KR20170011361A - Device and method for providing data, and computer program for executing the method - Google Patents
Device and method for providing data, and computer program for executing the method Download PDFInfo
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- KR20170011361A KR20170011361A KR1020150103874A KR20150103874A KR20170011361A KR 20170011361 A KR20170011361 A KR 20170011361A KR 1020150103874 A KR1020150103874 A KR 1020150103874A KR 20150103874 A KR20150103874 A KR 20150103874A KR 20170011361 A KR20170011361 A KR 20170011361A
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Abstract
Description
Embodiments of the present invention are related to a data providing apparatus, a method, and a computer program.
With the development of electronic communication technology, a user can use various functions as an electronic device capable of wired / wireless communication. The user can use the electronic device to access the Internet and send / receive data to / from another person's electronic device or server.
By using these electronic communication technologies, we can provide services that enable people to connect with friends (such as friends, seniors, and colleagues), build up a new network, and form a broader human network (Social Network Service). It is also called 'SNS'. This is a one-person, one-person community that enables people to share personal information on the Internet and help them communicate. As the desire for personal expression becomes stronger, the social network service which keeps the social relationship between people and maintains the friendship relationship is gradually developing.
The above-described background technology is technical information that the inventor holds for the derivation of the present invention or acquired in the process of deriving the present invention, and can not necessarily be a known technology disclosed to the general public prior to the filing of the present invention.
Embodiments of the present invention provide a data providing device capable of selecting an associated tag associated with a specific tag and providing it to a user by selecting an associated tag among a plurality of tags used together by a plurality of users, And a computer program.
In embodiments of the present invention, in selecting an association tag related to a specific tag and providing it to a user, the association tag may be selected and reflected to the user by reflecting the category of the tagged data, the similarity between tags, and the timeliness A data providing apparatus, a method, and a computer program.
According to an embodiment of the present invention, there is provided a method of providing data in a data providing apparatus, comprising: clustering tags by author by a control unit; Calculating a degree of association between each of the clusters by a control unit; And determining, by the control unit, a priority of associating tags for a specific tag based on the degree of association.
Another embodiment of the present invention discloses a computer program stored on a medium for executing the method using a computer.
According to another embodiment of the present invention, a control unit analyzes tags included in a plurality of data stored in a database and selects at least one association tag for each tag. And a communication unit for providing the association tags to the user terminal, wherein the control unit clusters the tags for each author, calculates the association degrees between the clusters, And determines the priority of the associated tags for the data.
Other aspects, features, and advantages will become apparent from the following drawings, claims, and detailed description of the invention.
According to the present invention, there is provided a data providing device capable of selecting an associated tag related to a specific tag and providing it to a user by selecting an associated tag among a plurality of tags used together by a plurality of users , And a computer program.
Also, in selecting an associated tag related to a specific tag and providing it to a user, a data providing device that can select an associated tag reflecting the category of the tagged data, the similarity and the timeliness between the tags, Methods, and computer programs.
1 is a diagram schematically showing a configuration of a data providing system according to an embodiment of the present invention.
2 is a block diagram schematically showing an example of the internal configuration of the data providing apparatus of FIG.
3 and 4 are flowcharts schematically illustrating an example of a data providing method according to an embodiment of the present invention.
5 is a view schematically showing an exemplary form in which data is provided through a data providing method according to an embodiment of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS The present invention is capable of various modifications and various embodiments, and specific embodiments are illustrated in the drawings and described in detail in the detailed description. The effects and features of the present invention and methods of achieving them will be apparent with reference to the embodiments described in detail below with reference to the drawings. However, the present invention is not limited to the embodiments described below, but may be implemented in various forms. In the following embodiments, the terms first, second, and the like are used for the purpose of distinguishing one element from another element, not the limitative meaning. Also, the singular expressions include plural expressions unless the context clearly dictates otherwise. Also, the terms include, including, etc. mean that there is a feature, or element, recited in the specification and does not preclude the possibility that one or more other features or components may be added. Also, in the drawings, for convenience of explanation, the components may be exaggerated or reduced in size. For example, the size and thickness of each component shown in the drawings are arbitrarily shown for convenience of explanation, and thus the present invention is not necessarily limited to those shown in the drawings.
Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, wherein like reference numerals refer to like or corresponding components throughout the drawings, and a duplicate description thereof will be omitted .
FIG. 1 is a view schematically showing a configuration of a data providing system according to an embodiment of the present invention, and FIG. 2 is a block diagram schematically illustrating an example of the internal configuration of the data providing apparatus of FIG.
Referring to FIGS. 1 and 2, a
The
The
The
Alternatively, the
Meanwhile, 'data' according to one embodiment of the present invention may mean various information or contents provided through the Internet or computer communication. The 'material' may be an electronic file or an electronic signal that contains information that a person can perceive through at least one of the five senses, such as an electronic file containing information about a still image, a moving image, text, and sound. The
In addition, the user can specify one or more tags for the data while uploading the data. A tag can be a text-based message, and can consist of words related to the data or words selected by the user.
For example, the user can upload pictures taken while traveling in the Udo Island of Jeju Island, and specify texts such as 'Jeju Island', 'Jeju Island Travel', 'Udo Island' and 'Udo Island Landscape' as tags for the corresponding photos. These tags can be used for various functions such as data management, inquiry, and search. Since the author or registrant of the data designates the tag directly, an appropriate tag without error can be designated in terms of the actual relation with the data, the trend reflection, and the like. Further, the user may specify one or more tags in one data item. Hereinafter, when a first tag is assigned to the first data, the first data may be referred to as data associated with the first tag, and the first tag may be referred to as a tag associated with the first data. The
In addition, the
For example, the
In another example, the
In addition, the
For example, the
The
The
Here, the
The
The
The
The
The
Referring again to FIG. 1, the
At this time, each of the
1, the
The
3 and 4 are flowcharts schematically illustrating an example of a data providing method according to an embodiment of the present invention. Hereinafter, a data providing method using the
Referring to FIG. 3, the
Thereafter, the
Thereafter, the
Thereafter, the
Thereafter, the
At this time, the
Here, the page is a visual arrangement of the data and tags provided through the data providing service, and may be an electronic document provided through an Internet web page or an application.
An example of such a page may be an electronic document graphic user interface (GUI) 160 that may be provided on the
Accordingly, the
Hereinafter, the step S100 of FIG. 3 will be described in more detail.
According to the embodiment shown in FIG. 4, the step S100 of the data providing method (the step of analyzing the tags included in the plurality of data and selecting the associated tags for each tag) includes clustering tags for each author ), Calculating the association degree of each tag (step S 120), and determining the ranking of the selected association tags (step S 130).
Here, the step of clustering tags by author (step S110) is characterized in that tags frequently used by a plurality of users are selected as association tags. In this way, when clustering tags by author, it can be judged that the degree of association is higher as the tag pairs frequently appear together in a plurality of data. However, it can be judged that the degree of association of the tag pairs created by a plurality of users is higher than the tag pairs created by one user many times.
As a specific example, assume that two users have created four data, as shown in Table 1 below.
User 1 wrote three data: Data 1, Data 2, and Data 3. In these three data, 'Jeju Travel' tags are included all three times, but since they are all tags created by the same user 1, the frequency of 'Jeju Travel' tag created by User 1 is set to 1. Likewise, all three "black pork" tags were included in the three data from data 1, 2, and 3, but since they are all tags created by the same user 1, The frequency is treated as 1.
Therefore, the frequency of each tag of 'Jeju Travel', 'Udo', 'Black Pig', 'Seogwipo', 'Up' and 'Olle Road' included in User 1 's data is set to 1.
User 2, on the other hand, created one piece of data 4. The frequency of each tag of 'Seogwipo', 'Jeju Travel' and 'Ollewal' included in this one data is treated as 1.
The frequency of 'Jeju Travel', 'Seogwipo', and 'Olgal' is 2, and the frequency of 'Udo', 'Black Pig', and 'Up' is 1 when the tag of User 1 and the tag of User 2 are added together. do.
Also, since the 'Jeju Travel' tag and the 'Ole Street' tag are included in the data 3 created by the user 1 and included in the data 4 created by the user 2, the 'Jeju Travel' tag and the ' The appearance frequency is 2.
As a result, the clustering results of the four tags created by the two users are the same as in Table 2 (frequency of tags) and Table 3 (frequency of coexistence between tags).
Next, the step of calculating the degree of association for each tag (step S120) may be performed by calculating a degree of association for each of the other tags of one tag. Step S120 may be various kinds of numerical or logical conditions for determining the degree of association of specific data in the system. In addition, this association calculation may be performed by the
As a specific example, an association calculation between each tag can be performed based on a normalized pointwise mutual information (NPMI) method. First, pointwise mutual information (PMI) is a method based on probability theory and is an index expressing the association of specific values of two random variables. Assuming that words with similar polarity are likely to appear in the same document, PMI can be used to measure the association of two vocabularies (tags). The association of the two vocabularies using the PMI is represented by the following equation (1).
[Equation 1]
Here, x and y are two words to be related. PMI is expressed as a probability value that two words appear in the same document and a probability value that a specific word appears in the document as in the above expression. If the probability that the two words appear is independent of each other, the PMI value will be zero. If the PMI value is positive, it means that the two vocabularies are likely to appear in the same document and have a similar semantic polarity, and the PMI value being negative indicates that the two words are less likely to appear in the same document, It will mean.
If the association of two vocabularies obtained using the PMI is normalized, it can be expressed as the following equation (2).
&Quot; (2) "
In calculating the degree of association between the two tags, the degree of association is calculated for each category of the data including the tag, and then the value of the npmi between the two tags in the category and the value of the corresponding tag in the category The product of frequency can be summed over all categories to obtain the association for the entire category. Accordingly, the higher the relevance is, the more the tags of the predominant subject (that is, the more frequent subject) are calculated. The association calculation according to the category can be expressed by the following equation (3).
&Quot; (3) "
Next, it is possible to perform the step of determining the ranking of the selected related tags (step S 130). That is, after calculating the association degree between each tag in step S120, the ranking of the association tags is further determined by further referring to similarity, timeliness, and distance based similarity among the tags. Alternatively, the tag may be filtered by a predetermined criterion.
For example, the higher the similarity between tags, the lower the degree of association can be considered. For example, if you compare 'Jeju Island Travel' and 'Jeju Travel', you can see that the similarity between the two tags is very high. In this case, since the search results for 'Jeju Island Travel' and 'Jeju Travel' will be almost the same, users want to get referenced words with related tags, something to do. Therefore, in the step of determining the ranking of the selected related tags (step S130), the higher the similarity between the tags is, the lower the degree of association is, and the priority thereof can be set later.
As another example, the priorities of associated tags may be determined by reflecting the timeliness. For example, the step of analyzing the tags included in the plurality of data and selecting the associated tags for each tag (operation S100) may be performed every day at a designated time (for example, at an early morning with less user access). At this time, the
For example, when the
In order to solve such problems, a
As another example, in the case of a specific tag, certain types of tags may be collectively excluded or their priorities lowered when selecting an associated tag. For example, if an association tag of the tag 'restaurant' is searched, most related tags such as 'Gangnam restaurant' and 'Bundang restaurant' may be tags related to the area name. However, for users, you may want to get associated tags such as 'stake restaurant', 'pasta restaurant', etc., which are different from regional restaurants. To do this, it is possible to exclude tags of the form of 'local name + restaurant' from related tags of 'restaurant'.
As another example, the tag may be filtered by a predetermined criterion.
First, tags that are abused in an improper way may be filtered to increase search ranking. For example, to increase search ranking, if a search query ranked above the real-time search word is entered as a tag (regardless of the content of the data), these tags may be filtered with an abusing tag.
Alternatively, the tags may be filtered against illegal tags or public affairs. For example, tags associated with gambling, drugs, etc. may be filtered with an abusing tag.
Alternatively, the tags related to the personal information may be filtered. For example, tags that are excessively invasive of privacy or infringe on an individual such as an entertainer, such as "Mr. Exposure" or "Mr. A" may be filtered by an abusing tag.
Through the method of providing data according to the present invention, in selecting an associated tag related to a specific tag and providing it to a user, a plurality of tags used by a plurality of users are selected as an associated tag and provided to the user, But it may also cause the user's interest. Also, through the data providing method of the present invention, the association tag is selected by reflecting the category of the tagged data, the similarity and the timeliness among the tags, and provided to the user, thereby providing the user with an associated tag To provide a data service that can be used to provide information.
The embodiments of the present invention described above can be embodied in the form of a computer program that can be executed on various components on a computer, and the computer program can be recorded on a computer-readable medium. At this time, the medium may be a magnetic medium such as a hard disk, a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM and DVD, a magneto-optical medium such as a floptical disk, , A RAM, a flash memory, and the like, which are specifically configured to store and execute program instructions. Further, the medium may include an intangible medium that is implemented in a form that can be transmitted over a network, and may be, for example, a medium in the form of software or an application that can be transmitted and distributed through a network.
Meanwhile, the computer program may be designed and configured specifically for the present invention or may be known and used by those skilled in the computer software field. Examples of computer programs may include machine language code such as those produced by a compiler, as well as high-level language code that may be executed by a computer using an interpreter or the like.
The specific acts described in the present invention are, by way of example, not intended to limit the scope of the invention in any way. For brevity of description, descriptions of conventional electronic configurations, control systems, software, and other functional aspects of such systems may be omitted. Also, the connections or connecting members of the lines between the components shown in the figures are illustrative of functional connections and / or physical or circuit connections, which may be replaced or additionally provided by a variety of functional connections, physical Connection, or circuit connections. Also, unless explicitly referred to as " essential ", " important ", etc., it may not be a necessary component for application of the present invention.
Accordingly, the spirit of the present invention should not be construed as being limited to the above-described embodiments, and all ranges that are equivalent to or equivalent to the claims of the present invention as well as the claims .
10: Data Delivery System
100: data providing device
110:
120:
130: Database
200: User terminal
300: Network
Claims (20)
Clustering the tags by author by the control unit;
Calculating a degree of association between each of the clusters by a control unit; And
And determining, by the control unit, a priority of associated tags for a specific tag based on the degree of association.
Clustering the tags for each author comprises:
Characterized in that the tags used by the plurality of authors together are selected as the associated tags.
Clustering the tags for each author comprises:
Calculating frequency of each tag by a control unit; And
And calculating the frequency of coexistence between tags by the control unit.
The step of calculating the frequency of each tag may include:
Characterized in that each tag is calculated by how many authors are included in the created data.
Wherein the frequency of all the tags included in the data is the same regardless of the number of tags included in the data created by the same author.
Wherein the step of calculating the co-
And calculating how many pieces of data are included in the two tags based on the frequency of each tag.
Wherein the step of calculating the degree of association between the clustering tags comprises:
Wherein the data is calculated based on the frequency of each tag and the frequency of coexistence between the tags in the two tags.
Wherein the step of calculating the degree of association between the clustering tags comprises:
Wherein the method is performed based on a normalized pointwise mutual information (NPMI) method.
Wherein the step of calculating the degree of association between the clustering tags comprises:
Wherein the degree of association is calculated for each category of data, and then the degree of association is calculated for each category.
Wherein the step of determining a priority of association tags for a specific tag based on the degree of association comprises:
Wherein the priority is lowered as the similarity between the specific tag and the association tag is higher.
Wherein the step of determining a priority of association tags for a specific tag based on the degree of association comprises:
Wherein the priorities of the associated tags are determined by merging the associations based on the data for the first period and the associations based on the data for the second period different from the first period.
Wherein the step of determining a priority of association tags for a specific tag based on the degree of association comprises:
And lowering the priorities of the specific types of tags collectively.
After the steps,
Receiving, by the communication unit, a data request signal requesting to provide data related to the first tag from the user terminal;
Selecting, by the control unit, first data associated with the first tag among the plurality of data;
Providing the selected first data to the user terminal by a communication unit; And
And providing at least one association tag for the first tag to the user terminal by a communication unit.
And a communication unit for providing the association tags to the user terminal,
Wherein,
Clustering tags for each author, computing a degree of association between each of the clustering tags, and determining a priority of associated tags for a specific tag based on the calculated degree of association.
Wherein,
In clustering tags by author,
And the frequency of tag occurrence and the frequency of simultaneous occurrence of tags.
Wherein,
And the frequency of each tag is calculated on the basis of whether or not each tag is included in data created by several authors.
Wherein the frequency of all the tags included in the data is the same regardless of the number of tags included in the data created by the same author.
Wherein,
And calculates the frequency of coexistence between tags based on how many data are included in the two tags based on the frequency of each tag.
And calculates the association degree between each of the two tags based on the frequency of each tag and the frequency of coexistence between the tags.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113343069A (en) * | 2021-06-10 | 2021-09-03 | 北京字节跳动网络技术有限公司 | User information processing method, device, medium and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20060066238A (en) | 2004-12-13 | 2006-06-16 | 한재준 | Service system and method for transmission confirmation of character message |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20060066238A (en) | 2004-12-13 | 2006-06-16 | 한재준 | Service system and method for transmission confirmation of character message |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113343069A (en) * | 2021-06-10 | 2021-09-03 | 北京字节跳动网络技术有限公司 | User information processing method, device, medium and electronic equipment |
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