KR20100006752A - Contents distributing system and method thereof - Google Patents

Abstract

PURPOSE: A content distribution system and a method thereof for the contents of high activity are provided to improve the contents efficiency of a social network service by combining a context around a keyword. CONSTITUTION: A contents distribution information generator(300) obtains network data about a personal media contents. The circulation information generating unit obtains the network action information of the contents. A context searcher(400) selects the contents or the individual media in which the contents belongs. The selected contents or the individual media is circulated.

Description

Contents distribution system and method

The present invention relates to a content distribution system and method, and in particular, in order to improve the content dissemination efficiency of social network services, by analyzing the network activity in combination with a context centered on keywords, the content to selectively present highly active content and users. A distribution system and method are disclosed.

In recent years, network activities that emphasize 'relationship' have come to the fore, with the emergence of a variety of communities that emphasize the rapid spread of personal media and contacts that record and collect personal posts and other necessary information through the Internet. The social network service (SNS) symbolized by the community and the one-person (individual) media field (blog, mini homepage) emphasized such 'relationship' is the people who want to meet their needs beyond the limits of time and space. It is an effective model for a world that can be connected with.

Personal media (social media) represented by blogs, mini homepages, myspace, etc. are basic social network services, and the contents are simple and the format is free, so various contents are mass produced easily and quickly to create vast information. . However, with him, personal media has the nature of isolation, so that distribution can use relationships between tracks (trackbacks, pingbacks, links, scraps, neighbors, comments, visitor records, etc.) or post content. This is done through a limited path using search based on. Here, the distribution of content refers to all actions of using and distributing the content through visits to the content, scraps (pingbacks), trackbacks, RSS, and the like.

Although social network providers offer additional personal media introductions, such as presenting personal media or content rankings found by many users through regular rules or simple network analysis, or introducing manually searched content, this is extremely limited. Because of this, the actual influence on the distribution of personal media is insignificant, and many personal media and high-quality contents generated by these personal media are not exposed to users who need them.

1 shows an example of a system provided by a social network service provider for content distribution activation in a blog service which is one of social network services.

As shown, by analyzing network activity such as visiting the blog 10 or explicitly establishing a connection to the blog or content (neighbors, trackbacks, pingbacks, RSS, etc.) It is provided with a tendency / ranking analysis unit 20 to determine the degree of attention (attention) about the ranking to expose the ranking (21) or present the tendency (22).

However, it simply lists blogs by comprehensive rankings based on explicit network activity, such as pageview rankings for blogs 10, exposure to external search portals, pingbacks or trackbacks, etc. The distribution of some blogs or contents belonging to the list may be expanded, but it is impossible to effectively provide contents to users with various interests or to activate the distribution of contents of numerous users with high quality contents.

In particular, the general access to a large number of contents is performed by searching, but such searching is performed by a variety of personal writing contents, and the reliability of the information is low, and the high-quality contents written by the users are lower than the low-quality contents. The content may be dead. In addition, even if a high-quality content is created, it is difficult to spread the content or access the high-quality content because there is no automatic distribution process that effectively spreads it to users or it is extremely incomplete.

Therefore, by effectively activating the distribution of contents of social network services in which distribution activities are made according to individual preferences of users, a new system that makes it easy for users to access high-quality contents and easily spread them when creating high-quality contents Or method is required.

As described above, an object of the present invention, which is newly proposed to effectively activate content distribution of a social network service, is to grasp the activity of each context for social network service content, and based on the context extracted according to the user's use of the social network service. The present invention provides a content distribution system and method that enables content distribution to be activated by recommending highly active posts or user information based on contextual activity identified by the context.

Another object of the embodiments of the present invention is to analyze a network activity area of content for a specific subject area, extract a user who has generated a good content and a user who serves to spread the content, and use the corresponding role information to extract content information for each subject area. It is to provide a content distribution system and method that can be utilized.

Another object of the embodiments of the present invention is to obtain content and user information with high network activity for a specific subject area, and to arrange and provide the content or user with high network activity to a user who shows interest in the subject area. It is to provide a content distribution system and method for increasing the accessibility to high-quality content and to enable rapid spread of the content.

Another object of the embodiments of the present invention is to expand the subject area through context extraction and expand the network activity analysis area to include implicit activities together with explicit activities when analyzing network activity. It is to provide a content distribution system and method that can increase the reliability of the network activity analysis for the subject area by determining the base set that is the base.

Another object of the embodiments of the present invention is to provide a content distribution system and method for analyzing the network activity of the content for each subject area and presenting the importance, role or value of the content or the personal media that posted the content.

In order to achieve the above object, the content distribution system according to an embodiment of the present invention obtains the network data for the personal media content of the social network service, the content area related to the context extracted from the content A content distribution information generation unit for obtaining network activity information of the content; Extracts user interest information according to the use of the social network service, selects a corresponding subject area, and selects and distributes content to be provided to the user or personal media to which the content belongs based on network activity information on the selected subject area. It includes a context search unit to make.

The content distribution information generation unit includes a subject area selection matching unit that selects a subject area having high similarity by comparing the predetermined subject areas with the context.

The content distribution information generation unit collects information about contents having a network relationship with the content of the personal media as network data, extracts a context from the corresponding content, and classifies network data reflecting the context into a base set. A modeling unit; The network data modeling unit may further include an activity network analyzer configured to obtain network activity information of the content by calculating network activity of the content set for the base set.

The network data modeling unit further collects network data on content in which an implicit network relationship is established, including visits and comments on content of the personal media.

The context retrieval unit may include a content distributor that exposes the content having the most network activity in the selected subject area or the personal media to which the content belongs to the user.

According to another embodiment of the present invention, a content distribution system extracts a keyword of personal media content to determine a subject area, and calculates an activity index of the content for the subject area based on contents that have a network relationship with the content. A distribution information generation unit; When registering or selecting personal media content or a search query occurs, the subject area of interest is determined by keyword extraction, and the content distribution information of the corresponding subject area is determined from the activity index information of the content of each subject area calculated by the content distribution information generation unit. It includes a content search unit for obtaining.

The content distribution information generation unit calculates the content activity index for the subject area into an authority index and a center index to correspond to the authority index and the quality of the content, and to correspond to the center index and the degree of content propagation to provide information on the role and level of the content. Create by subject area.

The content distribution method according to another embodiment of the present invention obtains network data about personal media content of a social network service, and obtains network activity information of the content for a subject area related to a context extracted from the content. Generating content distribution information; Extracts user interest information according to the use of the social network service, selects a corresponding subject area, and selects and distributes content to be provided to the user or personal media to which the content belongs based on network activity information on the selected subject area. And a context search step.

Content distribution method according to another embodiment of the present invention extracts the keywords of the personal media content to determine the subject area, and calculates the activity index of the content for the subject area based on the content that has a network relationship with the content Generating content distribution information; When registering or selecting personal media content or a search query occurs, the subject area of interest is determined by keyword extraction, and the content distribution information of the corresponding subject area is determined from the activity index information of the content of each subject area calculated in the content distribution information generation step. And a content searching step of obtaining the content.

Content distribution system and method according to an embodiment of the present invention is to determine the activity of each context for the social network service content, and based on the activity of each context based on the context previously extracted based on the user's use of the social network service. By recommending high posts or user information, the distribution of high-quality content is effective.

Content distribution system and method according to an embodiment of the present invention analyzes the network activity area of the content for a particular subject area to extract the user who has generated the content of good quality and the user that serves to spread the content and the corresponding information by subject area There is an effect that can be used as content diffusion information.

Content distribution system and method according to an embodiment of the present invention obtains the content and user information with high network activity for each specific subject area, and appropriately arrange the content or user with high network activity to the user who shows interest in the subject area By providing it, it has the effect of increasing the accessibility to high-quality content and enabling the rapid spread of the content.

Content distribution system and method according to an embodiment of the present invention expands and corresponds a subject area through context extraction, and expands network activity analysis area to include implicit activity together with explicit activity when analyzing network activity. Therefore, it is possible to increase the reliability of network activity analysis on the subject area corresponding to the context by determining a base set on which the analysis is based.

Content distribution system and method according to an embodiment of the present invention by analyzing the network activity of the content by subject area to present the importance, role or value of the content or the personal media that posted it to the user's interest and active social network services It has the effect of inducing participation.

The present invention as described above will be described in detail with reference to the accompanying drawings and embodiments. Although a blog is described as an example of a personal media of a social network service in the following, it is noted that this is a concept encompassing various community services including mini homepage (aka mini homepage), MySpace.

2 is a conceptual diagram illustrating a concept of a content distribution system according to an exemplary embodiment of the present invention, and shows an example for activating content distribution in a blog service which is a kind of personal media as shown.

First, based on the content of each blog, the tag used by the blog (personal media), or the extracted keywords, the main search terms used in the service (such as the top queries that generate most traffic) Selects a subject area using the selected search term (115), and analyzes the network activity of the blog 100 in which various mutual network activities are performed in content units or blog units to select a subject area closest to the blog or content. Afterwards, the activity network data of the blog or content is generated in the subject area (110). Here, the activity network data may include an index indicating the activity of the analyzed blog or content based on the blog or content related to analyzing the network activity on the blog or the content, and is required to calculate the activity index. All relevant information may be included. On the other hand, such activity index may include a number of indexes indicating the role or level of the blog or content, it is possible to determine what role (or location) the blog or content plays in social network services, You'll know the level (the index, that is, the index that allows you to rank your blog or content).

On the basis of the activity index, a list 118 of contents (posts, multimedia, etc.) whose activity / role is identified for each subject area and a blog (marked as an author or an operator) to which the contents belong can be obtained. The list may include link information and activity network data about the corresponding content or blog, and may include information on classification or ranking based on the activity network data. On the other hand, such information may be collected periodically or aperiodically. In this case, previously obtained vitality information may be updated or cumulatively updated, and various existing information reflecting updates for adjusting existing information to current information may be performed.

The process of selecting the subject area or obtaining the activity network data of the content / blog for each subject area may be performed regardless of the user's operation in the server of the social network service provider.

In this way, the content or blog activity network data is classified and obtained according to the subject area, so that specific content spreading information for various interests can be obtained and distributed according to various distribution methods as described below.

When the user uses the blog 100, the user visits the blog (including content check, comment, guestbook, etc.), posts the content on the blog (writes himself, pings back, trackbacks, etc.), or requests a search to search for content of interest. If you are looking for a representative. In this case, the social network service provider may extract the keyword from the blog or content visited by the user or extract the keyword from the search query 120 to grasp the user's interest information. The keyword extraction may use various contextual search methods and may use content features such as tags.

The subject area may be selected by determining the similarity with the selected subject area by using the obtained keywords. In order to perform this more effectively, the subject area may be based on a context in which the keyword is extended to keywords similar to the extracted keyword. The context matching 125 may be performed to determine the subject area by checking the similarity with. For this purpose, a thesaurus database may be used to store similar keywords. Here, the context means a set of similar keywords.

After selecting the blog or the subject area to which the contents belonging to the blog through context matching, the contents of the activity or the role identified by the subject area and the contents of the blog or the contents of the blog which are prominent in the network activity in the selected subject area The content distribution may be activated by selecting 130 from the list 118 for the blog (marked as the creator or the operator) and presenting it to the user.

Such content distribution may be to present an authoritative content or blog in the subject area, or to present a content or blog having a lot of links to the related content, thereby expanding the distribution of the content. In addition, the content distribution may be to provide a high-quality content as needed to provide a distinctive network activity of the topic area or the role of the blog. In addition, the content distribution may be to present the rank of the content or blog for the subject area to the user, through which the high-quality content can be selected and utilized for each subject area.

That is, a number of contents for each region of interest may be distributed by exposing information about contents to be distributed for each region of interest of the user through reliable analysis.

On the other hand, this distribution of content induces network activity of users (neighboring requests between blogs, RSS settings, comments, trackbacks, pingbacks, etc.) to reliably expand the network activity area, thereby increasing overall network activity of social network services. As such, content distribution can be expected.

3 is a block diagram of a content distribution system according to an embodiment of the present invention. As shown in FIG. 3, a configuration for generating content distribution information using a blog service as an example and a configuration for effectively utilizing the content distribution information generated as described above will be described. do.

This section describes how to use blog content as a unit, but note that this can be extended to a blog and performed on the blog.

First, in a blog service, which is a personal media of a social network service, network activity information of the content of a subject area determined as the context is determined by using network data of the content of the blog 200 and a context extracted from the content. The content distribution information generation unit 300 and the user interest information obtained by using the social network service are extracted as a context, and a subject area corresponding thereto is selected, and the user is based on network activity information on the selected subject area. The context search unit 400 selects and distributes the content to be provided to the user or the personal media to which the content belongs.

Here, the content distribution information generation unit 300 or the context search unit 400 need a means for extracting a context from the contents of the blog and selecting a topic area having a high similarity compared to preset topic areas. The means for selecting the subject area preferably includes a context extractor which extracts a context by expanding tag information of the content or a keyword extracted from the content into a pseudo keyword region, each of which has a network data modeling unit 310. ) And the keyword extraction unit 410.

Meanwhile, the context extracting unit configured in the content distribution information generating unit 300 or the context searching unit 400 calculates keyword similarity using tag information about the content and keyword information extracted from the content, and calculates keyword similarity. The similarity calculation range may be extended to a similar keyword region to extract a context including the similar keyword for the core keyword.

That is, in the configuration according to the embodiment of the present invention, the process of dividing the blog or the contents of the blog into the subject areas is performed when generating content distribution information and context search, respectively, and is simply extracted from the content. Since the keyword may not be matched with an appropriate subject area, it is desirable to extend the keyword to a context so that most content may match the preset subject area.

On the other hand, the content distribution information generation unit 300 collects the information about the content of the network relationship is set to the network data, extracts the context from the content to classify the network data reflecting the context into a base set A network data modeling unit 310 and an activity network analyzing unit 320 for calculating network activity of the content for the base set classified by the network data modeling unit 310 to obtain network activity information of the content. can do. Here, in order to select the subject area of the content to be modeled, the network data modeling unit 310 may utilize a keyword database 301 in which a keyword corresponding to the subject area is stored and a relational database 302 for keyword expansion.

The network data modeling unit 310 selects a base set for calculating an actual network activity index from the network relationship with respect to the content. When the base set is effectively selected, more realistic network activity information can be obtained. In particular, in the present embodiment, the network activity index is not obtained using only the network activity of the content, but the network activity related to the subject area is selected after considering the subject area to which the content belongs, and the network activity of the content of the corresponding subject area. Obtain an index to identify network activity characteristics of more reliable content.

In addition, as illustrated, the subject area 305 may be set by clustering keywords of the keyword database 301, and the result of the keyword clustered result (topic area) 305 may be continuously maintained and managed.

4 to 5 illustrate the contents of the blog and the network activity of the blog, and FIG. 4 illustrates the explicit network relationship (solid line) and the implicit network relationship (dotted line) of the content. For example, although the network relationship for a specific content 511 is generally analyzed based on an explicit relationship, trackback or pingback, in this embodiment, in order to extend this network relationship as much as possible, similarities, visits, comments, etc. of tags may be used. Extend the same implicit network relationships to network relationships. The network relationship having the same subject area as the corresponding content 511 among the expanded network relationships is preferentially selected to obtain information about the network relationship of the corresponding content 511. Therefore, in the illustrated case, the content 501 having an explicit network relationship and the content 502, 521 having an implicit network relationship may be identified as a group having a network relationship with the corresponding content 511.

On the other hand, it is further extended to identify all the contents of the blog having the contents 501, 502, and 521 related to the contents 511, and to identify contents having high similarity with the contents 511, even though the direct network Even if the relationship is not established, the content may be incorporated into an implicit network having a network relationship. In addition, the tag information of the content 511 may be used. Since such a tag becomes a main keyword for identifying the subject area of the corresponding content 511, the tag information is expanded and compared with similar keywords. You can also extend content incorporated into network relationships.

5 shows an explicit network relationship (solid line) and an implicit network relationship (dashed line) of a blog including content. Understanding network relationships for blogs also extends network relationships to implicit network relationships such as trackbacks, pingbacks, visits, and comments, as well as explicit network relationships such as adding neighbors or registering RSS. For example, in the case of a specific blog 603, blogs 602 and 605 that are explicit network relationships and blogs 601 and 604 that are implicit network relationships are also identified as a group having a network relationship. In this extended network relationship, blogs having similar subject areas similar to those of the specific blog 603 may be selected first to obtain information about the network relationship of the blog 603.

6 is a diagram illustrating a process of selecting a base set by the network data modeling unit 310 of FIG. 3. The network data modeling unit 310 substantially calculates a network activity index of a specific content or blog by using the network relationships of FIGS. 4 and 5. It shows how to select the base set which is network data.

First, a network relation for a specific content is obtained for an explicit content network 710 and an implicit content network 711 (P1), and then integrated to obtain an integrated content network 720 (P2). In operation 720, the content having a keyword (or a tag) corresponding to the context extracted from the specific content is selected as the root content network 730, and then the content 731 pointed to or pointing to the root content network 730 is selected. In addition, a base set 735 is obtained (P3).

Meanwhile, in the case of a blog, an explicit blog network 740 and an implicit blog network 741 for a specific blog may be obtained (B1), and an integrated blog network 750 may be obtained (B2). Here, the implicit blog network may be the same as the integrated content network 720 since the implicit blog network substantially means a network for internal content of the blog. In addition, when the integrated blog network 750 selects a blog having a keyword corresponding to the context extracted from the specific blog, the blog network 760 including the context may be obtained (B3). This can be used when providing the relevant blog group to the user, and can be used for various target services.

The activity network data can be obtained by calculating the network activity index by analyzing the base set thus obtained. In the case of the activity network analysis unit 320 illustrated in FIG. 3, the authority index and the center index for each content of the base set are obtained. A calculation section 321 for each subject area that calculates a ranking of the content for each subject area, and network data of the network data modeling unit 310 for a subgroup (base set 735, root) for the content of the personal media. A personal-based subgroup extraction unit 322 divided into a network 730, an integrated content network 720, an integrated blog network 750, a blog network 760 including a context, and the like, and respective contents of the base set. It includes a role extraction unit 323 for each subject area for calculating the authority index and the central index for determining the role of the content for each subject area. It is done by Of course, only some of them may be configured, or may further include arithmetic units for obtaining arithmetic information to be provided according to the type of service by variously modifying the information obtained through them.

Meanwhile, the activity network analyzer 320 may further include a content distribution information storage unit for each subject area that accumulatively stores the corresponding content and the personal media information to which the content belongs, together with the network activity information of the content of each subject area. As an example, the active posts / author list 325 for each subject area may be configured to accumulate and store a list of highly active posts (content) and authors (blogs) for each subject area.

Meanwhile, the content distribution information storage unit for each subject area calculates the obtained content activity index into the authority index and the center index to correspond to the authority index and the quality of the content, and the center index and the degree of content propagation to provide information on the role and level of the content. May be generated for each subject area and stored in the content distribution database 330 for each subject area. In addition, the content distribution information storage unit for each subject area may store the activity network data (network activity index, role, information for calculating the link and a source, etc.) as it is or in order according to the ranking of the content distribution database for each subject region 330. You can also save to).

 On the other hand, the storage and update of such information may be performed by the content distribution information storage unit for each subject area, and the update method may be applied in various ways such as alternative storage, cumulative storage, and reference of previous information.

Herein, the authority index and the center index are network activity indexes obtained based on the base set. When there are many contents (left q1 to q3) indicating a specific content P as shown in FIG. Since the content (P) is of good quality and its author is authoritative, this state is calculated by the authority index, and when the specific content (P) indicates multiple contents (right q1 to q3), the corresponding content (P) Since it is located at the center of content diffusion, we calculate this state as the center index. The authority index and the central index are mutually strengthened, and the content indicated by the content with the high central index is high in the authority index and vice versa. Here, not only the content but also the blog may be calculated in the same way.

If this is expressed as a simple equation, the authority index is as shown in Equation 1, and the center index is as shown in Equation 2. Here, the authority index (x <p>) is the center index of the backlinks, and the center index (y <p>) means the sum of the authority indexes of the forward links.

Here, p is a target node (content, blog), q is a backlink node, and they are included in the base set (E).

Here, p is a target node (content, blog), q is a forward link node, and these are included in the base set (E).

That is, the base set 800 shown on the left side of FIG. 8 is the sum of the root network 810 and the networks pointed to or indicated by the root network. When analyzing this, each content or blog as shown on the right is a node. It plays a role of spreading information 821 or providing information to be spread 822. The roles can be divided into information spreaders and information authorities according to these roles. Of course, in addition to these roles, a variety of roles can be distinguished, such as selecting nodes that link groups with active network activities, or selecting nodes with high network activity indices for other subject areas but low activity indices for those subject areas. .

As described above, the content distribution information is generated, and the context search unit should be configured to effectively provide the content distribution information obtained to the social network service user to activate the content distribution.

The context search unit 400 illustrated in FIG. 3 may include a keyword extractor 410 extracting a keyword from content or a search word of the blog 200, preset subject areas, and the keyword extractor 410. A context matching unit 420 which selects a topic area having the highest similarity by comparing contexts in which keywords are expanded, and content to be provided to the user or personal media to which the content belongs, based on network activity information on the selected subject area; The content distribution unit 430 to select and distribute.

The content distributor 430 exposes the content having the most network activity in the selected subject area or the blog to which the content belongs to the user, or authorizes the content according to the network activity information of the content for the selected subject area. B ranking information may be exposed to the user, or authority or ranking information for each role of content may be processed and exposed to the user. In other words, you can expose authoritative documents or authors (blogs) using the Network Activity Index, or expose interest-based network value rankings and role rankings when navigating, searching, and posting content using tags. Additional information about the ranked keywords may also be provided.

Therefore, through such a content distribution process, high-quality content can be frequently exposed to more users, and content or blogs that spread high-quality content can also be frequently exposed, thereby promoting the spread of content. Establishing a network relationship can increase the network activity of more users and promote content diffusion.

In the above described and illustrated with respect to preferred embodiments according to the present invention. However, the present invention is not limited to the above-described embodiment, and various modifications can be made by those skilled in the art without departing from the gist of the present invention attached to the claims. .

1 is a content spreading service through conventional network activity analysis.

2 is a conceptual diagram illustrating a content distribution method according to an embodiment of the present invention.

3 is a block diagram of a content distribution system according to an embodiment of the present invention.

4 is an exemplary diagram of a content network relationship according to an embodiment of the present invention.

5 is an exemplary diagram of a blog network relationship according to an embodiment of the present invention.

6 is an exemplary view showing a context-based network data selection method according to an embodiment of the present invention.

7 is a network relationship diagram for explaining a network activity index.

8 is a network relationship diagram for explaining a network role according to a network activity index.

** Description of symbols for the main parts of the drawing **

300: content distribution information generation unit 310: network data modeling unit

320: activity network analysis unit 330: content distribution DB by subject area

400: content distribution department

Claims (38)

A content distribution information generation unit for acquiring network data of personal media content of a social network service, and obtaining network activity information of the content for a subject area related to a context extracted from the content; Extracts user interest information according to the use of the social network service, selects a corresponding subject area, and selects and distributes content to be provided to the user or personal media to which the content belongs based on network activity information on the selected subject area. Content distribution system comprising a context search unit for. The content distribution system of claim 1, wherein the content distribution information generation unit or the context search unit includes a subject area selection matching unit configured to select a subject area having high similarity by comparing the predetermined subject areas with the context. The content distribution system of claim 2, wherein the subject region selection matching unit comprises a context extraction unit configured to extract a context by extending tag information of the content or a keyword extracted from the content into a similar keyword region. The method of claim 1, wherein the content distribution information generation unit or the context search unit calculates keyword similarity using tag information about the content and keyword information extracted from the content, and sets a similarity calculation range of the keyword as a similar keyword region. And a context extracting unit configured to expand and extract a context including a similar keyword for the core keyword. The method of claim 1, wherein the content distribution information generation unit A network data modeling unit configured to collect information about contents in which a network relation is established with the contents of the personal media as network data, extract a context from the contents, and classify the network data reflecting the context into a base set; And an activity network analyzer for calculating network activity of the content with respect to the base set classified by the network data modeling unit to obtain network activity information of the content. The content distribution system of claim 5, wherein the network data modeling unit further collects network data on content in which an implicit network relationship is established, including visits and comments on content of the personal media. The system of claim 5, wherein the network data modeling unit further collects, as network data, information about personal media in which a network relationship is established with respect to personal media including the content. The content distribution system of claim 5, wherein the activity network analysis unit further comprises a ranking operation unit for each subject area that calculates an authority index and a central index for each content of the base set to obtain a ranking of the content of each subject area. system. The content distribution system of claim 5, wherein the activity network analyzer further comprises a person-based subgroup extraction unit that divides the network data of the network data modeling unit into subgroups of the content of the personal media. The content distribution system of claim 9, wherein the context retrieval unit comprises a content distributor that exposes subgroup information about the selected subject area to the user. 6. The contents of claim 5, wherein the activity network analyzer further includes a role extractor for each subject area that calculates a role of content for each subject area by calculating an authority index and a center index for each content of the base set. Distribution system. The content distribution system of claim 5, wherein the activity network analyzer further includes a content distribution information storage unit for each subject area that accumulates and stores the corresponding content and personal media information to which the content belongs, together with network activity information of the content of each subject area. Distribution system. The content distribution system of claim 1, wherein the context search unit comprises a content distributor that exposes the content having the most network activity in the selected subject area or the personal media to which the content belongs to the user. The content distribution system of claim 1, wherein the context search unit comprises a content distribution unit configured to expose authority or ranking information of content according to network activity information of the content of the selected subject area to the user. The content distribution system of claim 1, wherein the context search unit comprises a content distribution unit configured to process authority and rank information for each role of the content according to network activity information of the content of the selected subject area and expose the same to the user. . A content distribution information generation unit for extracting a keyword of personal media content to determine a subject area, and calculating an activity index of the content for the subject area based on contents in a network relationship with the content; When registering or selecting personal media content or a search query occurs, the subject area of interest is determined by keyword extraction, and the content distribution information of the corresponding subject area is determined from the activity index information of the content of each subject area calculated by the content distribution information generation unit. Content distribution system comprising a content search unit for obtaining a. The method according to claim 16, wherein the content distribution information generation unit calculates the content activity index for the subject area into an authority index and a center index to correspond to the authority index and the quality of the content and to correspond to the center index and the degree of content propagation. Content distribution system, characterized in that for generating information on the level for each subject area. The content distribution system of claim 17, wherein the content vitality index is adjusted with reference to an existing calculation value after the calculation. The content distribution system according to claim 16, wherein the content distribution information obtained by the content search unit is information on the role and level of content for each subject area, and the content ranking information for each role for the subject area of interest. A content distribution information generation step of acquiring network data of personal media content of a social network service, and acquiring network activity information of the content of a subject area related to a context extracted from the content; Extracts user interest information according to the use of the social network service, selects a corresponding subject area, and selects and distributes content to be provided to the user or personal media to which the content belongs based on network activity information on the selected subject area. And a context retrieval step. 21. The method of claim 20, wherein the content distribution information generation step or context search step further includes a subject area selection matching step of selecting a subject area with high similarity by comparing the predetermined subject areas with the context. Way. 22. The method of claim 21, wherein the subject region selection matching step includes extracting a context by extracting a context by extending tag information of the content or a keyword extracted from the content to a similar keyword region. The method of claim 20, wherein the generating of the content distribution information or the context retrieval step includes calculating keyword similarity using tag information of the content and keyword information extracted from the content, and converting the similarity calculation range of the keyword into a similar keyword region. And a context extraction step of expanding and extracting a context including a similar keyword for the core keyword. The method of claim 20, wherein the content distribution information generating step A network data modeling step of collecting information on contents in which a network relation is established with the content of the personal media as network data, extracting a context from the contents, and classifying the network data reflecting the context into a base set; And an activity network analysis step of obtaining network activity information of the content by calculating network activity of the content for the base set classified in the network data modeling step. 25. The method of claim 24, wherein the network data modeling step further comprises collecting network data on content for which an implicit network relationship is established, including visits and comments on content of the personal media. . 25. The method of claim 24, wherein the network data modeling step further includes collecting, as network data, information about personal media having a network relationship with respect to the personal media including the content. The method of claim 24, wherein the analyzing of the activity network further comprises a ranking operation for each subject area that calculates the authority index and the center index for each content of the base set to obtain a ranking of the content for each subject area. How to distribute content. 25. The method of claim 24, wherein analyzing the active network further comprises extracting a person-based subgroup that divides the network data of the network data modeling unit into subgroups of the content of the personal media. 29. The method of claim 28, wherein the context retrieving step includes a content distribution step of exposing subgroup information about the selected subject area to the user. The method of claim 24, wherein the analyzing of the activity network further comprises: extracting a role for each subject area to determine a role of content for each subject area by calculating an authority index and a center index for each content of the base set. How to distribute content. The method of claim 24, wherein the analyzing of the activity network further comprises: storing content distribution information for each subject area, accumulating and storing the corresponding content and personal media information to which the content belongs, together with network activity information of the content for each subject area. How to distribute content. 21. The method of claim 20, wherein the context retrieval step includes a content distribution step of exposing the user with the most network activity in the selected subject area or the personal media to which the content belongs to the user. 21. The method of claim 20, wherein the context retrieval step includes a content distribution step of exposing the authority or rank information of the content according to the network activity information of the content for the selected subject area to the user. 21. The method of claim 20, wherein the context retrieval step includes a content distribution step of processing authority and ranking information for each role of the content according to the network activity information of the content for the selected subject area and exposing it to the user. Distribution method. A content distribution information generation step of extracting a keyword of personal media content to determine a subject area, and calculating an activity index of the content for the subject area based on contents in a network relationship with the content; When registering or selecting personal media content or a search query occurs, the subject area of interest is determined by keyword extraction, and content distribution information of the corresponding subject area is determined from the activity index information of the content of each subject area calculated in the content distribution information generation step. Content distribution method comprising the step of obtaining a content. The method of claim 35, wherein the generating of the content distribution information comprises calculating a content activity index for the subject area by using an authority index and a center index to correspond to the authority index and the quality of the content and to correspond to the center index and the degree of content propagation. Content distribution method, characterized in that for generating information on the level and level for each subject area. 37. The method of claim 36, wherein the content vitality index is adjusted with reference to an existing calculation value after the calculation. The method of claim 35, wherein the content distribution information obtained in the content searching step is information on the role and level of content for each subject area, and the content ranking information for each role for the subject area of interest.

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