a second user has "liked" without commenting and sharing, the function related to comment and sharing is not considered, and SA (x) = f _LK (l) = (1-k ₂ × E _LK l)). Equation (4) may be weighted according to each mode of action as in Equation (3). In another embodiment of the present invention, if there is no access token for the user to obtain the behavior index, the information can not be obtained from the network service, and the behavior index can not be calculated. In this case, the data exponent setting device calculates the behavior index by averaging the probability of the user having the access token.

In one embodiment of the present invention, probability is obtained by extracting information on an action and using a normal distribution. The normal distribution is only one example of obtaining the probability, and there is no limit to the method of obtaining the probability. If the user's behavior is more than a certain number of times, the probability distribution to perform the behavior is a normal distribution. The probability density function can be assumed to be a Gaussian function. Then, when the average value and the standard deviation or the variance value are obtained, the probability of an action can be estimated. Equation (5) is a Gaussian function which is a probability density function.

[Correction according to Rule 26.03.2013]
Equation 5

In Equation (5), f _x () represents a Gaussian function, x represents a random variable, and μ represents an average σ ² .

According to another embodiment of the present invention, the behavior index can be obtained by using the highest probability among the probabilities and the probabilities related to the occurrence of the behavior to predetermined values according to the mode of the response behavior. The equation for calculating the behavior index is shown in Equation (6).

Equation 6

Referring to Equation 6, f _RE when (r) from the second user and only the number of times when a comment highest among only the probability of r _m, only a few comments than _max commented, the k ₁ × E _{RE (r)} is, otherwise, is the _{_{k 1 × (M RE (r}} ) + M RE (r) -E RE (r)). M _RE (r) represents the highest probability of the probability of a comment month. In the case of the second user commenting faster than i _{m, max,} which is the highest probability among the time of the second user's commenting in f _RT (i), k ₃ × (M _RT + M _RT -E _RT , Otherwise k ₃ × E _RT (i). M _RT represents the highest probability of commenting time. f _LK (l) and f _SH (s) are the same as in Equation (4).

In the following, an example of the probability required to determine the behavior index will be described. Table 1 is a table showing the number of actions that the second users A, B, C, and D made a comment, which is a response to the upload data of the first user.

Table 1

A Comments	B's Comments	C Comments	D Comments
2	2	2	One
0	0	2	0
One	One	3	0
One	One	0	0
0	0	2	One
0	0	0	One
2	0	3	0
0	One	One	One
0	One	0	One
One	0	2	0
0	0	2	0
0	One	3	One
0	0	2	One
One	0	2	One
0	0	2	One
0	One	2	One
0	0	2	0
0	One	2	0
0	0	2	0
0	0	3	0
One	0	One	2
One	One	2	0
0	One	2	One
0	0	2	0
0	0	2	0
0	One	2	One
0	One	2	0
0	0	2	0
One	One	2	0
One	0	2	0
0	0	0	One
0	One	0	0
0	0	0	0
0	0	0	0
0	0	2	0
One	One	0	0
One	0	2	0
0	0	2	One
0	One	2	One
0	0	2	0
One	0	3	0
One	0	2	0
0	0	2	0
One	0	2	One
0	0	2	0
0	One	2	0
0	0	2	One
0	0	2	0
2	One	One	0
0	0	2	0
0	One	2	0
0	0	2	0
0	0	2	One
0	0	3	0
0	One	3	0
0	One	2	0
One	0	2	0
0	0	2	0
One	0	2	0
0	0	2	0

Tables 2 to 5 are tables for the comment probabilities of A, B, C, and D based on Table 1. Referring to Tables 2 to 5, probability density according to the number of actions, frequency, and frequency of each of the users A, B, C, and D is shown.

Table 2

Number of times	frequency	Probability density
0	42	0.57820441
One	15	0.365736157
2	3	0.010918535
3	0	1.53841E-05
4	0	1.02303E-09
5	0	3.2108E-15
Other
	0

Table 2 shows the probability density for the behavior of A. As shown in Table 1, the average number of A actions is 0.35, and the standard deviation is 0.5722762, so that the probability density is obtained as shown in Table 2. Referring to Table 2, the probability that A creates two comments on the behavior of the first user is 1.09%.

Table 3

Number of times	frequency	Probability density
0	39	0.601029655
One	20	0.363781068
2	One	0.0050976
3	0	1.65376E-06
4	0	1.24211E-11
5	0	2.15989E-18
Other
	0

Table 3 shows the probability density of B's behavior. Referring to Table 1, the average number of B actions is 0.3666667, and the standard deviation is 0.5153208, so that the probability density is obtained as shown in Table 3. Referring to Table 3, the probability of B making a comment on the behavior of the first user is 36.37%.

Table 4

Number of times	frequency	Probability density
0	8	0.042183494
One	3	0.302392542
2	42	0.476406479
3	7	0.16495379
4	0	0.012552349
5	0	0.000209926
Other	0

Table 4 shows the probability densities for C's behavior. As shown in Table 1, the average number of C actions is 1.8 and the standard deviation is 0.8124038, so that the probability density is obtained as shown in Table 4. Referring to Table 4, the probability that C will make three comments on the behavior of the first user is 16.49%.

Table 5

Number of times	frequency	Probability density
0	41	0.634973438
One	18	0.330765365
2	One	0.0034442479
3	0	7.15829E-07
4	0	2.97395E-12
5	0	2.46857E-19
Other
	0

Table 5 shows the probability density for the behavior of D. Referring to Table 1, the average number of D behaviors is 0.3333333 and the standard deviation is 0.505525, so that the probability density is obtained as shown in Table 5. Referring to Table 5, the probability that D will make one comment on the behavior of the first user is 33.07%.

Figs. 2 to 5 are views showing respective histograms for Tables 2 to 5. Fig.

3 is a histogram 300 of the probability density for the behavior of the second user B, and FIG. 4 is a histogram of the probability density of the second user C, FIG. 5 is a histogram 500 of the probability density for the behavior of the second user D, and FIG. 2 to 5, Freq 201 to 501 indicate the frequency of comments, and probability densities 202 to 502 represent probability density according to frequency and frequency. The vertical axis on the left side is the axis related to the probability density, and the vertical axis on the right side is the axis corresponding to the frequency number.

Hereinafter, an example of obtaining the behavior index according to Equations (4) and (6) will be described.

Table 6 is a table of the responses of other users to the data uploaded by the user for a predetermined period, that is, comments. In Table 6, it is assumed that the users A, B, C, and D have uploaded 20 pieces of data.

Table 6

A uploaded data

Upload data for B

C uploaded data

D uploaded data

One

B

D

B

C

One

A

D

A

C

One

A

One

A

C

2

C

2

C

2

B

A

2

A

C

3

B

C

3

D

A

C

3

B

3

C

4

B

4

C

A

C

4

D

4

C

A

C

5

C

D

C

5

C

5

C

6

D

6

D

C

6

B

6

B

C

7

C

7

A

C

7

D

B

7

C

8

B

C

D

8

C

8

C

9

B

D

9

C

9

A

B

9

B

C

A

10

C

10

A

C

10

A

10

C

11

C

11

D

11

B

C

12

B

D

C

12

B

12

C

13

D

C

13

D

13

C

14

D

C

14

A

14

C

15

D

C

15

C

15

B

C

16

B

D

C

16

A

B

16

B

C

17

C

17

C

17

A

17

A

C

18

B

C

18

D

C

18

C

19

C

19

D

C

19

B

19

C

A

C

20

C

20

C

20

C

In Table 6, the numbers represent the data uploaded by the user and the other users who comment on the uploaded data. The second user who commented on B uploaded data 1 is A, C, D, A has 2, C has 1, D has 2. In the example below, it is assumed that the comment is taken only as an example and other actions are not considered. In Equation (4) and Equation (6), K ₁ is assumed to be 1. In the following, E _RE1 (), subscript 1 in SA ₁ refers to data 1. The probability density E _RE1 (A, 2) is 0.010918535, where A replies twice to one piece of data. Referring to Table 4, C gives one comment to one piece of data. The probability density E _RE1 (C, 1) it is 0.302392542, and the table you see the 5, D two times a month, the probability comment on one of the data density E _RE1 (D, 2) is 0.003442479.

According to Equation (4), the behavior index for which A has two comments becomes 0.989081465 according to SA ₁ (A, 2) = (1-K ₁ × E _RE1 that act index _{SA 1 (C, 1) =} (1-K 1 × E RE1 (C, 1)) , and the 0.697607458 according to, D is behavior index that two comments are _{SA 1 (D, 2) =} ( 1-K ₁ x E _RE1 (D, 2)).

According to Equation (6), since A has made two comments, A has more than 0, which is the highest probability of a comment, and D is two comments, so D is the most likely The number of high probability was more than 0 times. However, since C has made one comment, C is less than 2 times the probability of the highest probability of making a comment. Therefore, the behavior index for which A has two comments is obtained according to SA ₁ (A, 2) = K ₁ × ((M _RE (A) + M _RE (A) -E _{RE 1} (A, 2)). Referring to Table 2, to be _{M RE (a) = 0.57820441,} E RE1 (a, 2) = is the 0.010918535 _{Thus, SA 1 (a, 2)} = K 1 × ((M RE (a) + M ₁ (C, 1) = K ₁ × E _RE1 (C), where C is a comment, _RE (A) -E _RE1 (A, 2)) = (0.57820441 + 0.57820441-0.010918535) = 1.145490285 , obtained according to 1). Referring to Table _{4, E RE1 (C, 1} ) = 0.302392542 becomes _{Accordingly, SA 1 (C, 1)} = K 1 × E RE1 (C, 1) = is the 0.302392542 . act index is determined in accordance with the _{SA 1 (D, 2) =} K 1 × ((M RE (D) + M RE (D) -E RE1 (D, 2)) D is that two commented Table 5, to be _{M RE (D) = 0.634973438,} E RE1 (D, 2) = is the 0.003442479 _{Thus, SA 1 (D, 2)} = K 1 × ((M RE (D) + M RE (D) -E _RE1 (D, 2)) = (0.634973438 + 0.634973438-0.003442479) = 1.266504397.

In step 130, the data exponent setting device determines the data exponent of the uploaded data based on the behavior index. The data index setting device substitutes the behavior index into a predetermined formula to obtain a data index (value). Equation (7) is an example of a formula for obtaining a data index.

[Correction according to Rule 26.03.2013]
Equation 7

Referring to Equation (7), DV (N) in DV (N) represents a data index and N represents data created by the first user. x denotes a set of users who responded to N data. From Equation (7), it can be seen that the data index is calculated by summing the behavior index of the second user's response behavior to the first user's behavior.

In Table 6, the data index for the data 1 uploaded by B is calculated according to Equation (7). In the following, DV ₁ , B ₁ , E _RE1 (), subscript 1 in SA ₁ means that it is associated with data 1. According to Equation 7, DV ₁ (B ₁ ) = f _DV (SA ₁ (A, 2)) + f _DV (SA ₁ (C, 1)) + f _DV (SA ₁ . Substituting Equation _{_{4, f DV (SA 1 (}} A, 2)) = (1-K 1 × E RE1 (A, 2)) = 0.989081465, f DV (SA 1 (C, 1)) = (1 Since _{_{-K 1 × E RE1 (C,}} 1)) = 0.697607458 f DV (SA 1 (D, 2)) = (2-K 1 × E RE1 (D, 2)) = 0.996557521, DV 1 (B 1) = 0.989081465 + 0.697607458 + 0.996557521 = 2.683246444. Substituting equation _{_{6, f DV (SA 1 (}} A, 2)) = K 1 × ((M RE (A) + M RE (A) -E RE1 (A, 2)) = 1.145490285, and the, f _{_{DV (SA 1 (C, 1}} )) = K 1 × E RE1 (C, 1) = become _{_{0.302392542, f DV (SA 1 (}} D, 2)) = K 1 × ((M RE (D) + M since the _{_{RE (D) -E RE1 (D}} , 2)) = 1.266504397, is the _{_{DV 1 (B 1) = 1.145490285}} + 0.302392542 + 1.266504397 = 2.714387224.

In step 140, the data exponent setting device determines the data rank according to the data index of the uploaded data among the data for which at least one data exponent is determined. Preferably, the higher the data index, the higher the ranking.

FIG. 6 is a flowchart illustrating a method of setting a ranking on data uploaded to a network service according to another embodiment of the present invention.

Referring to FIG. 6, step 610 is the same as step 110 of FIG. 1, and thus will be omitted in order to avoid redundant description.

In step 620, the data index setting device determines the influence index of the second user. The influence index is an influence calculated based on the activity of a user in a network service or the behavior of another user's response to the activity, that is, the degree of response of another user. The behavior of a socially famous person or a response of a famous person to an action of another person has a great influence or influence on a network service rather than a normal person's action. The data index setting device calculates an influence index of a user by analyzing a behavior of a user in a network service, that is, another user's behavior on uploaded data. In an embodiment of the present invention, the data index setting device calculates an influence index based on a data index of data created by a user during a predetermined period. Equation (8) is an equation for calculating the influence index.

[Correction according to Rule 26.03.2013]
Equation 8

SV _t () is in the interval of t represents an impact factor, A is the data index for the A of the section of DV _t () is t in represents the estimated user's impact factor, DV _t (n) in SV _t (A) , And n represents a set of data created by A

For example, the influence index SV ₂ (A) = DV ₂ (A 1) + DV ₂ (A 2) + + DV ₂ (AN). Here, A1 to AN represent the number of pieces of data uploaded by the user A in two sections. _{_{DV 2 (A1) ~ DV 2}} (AN) , respectively can be calculated using the equation (7).

In addition, in another embodiment of the present invention, the data index setting device adds an influence index calculated at a point in time or a time immediately preceding a time for calculating a current influence index to a data index of data created by a user during a predetermined section, The index can be calculated. Equation (9) is another expression for calculating the influence index.

[Correction according to Rule 26.03.2013]
Equation 9

The difference between Equation (9) and Equation (8) is that the influence index can be calculated by further using the influence index at t-1, which is the time point before or after t. The influence index can be obtained by obtaining a data index for all data uploaded by a user in a predetermined section, adding all of the data indexes, and adding the influence index of the previous section to this value. For example, the influence index SV ₂ (A) = SV ₁ (A) + DV ₂ (A 1) + DV ₂ (A 2) + + DV ₂ (AN). Here, A1 to AN represent the number of pieces of data uploaded by the user A in two sections. The value of SV ₀ (A), which is the raw influence index, can be set by the operator of the data index setting device.

Step 630 is the same as step 120 of FIG. 1, and thus will be omitted in order to avoid redundant description.

In step 640, the data exponent setting device determines the data exponent of the uploaded data based on the influence index and behavior index of the second user. Step 340 is to add an influence index to the behavior index to determine the data index, as compared to step 130 of FIG. The data index setting device obtains a data index (value) according to a predetermined formula of the influence index and the behavior index of the extracted second user. Equation (10) is an example of a formula for obtaining a data index.

[Correction according to Rule 26.03.2013]
Equation 10

Referring to Equation (10), DV (n) represents a data index, and N represents data created by the first user. x denotes a set of users who responded to N data. In addition, SV (x) represents the influence index of x at the time point at which the data index is to be obtained or at the previous point or section of the interval as described above. From Equation (10), it can be seen that the data index is obtained based on the influence index and the action index. Equation (11) shows an example of f _DV (SV (x), SA (x)).

[Correction according to Rule 26.03.2013]
Equation 11

Referring to Equation (11), a and b are weights. The weights can be set by the provider of the data index setting method.

In Table 6, it is assumed that the data exponent for the data 1 uploaded by B is obtained according to Equation (10) and Equation (11). According to Equation _{_{10, DV 1 (B 1)}} = f DV (SV 0 (A), SA 1 (A, 2)) + f DV (SV 0 (C), SA 1 (C, 1)) + f _DV (SV ₀ (D), SA ₁ (D, 2)). In the above example, it is assumed that the weights a and b and the influential indexes SV ₀ (A), SV ₀ (C), and SV ₀ (D) Referring to Equation _{_{4, f DV (SA 1 (}} A, 2)) = (1-K 1 × E RE1 (A, 2)) = 0.989081465, f DV (SA 1 (C, 1)) = (1 _{_{-K 1 × E RE1 (C,}} 1)) = 0.697607458, f DV (SA 1 (D, 2)) = (1-K 1 × E RE1 (D, 2)) Since = 0.996557521, DV _₁ (B ₁ ) = (1 + 0.98081465) + (1 + 0.697607458) + (1 + 0.996557521) = 5.683246444. Referring to Equation (6), f _DV (SA ₁ (A, 2)) = K ₁ x (M _RE (A) + M _RE (A) -E _RE1 (A, 2) = 1.145490285, f _{_{DV (SA 1 (C, 1}} )) = K 1 × E RE1 (C, 1) = become _{_{0.302392542, f DV (SA 1 (}} D, 2)) = K 1 × ((M RE (D) + M because _{_{RE (D) -E RE1 (D}} , 2)) = 1.266504397, is the _{_{DV 1 (B 1) = (}} 1 + 1.145490285) + (1 + 0.302392542) + (1 + 1.266504397) = 5.714387224.

When the data ranking is determined in FIG. 1 or 6, the data index setting method may further include displaying the uploaded data with priority in accordance with the data ranking.

7 is a diagram illustrating an example of an interface for displaying uploaded data according to a data order according to an embodiment of the present invention. Referring to FIG. 7, data uploaded according to the categories 710 to 730 is displayed on the interface 700. FIG. The uploaded data 711 to 716 are displayed in the category 710, and the uploaded data 711 having the highest data index is displayed first. Thereafter, the next data 712 to 716 is displayed according to the data ranking.

The predetermined values, weights, determined behavior index, determined data index, and determined influence index used in the calculation of the behavior index are stored in a predetermined place of the data index setting device.

FIG. 8 is a block diagram of an apparatus for setting ranking on data uploaded to a network service according to an exemplary embodiment of the present invention. Referring to FIG.

8, the data index setting apparatus 800 includes a communication unit 802, a storage unit 804 and a control unit 806. The data index setting apparatus 800 includes a network service 810 and a user terminal 820 via a wired or wireless network.

Network service 810 is at least one network service 810 to which a user is subscribed. As an example, the network service 810 may be a social network service. The communication unit 802 receives information on the behavior of the second user with respect to the data uploaded by the first user from the network service 810 to which the first user is subscribed or registered. A user signifies or registers with at least one network service 810 and uses the service. Data refers to all entities that are exchanged between a user and a user in the network service 810. For example, the data may be information, news, images, video, URL, location information, and so on. The above data are examples, but are not limited thereto. An action of a user means a behavior of a user performed in the network service 810. Examples of user actions include, but are not limited to, writing on a network service, such as Facebook, commenting on writing, clicking on "like" clicks, uploading data such as images or video, sharing on uploaded data, Service tweets, tweets about other tweets, and so on. The above user action is also an example, but is not limited thereto. The behavior of the first user means that the first user uploads the data to the network service. In the example of the above user action, writing, data upload, and tweet can be regarded as the actions of the first user. The action of the second user means an action of responding to the action of the first user. That is, the action of the second user is an action of responding to the data uploaded by the first user. In the example of the above user action, a comment on a writing, a 'liking' click, a sharing on uploaded data, and a retweet can be regarded as a second user's action.

The data exponent setting device 800 may use the access token to at least one network service 810 to which the user is subscribed to determine the user's activity in the network service 810, The behavior of other users can be grasped. The data index setting device 800 can access the resources of the network service 810 to which the user subscribes using the access token. The access token is used to request the network service 810 API on behalf of the user. It is possible to obtain various information regarding the user in the data ranking setting network service 810. [ An example of the network service 810 is Facebook. For Facebook, it provides OAuth-based Open API. The key to OAuth authentication is to have the user enter his or her ID and password on Facebook pages, and if the username and password match, issue an access token instead. The issued access token can also be retrieved at any time when the user desires. If you do not use the OAuth method, when you create an interlocking page to log in to Facebook, you enter the user's Facebook ID and password in the network service (810) that you want to connect to and use the Server to Server ) Interface to verify that the ID and password match. The problem with using this method is that the network service 810 that is to be interlocked can find out the password of the user so that there is a problem in terms of security and therefore the trust of the interlocking service must be based on, Is not clear. To solve this problem, the user's ID and password are entered on the service page provided by Facebook, and instead, an encrypted token is issued to use the Open API. The access token contains information about the user being authenticated, along with information about which APIs are accessible, and in some cases, setting the token to invalid so that it is no longer usable. If the user directly enters the ID and password on the page in Facebook and authenticates and has the issued access token, the following information will be accessible. Examples of information that can be accessed on Facebook include 'accounts' information, which is page information held by an account, 'activities' information, which is profile information of an activity, 'adaccounts', which is advertisement management account information, , 'Apprequests' information of app request information, 'books' information of a book, 'checkins' information of a region based check in, 'cover' information of a photograph used in a cover, 'events' Friend "information," friendrequests ", friend information, and friend information, which are information of the family, which is family information, family information, activity information of the friend, 'friends' information about a game, 'games' information about a game, 'groups' information about group information on Facebook, 'home' information about postings occurring on my network, information about 'inbox' , Tube Links' information, 'movie' information, which is profile information about the movie, 'profile' information about music, 'interest' information, 'Notifications' information, 'outbox' information, 'payment' information, and 'payment information', which are payment information, in the present access 'Photos' information as tag information, 'picture' information as profile picture information, 'posts' information as article information, 'game information', 'information' scores' information, 'statuses' information that I posted on the wall,' tagged 'information that is tagged information,' television 'information that is TV related profile information,' updates' information that is update information, and videos 'Information. All. The data index setting device 800 can confirm the behavior of the first user and the behavior of the second user by using some or all of the information accessible using the access token as described above. The data index setting device 800 accesses the network service 810 periodically or non-periodically through the communication unit 802 to receive information, and confirms the behavior of the users based on the received information. The influence index and data Quot; index ".

The control unit 806 determines an action index for the action of the second user. The behavior index is a value set with respect to a behavior of a response to a user action. An example of an equation for obtaining the behavior index is shown in Equation 1 above. According to one embodiment of the present invention, the behavior index may be determined based on a preset value according to an aspect of the response behavior. An example of an equation for calculating an action index is shown in Equation (2). The behavior index is the sum of the predetermined values according to the mode of the response behavior. The fact that the sum is used in the behavior index is only an example, and the formulas may vary from case to case. According to another embodiment of the present invention, the behavior index may be obtained by weighting a predetermined value according to an aspect of the response behavior. An example of an equation for calculating the behavior index is shown in Equation (3). According to another embodiment of the present invention, the behavior index may be obtained by adding a probability associated with the occurrence of a behavior to a predetermined value according to an aspect of the response behavior. An example of an equation for calculating the behavior index is shown in Equation (4). In this case as well, as in the case of Equation (3), weights may be further added in accordance with each mode of action. According to another embodiment of the present invention, the behavior index can be obtained by using the highest probability among the probabilities and the probabilities related to the occurrence of the behavior to predetermined values according to the mode of the response behavior. An example of an equation for calculating the behavior index is shown in Equation (6). In another embodiment of the present invention, if there is no access token for the user to obtain the behavior index, the information can not be obtained from the network service 810 and the behavior index can not be calculated. In this case, the control unit 806 calculates a value obtained by averaging the probability of the user having the access token as an action index. In one embodiment of the present invention, probability is obtained by extracting information on an action and using a normal distribution. This is an example only, and there is no limit to how to obtain the probability. If the user's behavior is more than a certain number of times, the probability distribution to perform the behavior is a normal distribution. The probability density function can be assumed to be a Gaussian function. Then, when the average value and the standard deviation or the variance value are obtained, the probability of an action can be estimated.

Based on Tables 1 to 6, an example of determining an action index using the probability is described above, and therefore, will not be described below.

The control unit 806 determines the data index of the uploaded data based on the behavior index. The control unit 806 substitutes the behavior index into a predetermined formula to obtain a data index (value). Equation (7) is an example of a formula for obtaining a data index.

The control unit 806 determines the data rank according to the data index of the uploaded data among the data for which at least one data index has been determined. Preferably, the higher the data index, the higher the ranking.

In another embodiment of the present invention, the control unit 806 determines the influence index of the second user. The influence index means an influence calculated based on the activity of a user in the network service 810 or the behavior of another user's response to the activity, that is, the degree of reaction of another user. The behavior of a socially famous person or an act of responding to the actions of other people of a famous person has a great influence or influence on the network service (810) rather than the behavior of a normal person. The control unit 806 analyzes a behavior of a user in the network service 810, that is, the behavior of another user with respect to the uploaded data, and calculates the influence index of the user. In an embodiment of the present invention, the controller 806 calculates the influence index based on the data index of the data created by the user during a predetermined time or period. Alternatively, the control unit 806 may calculate the influence index by adding the influence index calculated at a point in time immediately before or at the time of calculating the current influence index to the data index of the data created by the user for a predetermined time or section. An example of an equation for calculating the influence index is shown in Equations (8) and (9) above.

Then, the control unit 806 determines the data exponent of the uploaded data based on the influence index and the behavior index of the second user. The control unit 806 obtains a data index (value) according to a predetermined expression of the influence index and the behavior index of the extracted second user. Equations (10) and (11) are examples of formulas for obtaining data exponents.

The storage unit 804 stores a predetermined value, a weight, a determined behavior index, a determined data index, and a determined influence index used in the calculation of the behavior index.

The control unit 806 transmits a command for preferentially displaying the uploaded data to the user terminal 820 via the communication unit 802 in accordance with the rank of the determined data index.

The data index setting method as described above can also be implemented as a computer-readable code on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording media in which data that can be read by a computer system is stored. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like. The computer readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner. And, functional programs, codes, and code segments for implementing the disk management method can be easily deduced by the programmers of the present invention.

The present invention has been described with reference to the preferred embodiments. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.

Claims

A method for setting a data index of data uploaded to a network service by a first user,

Confirming a behavior of a second user with respect to the data;

Extracting a preset value for an action of the second user;

Determining a behavior index by adding a probability associated with the behavior occurrence to the preset value; And

And determining a data index of the data based on the behavior index.
A method for setting a data index of data uploaded to a network service by a first user,

Confirming a behavior of a second user with respect to the data;

Extracting a preset value for an action of the second user;

Adding a preset weight to the preset value;

Determining an action index for the action of the second user based on the predetermined value and a predetermined weight; And

And determining a data index of the data based on the behavior index.
A method for setting a data index of data uploaded to a network service by a first user,

Confirming a behavior of a second user with respect to the data;

Determining an influence index of the second user;

Determining an action index for an action of the second user; And

And determining a data index of the data based on the influence index and the behavior index.
The method of claim 3,

Wherein determining the influence index of the second user comprises:

Extracting a previous influence score of the stored second user;

Determining a data index of data uploaded by the second user up to a predetermined point in time; And

Determining an influence index of the second user based on the previous influence index of the second user and the data index of the data uploaded by the second user.
The method of claim 3,

Wherein the determining the behavior index for the behavior of the second user comprises:

Extracting a preset value for an action of the second user;

Adding a preset weight to the preset value;

And determining an action index for the action of the second user based on the preset value and a preset weight.
The method of claim 3,

Wherein the determining the behavior index for the behavior of the second user comprises:

Extracting a preset value for an action of the second user; And

And determining a behavior index by adding a probability associated with the behavior occurrence to the preset value.
4. The method according to any one of claims 1 to 3,

And determining a ranking of the data according to the determined data index.
4. The method according to any one of claims 1 to 3,

Determining a ranking of the data according to the determined data index; And

And displaying the uploaded data in a priority order according to the determined data rank.
An apparatus for setting a data rank of data uploaded to a network service by a first user,

A communication unit for receiving information on a behavior of a second user with respect to the data from the network service;

A storage unit for storing a predetermined value for an action of the second user; And

Extracting a preset value for the behavior of the second user from the storage unit, determining a behavior index by adding a probability related to the behavior occurrence to the preset value, and calculating, based on the behavior index, And a control unit for determining an index.
An apparatus for setting a data index of data uploaded to a network service by a first user,

A communication unit for receiving information on a behavior of a second user with respect to the data from the network service;

A storage unit for storing a predetermined value for an action of the second user; And

Extracting a preset value for the behavior of the second user from the storage unit, adding a preset weight to the preset value, and calculating an action on the behavior of the second user based on the preset value and a predetermined weight value Determining a data index of the data based on the behavior index, and determining a ranking of the data according to the determined data index.
An apparatus for setting a data index of data uploaded to a network service by a first user,

A communication unit for receiving information on a behavior of a second user with respect to the data from the network service;

A storage unit for storing a previous influence index of the second user; And

Extracting a previous influence index of the second user stored in the storage unit and determining a data index of data uploaded by the second user up to a predetermined time point to determine a previous influence index of the second user and a second influence index of the second user, Determining an influence index of the second user based on the data index of data uploaded by the user, determining an action index for the action of the second user, and determining, based on the influence index and the action index, And a control unit for determining a data index of the data.
12. The method according to any one of claims 9 to 11,

Wherein the controller determines the order of the data according to the determined data index.
12. The method according to any one of claims 9 to 11,

Wherein the control unit transmits a command for preferentially displaying the uploaded data to the external device via the communication unit according to the determined data order.