WO2008066261A1

WO2008066261A1 - Category-based advertising system and method

Info

Publication number: WO2008066261A1
Application number: PCT/KR2007/005617
Authority: WO
Inventors: Ki Hyun Hwang
Original assignee: Opms Co., Ltd.
Priority date: 2006-11-28
Filing date: 2007-11-08
Publication date: 2008-06-05
Also published as: KR100863990B1; CN101196923A; TW200832268A; KR20080048149A

Abstract

A category-based advertising system is disclosed. An analysis module extracts one or more web documents including a main keyword from a plurality of web documents. A keyword extraction module extracts one or more words included in the extracted web documents. A weight calculation module calculates the weight of each of the extracted words with respect to a corresponding category based on the exposure characteristics of the word on the web documents including the main keyword of the corresponding category. A category matching module sets one or more words as associated keywords, and generates one or more category sets with which associated keywords and weights thereof are associated. An advertisement matching module determines the category of each of the corresponding web documents using words included in the corresponding web document and the category sets, and matches the advertisement of an advertiser to the corresponding web documents.

Description

Description CATEGORY-BASED ADVERTISING SYSTEM AND METHOD

Technical Field

[1] The present invention relates, in general, to a category-based advertising system and method, and, more particularly, to a category-based advertising system and method for associating content corresponding to the category of interest of an advertiser with an advertisement. Background Art

[2] There are many cases in which documents such as news items, blogs, and search results pages, which are provided on websites (hereinafter referred to as "web documents"), frequently include advertisements. Advertisements, included in web documents, generally correspond to keywords included in the web documents and are displayed on that basis.

[3] For example, if an advertiser registers a word "bag" and pays costs (advertising cost s) for the corresponding word, the advertisement of the advertiser is combined with web documents that include the word "bag", and the corresponding advertisement is exposed to Internet users who view the corresponding web documents.

[4] However, in this method, since an advertisement is matched to a specific word, there is a problem in that there are many cases in which appropriate advertisement matching cannot be performed. For example, in the case in which a word "handbag" is included in a web document but a word "bag" is not included, the advertisement of the advertiser who registers the word "bag" is not combined with the corresponding web document.

[5] Further, in the case in which a word registered by an advertiser is included in the content of a web document but the overall content of the corresponding web document is inappropriate, it is preferable that the advertisement not be combined therewith. For example, as shown in FIG. 1, in the case in which the word "bag" 51 is included in the news story "a murderer abandoned a corpse in a bag" 50, it is preferable that the advertisement (bag advertisement) 52 of the advertiser not be matched with the news story and not be provided. However, when advertising is performed by simply matching words, as in the prior art, such a problem often occurs.

[6] In order to solve this problem, Korean Unexamined Patent Publication No.

10-2005-0058172 discloses an online advertising system and method using an exclusive keyword such that an advertisement is not combined with content which includes an exclusive keyword having a negative meaning. In Korean Unexamined Patent Publication No. 10-2005-0058172, the above-described problem is solved by preventing the advertisement of an advertiser from being combined with content which includes an exclusive keyword. Further, in Korean Unexamined Patent Publication No. 10-2005-0058172, content and the advertisement of an advertiser are categorized, and the advertisement is permitted to be combined with the corresponding content only when the categories of the content and the advertisement match each other.

[7] For example, the news story "a murderer abandoned a corpse in a bag" is included in a category 'society/culture/news', and the advertisement of the advertiser is included in a category 'shopping/fashion/bag', so that the advertisement is prevented from being combined with news stories in a category which has no relationship with the advertisement.

[8] Here, categories are set by an advertisement broker and are hierarchically divided, and an advertiser selects one of the categories. However, there may be a plurality of web documents which are preferable to be matched to the advertisement of an advertiser, even though the web documents are included in categories different from the category selected by the advertiser. Korean Unexamined Patent Publication No. 10-2005-0058172 has a problem in that the advertisement is prevented from being combined with those web documents because the web documents are included in different categories.

[9] The case in which the category selected by the advertiser is the category

'shopping/fashion/bag' and web documents are included in the category 'social/cultural/news' or the category 'social/cultural/movie' is taken as an example in the following description. If the content of the web documents directly relates to a bag, for example, the content of news relates to the trend of bags or a bag is important in the content of a movie, it is preferable that the advertisement of the advertiser be combined with the web documents. However, Korean Unexamined Patent Publication No. 10-2005-0058172 has a problem in that the advertisement is prevented from being combined with those web documents because the category selected by the advertiser is different from the category of the web documents.

[10] Further, a plurality of newly coined words has appeared on the Internet. However, according to conventional methods, advertisements cannot be combined with web documents on the basis of those words. Disclosure of Invention Technical Problem

[11] Accordingly, the present invention has been made keeping in mind the above problems occurring in the prior art, and an object of the present invention is to provide a category-based advertising system and method for analyzing documents (or content) provided on websites, analyzing categories and keywords associated with categories, and matching documents, including corresponding associated keywords, to an advertisement, thereby maximizing the advertisement effect.

[12] Another object of the present invention is to provide a category -based advertising system and method for additionally finding new associated keywords from documents, including associated keywords corresponding to a specific category, and associating the newly found associated keywords with the advertisement of an advertiser.

[13] A further object of the present invention is to provide a category-based advertising system and method for setting a plurality of words for a single category, and enabling an advertiser to select one category, thereby increasing the opportunities of advertisement exposure for the advertiser.

[14] Yet another object of the present invention is to provide a category-based advertising system and method for combining online content with advertisements which have a close relationship therewith, and providing the online content to users. Technical Solution

[15] In order to accomplish the above objects, the present invention provides category- based advertising system, including an analysis module for extracting one or more web documents, each including a main keyword which is representative of each category, from a plurality of web documents; a keyword extraction module for extracting one or more words included in the extracted web documents; a weight calculation module for calculating the weight of each of the extracted words with respect to a corresponding category based on the exposure characteristics of the word on the web documents, each including the main keyword of the corresponding category; a category matching module for setting one or more words, meeting a predetermined criterion in each of the corresponding categories, as associated keywords for the category, and generating one or more category sets with which associated keywords and weights thereof are associated; and an advertisement matching module for determining the category of each of the web documents using words included in the web document and the category sets, and matching the advertisement of an advertiser registered for the category to the corresponding web documents.

[16] Further, a category-based advertising method, includes extracting one or more web documents including a main keyword which is representative of each category, from a plurality of documents; extracting one or more words included in the extracted documents; calculating the weight of each of the extracted words based on the exposure characteristics of the words on the web documents including the main keyword of the corresponding category; setting one or more words, meeting a predetermined criterion in each of the corresponding categories, as associated keywords, and generating one or more category sets with which associated keywords and weights thereof are associated; and determining the category of each of the corresponding web documents using words included in each web document and the category sets, and matching an advertisement of an advertiser registered for the determined category to the corresponding web documents.

[17] The exposure characteristics include an average exposure frequency in which a word is exposed per document, and an exposure concentration degree calculated using a ratio of a number of documents, in which the word is exposed, to a total number of documents. Further, when the exposure concentration degree is calculated, the weight calculation module assigns a higher weight to a word included in a web document extracted from a site that has a higher weight with respect to each of the categories. Furthermore, when the exposure concentration degree is calculated, the weight calculation module assigns a different weight to each of the words based on a location of the word. The location of a word is any of a title portion, a body portion, and a background material portion of a document, and the weight of the location decreases in order of the title portion, the body portion, and the background material portion.

[18] The each of the associated keywords, which meets a predetermined criterion, has a weight with a rank equal to or higher than a predetermined rank in the corresponding category sets. Further, preferably, a unique word, found substantially in only one category, be set as an associated keyword with respect to the corresponding category, and have a highest weight.

[19] In the present invention, the category sets are updated periodically or whenever a new web document is added.

[20] According to the present invention, when the documents of a specific category are extracted, documents of the corresponding category, which do not include a main keyword but include associated keywords meeting predetermined reference, may be extracted. That is, documents each including associated keywords, ranked in predetermined order on a weight, may be extracted, and documents each including unique words, substantially found in one category, may be extracted. Brief Description of the Drawings

[21] The above and other objects, features and other advantages of the present invention will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

[22] FIG. 1 is a view showing an example of a conventional keyword advertisement;

[23] FIG. 2 is a view conceptually showing a category-based method according to the present invention;

[24] FIG. 3 is a block diagram showing a category-based advertising system according to an embodiment of the present invention; [25] FIG. 4 is a view conceptually showing a category-based advertising method in the case in which two or more categories are matched to a single word;

[26] FIG. 5 is a view showing a method of calculating a weight for a single word included in two or more categories; and

[27] FIG. 6 is a view showing an example assigning different weights to a word according to the location of the word in a document. Best Mode for Carrying Out the Invention

[28] Reference should now be made to the drawings, in which the same reference numerals are used throughout the different drawings to designate the same or similar components.

[29] FIG. 2 is a view showing the relationship between categories and associated keywords according to the present invention.

[30] In the shown categories, the category 'diet' includes associated keywords, such as

'nice figure' and 'hourglass figure', and the category 'car insurance' includes associated keywords, such as 'traffic accident', 'auto repair center', and 'tow'. A category set is a set of one or more associated keywords, and a main keyword, which is representative of associated keywords included in a single category, generally coincides with the name of a category. That is, the main keyword of the category 'diet' is 'diet,' which is the same as the name of the category.

[31] Based on such a category structure, an advertiser selects the category that will be associated with an advertiser's own advertisement with reference to a plurality of categories and associated keywords included in each of the categories.

[32] In a category-based advertising method according to the present invention, one or more web documents are classified into specific categories using category sets based at least on words included in corresponding documents, and the advertisements of advertisers, associated with the respective categories, are combined with corresponding documents. When an Internet user requests a document with which the advertisement is combined, an advertisement, combined with the corresponding document, is exposed to the user.

[33] Further, according to the present invention, each of the category sets is periodically or non-periodically updated. For example, whenever web documents are newly registered, or periodically, existing or new web documents are analyzed, associated keywords are extracted from the corresponding web documents, and additional associated keywords exposed along with the existing associated keywords are searched for based on the existing associated keywords and are added to the corresponding category sets. Therefore, associated keywords, newly generated according to the tendency of language used on the Internet, are continuously collected using an analyzing method having such a cyclic structure.

[34] Accordingly, since a plurality of words, including words which are starting to be newly used in a specific category, corresponds to associated keywords in a category selected by an advertiser, the number of advertisement exposures increases, thereby increasing the advertisement effect. Further, since all of the keywords included in a corresponding category are matched to a corresponding advertisement, a user can view an advertisement that has a high relationship with the content of a document selected by a user.

[35] FIG. 3 is a block diagram conceptually showing a category-based advertising system according to an embodiment of the present invention.

[36] A category-based advertising system 100 according to the present invention includes an analysis module 110, a keyword extraction module 120, a weight calculation module 130, a category matching module 140, a database 150, and an advertisement matching module 160.

[37] The analysis module 110 performs analysis on web documents using main keywords of respective categories. First, the analysis module 110 fetches one or more web documents, each including a main keyword which is representative of a category, from among a plurality of web documents. For example, in the case in which analysis is performed with respect to the category 'diet', web documents, each including the word 'diet', that is, a main keyword which is representative of the corresponding category, are fetched from predetermined websites.

[38] Meanwhile, it is possible to further extract documents which do not include a main keyword but include associated keywords, which were ranked in predetermined order according to importance or weight, or unique words, which will be described later, among associated keywords included in the corresponding category, in addition to documents including the main keyword.

[39] With regard to each category, the keyword extraction module 120 applies a morpheme analysis method to the web documents extracted by the analysis module 110, and extracts one or more words included in each of the web documents. The words extracted by the keyword extraction module 120 are provided to the weight calculation module 130.

[40] The weight calculation module 130 calculates the weight of each of the words extracted by the keyword extraction module 120 with respect to a corresponding category with reference to the frequency of exposure of each of the extracted words in a document, the exposure concentration degree, and the number of documents in which the corresponding word is exposed.

[41] The weight calculation module 130 may include an exposure concentration degree calculation module 131, a site weight calculation module 132, a trust weight calculation module 133, a location weight calculation module 134, a first weight calculation module 135, and a second weight calculation module 136.

[42] The exposure concentration degree calculation module 131 calculates the weight of each of the exposed words based on the exposure frequency (the number of exposures) of the word and the number of documents in which the word is exposed.

[43] With respect to a single category, the exposure concentration degree increases when the number of documents in which a corresponding word is exposed is small but the exposure frequency of the word is high. Otherwise, the exposure concentration degree decreases. This is because, in the case in which a word which is frequently exposed only in a specific field, the number of documents in which the word is exposed is small but the frequency of the word in the exposed documents increases. In the case in which the number of documents in which a word is exposed is large and the frequency of the exposed word in the respective documents is high, the corresponding word can be determined to be a general word. For example, in the category 'dental service', words, such as 'doctor', 'hospital', and 'nurse', are frequently exposed and the number of documents in which the words are exposed is large, so that such words can be determined to be general words which are widely used in the medical field. In contrast, although the number of documents in which words, such as 'implant' and 'decayed tooth', are exposed is relatively small, the words are frequently exposed in the category 'dental service'. Therefore, there is a strong possibility that the words, such as 'implant' and 'decayed tooth', are the associated keywords of the category 'dental service'. Therefore, these words have larger weight.

[44] As described above, the frequency at which a word is concentratedly exposed in a limited number of documents is called an 'exposure concentration degree'. A higher weight is assigned in proportion to the exposure degree. The weight, the value of which increases in proportion to the increase in the exposure concentration degree, may be calculated using, for example, the following equation:

[45] weight = N log(TF/(iDF + I)),

[46] where 'N' indicates the average exposure frequency per document, 'TF' indicates the number of documents to be analyzed, and 'iDF' indicates the number of documents in which a given word is found.

[47] If it is assumed that the number of documents analyzed by the analysis module 110 with respect to a first category is 10 millions, the word 'hourglass figure' is found in 10 thousand documents among a total of 10 million documents which are analyzed, and the word 'hourglass figure' is used an average of five times in each document, the weight of the word 'hourglass figure' can be calculated as follows:

[48] 5 log (10,000,000 / (10,000 + I)) = 2.49

[49] If it is assumed that the number of documents analyzed by the analysis module 110 with respect to the first category is 10 million, the word 'weight' is found in 3 million documents among a total of 10 million documents which are analyzed, and the word 'weight' is used an average of ten times in each document, the weight of the word 'weight' can be calculated as follows:

[50] 10 log (10,000,000 / (3,000,000 + I)) = 2.30

[51] As described above, the weight calculating method according to the present invention does not assign a high weight to a word simply because the word has a high exposure frequency in a plurality of documents. According to the present invention, a word con- centratedly exposed in a limited number of documents has a higher weight in a category.

[52] The site weight calculation module 132 increases/decreases the weight of each word based on the sources (website) of documents including the corresponding word. For this purpose, with regard to each of the websites, the site weight calculation module 132 calculates the weight of each of the websites with respect to a specific category based on the degree of the web documents in a specific category, among the web documents of the corresponding website. Thereafter, with regard to each of the words, the site weight calculation module 132 incorporates the site weight of the website, including the web documents having the corresponding word, in the corresponding word.

[53] The site weight can be incorporated by increasing/decreasing the exposure frequency for the word exposed in the specific website. For example, in the case in which the weight of each of words in a category 'diet' is calculated, the exposure concentration degree of a word exposed in a site A, the site weight of which is larger than a predetermined threshold value with respect to the category 'diet,' can be calculated using, for example, a frequency increased 1.5 times. That is, if the word 'nice figure' is stated 100 times in the site A, the exposure concentration degree is calculated by taking the word 'nice figure' as having been exposed 150 times. Otherwise, the site weight may be calculated according to the number of times that a word appears in a main site, and the calculated site weight may be multiplied by a weight calculated by the exposure concentration degree calculation module 131.

[54] The location weight calculation module 134 assigns a different weight to a word based on the location of the collected word in each document. The location of a word may be one of a title portion, a body portion, and a background material portion, in which case the location weight calculation module 134 assigns the weight to a word in the order of title portion > body portion > background material portion. This will be described with reference to FIG. 6.

[55] FIG. 6 is a view showing an example of assigning a different weight according to the location that a word occupies in a document. In the document shown in FIG. 6, the words 'model Na- Young Kang' is written in a title portion 61, and the word 'diet' 62 and 64 is displayed in the body and background material portions 65. As shown in the drawing, although the main keyword 'diet' exists in the document, the main purpose of this document is to publicize the model 'Na- Young Kang' shown in the title portion 61, and the diet is only accompanying information. Therefore, the location weight calculation module 134 assigns the highest weight to the word in the case in which the location of a collected word corresponds to a title portion, assigns the next highest weight in the case in which the location of the word corresponds to a body portion, and assigns the lowest weight to the word in the case in which the location of the word corresponds to a background material portion. Such a weight may be reflected when the frequency of the corresponding word is calculated. That is, the exposure frequency of a corresponding word may be calculated on the assumption that a word appearing in a title portion is exposed twice and a word appearing in a body portion is exposed once. Further, it is possible to calculate the weight based on the degree in which a word appears in a title portion, and to multiply the weight by the weight calculated by the exposure concentration degree calculation module 131.

[56] The trust weight calculation module 133 changes the weight calculated by the exposure concentration degree calculation module 131 based on the reliability of a site. The reliability of a site may be determined by checking whether a corresponding site is continuously managed over a predetermined period in a predetermined field (for example, 'diet').

[57] In the case in which a single word is found and collected from two or more categories, the first weight calculation module 135 and the second weight calculation module 136 determine the category in which the collected word is included.

[58] This will be described with reference to FIG. 4. FIG. 4 is a view conceptually showing a category-matching method in the case in which two or more categories are matched with a single word. The word 'scales,' shown in FIG. 4, may be exposed to both the category 'diet' (hereinafter referred to as a "first category" for convenience of description and understanding) and the category 'sport equipment' (hereinafter referred to as a "second category" for convenience of description and understanding).

[59] If the word 'scales' exists as a keyword extracted by the keyword extraction module

120 with respect to the category 'diet,' and the word 'scales' also exists in the category 'sports equipment,' the weight calculation module 130 sets the category 'diet' as the first category and sets the category 'sports equipment' as the second category. The weight of the first category for the word 'scales' is calculated by the first weight calculation module 135, and the weight of the second category for the word 'scales' is calculated by the second weight calculation module 136, and the results of the calculation are transmitted to the category matching module 140, as shown in FIG. 5. [60] Alternatively, the weights of a word having two or more categories may be calculated with respect to respective categories, and the calculated weights may be compared with each other. Thereafter, a category having a larger weight is selected as the category of the corresponding word, and the results of the selection are transmitted to the category matching module 140.

[61] Alternatively, a single word may be included in a plurality of category sets. In this case, the weight of the corresponding word may differ in category. For example, the weight of the word 'scales' may be 5.2 in the category 'diet' and may be 3.1 in the category 'sport equipment'.

[62] The category matching module 140 generates a category set, in which words meeting the predetermined criterion in a corresponding category are associated with the weights thereof, with respect to each of the categories. The category matching module 140 may include an associated keyword determination module 141 and a category set generation module 142.

[63] The associated keyword determination module 141 sets words, each having a weight equal to or larger than a predetermined reference value, or words, each having a weight with a rank equal to or higher than a predetermined rank, as the associated keywords of a corresponding category with reference to the weights of the words provided from the weight calculation module 130 according to each of the categories. For example, a plurality of methods may be adopted, for example, a method of setting words, each having a weight equal to or larger than 3, as the associated keywords of a corresponding category, a method of setting 20 words, having top weights, as the associated keywords of a corresponding category, and a method of setting words, having a weight larger than 2 and within top 20, as the associated keywords of a corresponding category, and so on.

[64] The category set generation module 142 generates a category set based on associated keywords having high weights in a corresponding category, and stores the category set in a database 150. The category set is data in which the associated keywords and the weights thereof are associated with each other. In the case in which the associated keywords included in the category 'diet' are 'nice figure', 'hourglass figure', and 'obese' and the weights thereof are 9, 8, and 7, respectively, the category set represents information in which the word 'nice figure' is combined with the weight 9, the word 'hourglass figure' is combined with the weight 8, and the word 'obese' is combined with the weight 7.

[65] Meanwhile, in the case in which a word found using a main keyword is a unique word which is found only in a corresponding category, the category matching module 140 can assign the highest weight to the unique word. In the case of highly specialized terms, the terms appear only in specific categories, but the exposure frequencies thereof are not high. Therefore, although corresponding words can indicate specific categories, the exposure frequencies thereof are not high. Accordingly, since the weights thereof are low, the words may not be registered as the associated keywords of the corresponding categories. In order to prevent this case, the category matching module 140 assigns the highest weight to a unique word. For example, the word 'pulmonary fibrosis' is not frequently used. However, in the case in which the web documents including this word belongs to a category 'pulmonary disease', even though the weight of the word 'pulmonary fibrosis' is set to a low value according to the above-described method of calculating weights, the highest weight is set for the word 'pulmonary fibrosis' if the word 'pulmonary fibrosis' is determined to be a unique word. One of methods of determining a unique word is to determine that a word is a unique word in the case in which the exposure frequency of the word is almost 0 in general categories but is relatively high in a specific category.

[66] Category sets stored in the database 150 are provided to the analysis module 110 again, and the analysis module 110 searches for new words related to associated keywords using associated keywords combined with the category sets. That is, web documents including associated keywords having higher weights are extracted again, and the above-described procedures are repeated for the corresponding documents, thereby continuously finding associated keywords which are newly coined or derived in each of the categories over time.

[67] The advertisement matching module 160 is connected to portal sites, search sites, and other websites, combines advertisements with web documents provided in the above- described sites, or combines advertisements with newly generated documents, and provides them to the sites. For this purpose, with regard to each of the web documents, the advertisement matching module 160 determines the category of the corresponding web document using words included in the corresponding web document and the category sets, and matches the advertisement of an advertiser who sets the determined category for the corresponding web document. That is, the advertisement matching module 160 reads each of the weights of words (associated keywords) with respect to each category from the category sets stored in the database 150, the words being included in web documents, and multiplies the number of exposures of each of the associated keywords by a weight value, thereby calculating a total weight value in each category. Further, the category that includes the highest weight is set as the category of a corresponding web document, and the advertisement of an advertiser who submitted an advertisement with respect to the corresponding category is combined with the corresponding web document.

[68] As described above, one or more web documents, each including a main keyword or associated keywords which meet a predetermined criterion with respect to each category, are extracted from a plurality of websites, and a plurality of words is extracted from the web documents, and the weight of each of the words in a corresponding category is calculated. Words, the weight values of which meet a predetermined criterion, and unique words are selected as associated keywords in the corresponding category to form a category set along with the main keyword. After the category set is formed, a procedure of forming associated keywords is periodically or occasionally repeated, so that terms newly generated in a corresponding category can be appropriately dealt with. The category sets formed as described above are used to match an advertisement to web documents. Therefore, the weights of words included in web documents are added to each other in each category, with the result that the category having the highest value is set as the category of the corresponding web documents, so that the advertisement of an advertiser, registered for the category, is combined with the web documents, thereby matching the most appropriate advertisement to the web documents.

[69]

Industrial Applicability

[70] According to the present invention, advertisements, which are most related to the content of corresponding web documents, can be displayed on the web documents on the Internet. Further, if one category is selected, the case in which new terms related to the corresponding category appear can be appropriately dealt with.

Claims

[1] A category-based advertising system, comprising: an analysis module for extracting one or more web documents including a main keyword which is representative of each category, from a plurality of web documents; a keyword extraction module for extracting one or more words included in the extracted web documents; a weight calculation module for calculating a weight of each of the extracted words with respect to a corresponding category based on exposure characteristics of the word on the web documents including the main keyword of the corresponding category; a category matching module for setting one or more words, meeting a predetermined criterion in each of the corresponding categories, as associated keywords for the corresponding category, and generating one or more category sets with which associated keywords and weights thereof are associated; and an advertisement matching module for determining a category of each of the web documents using words included in the web document and the category sets, and matching an advertisement of an advertiser registered for the category to the corresponding web document.

[2] The category -based advertising system according to claim 1, wherein the exposure characteristics include an average exposure frequency in which a word is exposed per document, and an exposure concentration degree calculated using a ratio of a number of documents in which the word is exposed, to a total number of documents.

[3] The category-based advertising system according to claim 2, wherein, when the exposure concentration degree is calculated, the weight calculation module assigns a higher weight to a word included in a web document extracted from a site that has a higher weight with respect to each of the categories.

[4] The category-based advertising system according to claim 2, wherein, when the exposure concentration degree is calculated, the weight calculation module assigns a different weight to each of the words based on a location of the word.

[5] The category-based advertising system according to claim 4, wherein the location of a word is any of a title portion, a body portion, and a background material portion of a document, and a weight of the location decreases in order of the title portion, the body portion, and the background material portion.

[6] The category -based advertising system according to claim 1, wherein the category matching module determines words each having a weight equal to or greater than a predetermined reference value in a corresponding category as associated keywords of the corresponding category.

[7] The category -based advertising system according to claim 1, wherein the category matching module assigns a highest weight to a unique word, found substantially in only one category, with respect to the corresponding category.

[8] The category -based advertising system according to claim 1, wherein the category-based advertising system periodically updates the category sets.

[9] The category -based advertising system according to claim 1, wherein the category-based advertising system updates the category sets whenever a new website is added.

[10] The category -based advertising system according to claim 1, wherein the category-based advertising system updates the category sets whenever a new web document is added.

[11] The category -based advertising system according to claim 1, wherein the analysis module extracts one or more web documents including associated keywords which are included in the category sets and meet a predetermined criterion.

[12] The category -based advertising system according to claim 11, wherein the associated keywords meeting a predetermined criterion include associated keywords which have a weight with a rank equal to or higher than a predetermined rank in the corresponding category sets.

[13] The category -based advertising system according to claim 11, wherein the associated keywords meeting a predetermined reference include a unique word found substantially in only one category.

[14] A category-based advertising method, comprising: extracting one or more web documents including a main keyword which is representative of each category, from a plurality of documents; extracting one or more words included in the extracted documents; calculating a weight of each of the extracted words based on exposure characteristics of the words on the web documents including a main keyword of the corresponding category; setting one or more words, meeting a predetermined criterion in each of the corresponding categories, as associated keywords, and generating one or more category sets with which associated keywords and weights thereof are associated; and determining a category of each of the corresponding web documents using words included in each web document and the category sets, and matching an advertisement of an advertiser registered for the determined category to the cor- responding web documents.

[15] The category-based advertising method according to claim 14, wherein the exposure characteristics include an average exposure frequency in which a word is exposed per document, and an exposure concentration degree calculated using a ratio of a number of documents in which the word is exposed, to a total number of documents.

[16] The category -based advertising method according to claim 15, wherein, the exposure concentration degree is calculated by assigning a higher weight to a word included in a web document extracted from a site that has a higher weight with respect to each of the categories.

[17] The category-based advertising method according to claim 15, wherein, when the exposure concentration degree is calculated, a different weight is assigned to each of the words based on a location of the word.

[18] The category -based advertising method according to claim 17, wherein the location of a word is any of a title portion, a body portion, and a background material portion of a document, and a weight of the location decreases in order of the title portion, the body portion, and the background material portion.

[19] The category -based advertising method according to claim 14, further comprising determining words, each having a weight equal to or greater than a predetermined reference value in a corresponding category, as associated keywords of the corresponding category.

[20] The category -based advertising method according to claim 14, further comprising setting a unique word, found substantially in only one category, as an associated keyword of the corresponding category, and assigning a highest weight to the unique word.

[21] The category -based advertising method according to claim 14, further comprising periodically updating the category sets.

[22] The category -based advertising method according to claim 14, further comprising updating the category sets whenever a new website is added.

[23] The category-based advertising method according to claim 14, further comprising updating the category sets whenever a new web document is added.

[24] The category -based advertising method according to claim 14, further comprising extracting one or more web documents including associated keywords which are included in the category sets and meet a predetermined criterion.

[25] The category-based advertising method according to claim 24, wherein the associated keywords meeting a predetermined criterion, include associated key words which have a weight with a rank equal to or higher than a predetermined rank in the corresponding category sets. [26] The category-based advertising method according to claim 24, wherein the associated keywords meeting a predetermined criterion include a unique word found substantially in only one category.