EP1861820A2 - Procede et appareil de generation et/ou de prediction d'efficacite de mots vedettes - Google Patents

Procede et appareil de generation et/ou de prediction d'efficacite de mots vedettes

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Publication number
EP1861820A2
EP1861820A2 EP06737681A EP06737681A EP1861820A2 EP 1861820 A2 EP1861820 A2 EP 1861820A2 EP 06737681 A EP06737681 A EP 06737681A EP 06737681 A EP06737681 A EP 06737681A EP 1861820 A2 EP1861820 A2 EP 1861820A2
Authority
EP
European Patent Office
Prior art keywords
keyword
keywords
advertising
entities
prediction function
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP06737681A
Other languages
German (de)
English (en)
Other versions
EP1861820A4 (fr
Inventor
Zachary Mason
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Efficient Frontier Inc
Original Assignee
Efficient Frontier Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US11/370,679 external-priority patent/US20060206479A1/en
Priority claimed from US11/371,267 external-priority patent/US10515374B2/en
Application filed by Efficient Frontier Inc filed Critical Efficient Frontier Inc
Publication of EP1861820A2 publication Critical patent/EP1861820A2/fr
Publication of EP1861820A4 publication Critical patent/EP1861820A4/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3325—Reformulation based on results of preceding query
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques

Definitions

  • the present invention relates to the field of data processing, in particular, to methods and apparatuses for keyword effectiveness prediction and/or > keyword generation, having particular application to advertising with search engines.
  • Search engines exist to make information accessible. Among the kinds of information promulgated by search engines is advertising.
  • the display of advertisements (“ads") is often mediated by a bidding system - an advertiser bids on a keyword, and the placement of his ad on the search result page for that keyword depends on, possibly among other factors, his bid.
  • the click-through rate on the ad is a function of its placement. It is in an advertiser's interest to know about as many relevant keywords as possible.
  • Manually generating keywords for a domain is a difficult, labor intensive task - for a given topic there can be many keywords. Additionally, the linguistic behavior associated with search is not entirely like any other, and thus may be difficult to intuit.
  • an automated method for generating search keywords for a merchant is valuable. It is also valuable to know how well a keyword will serve its purpose in order to bid optimally, both for merchants manually compiling a bidding strategy and for those using mathematical optimization techniques.
  • FIG 1 illustrates an overview of the present invention, in accordance with various embodiments
  • FIGS 2a-2b illustrate an overview of the keyword effectiveness prediction aspects of the present invention, in accordance with various embodiments;
  • Figures 3a-3b illustrate flow chart views of selected operations of the keyword effectiveness prediction methods of various embodiments of the present invention
  • Figure 4 illustrates an exemplary table for storing refined training data utilized by various components of embodiments of the present invention
  • Figure 5 illustrates an overview of the keyword generation aspects of the present invention, in accordance with various embodiments
  • Figure 6 illustrates a flow chart view of selected operations of the keyword generation methods of various embodiments of the present invention
  • Figures 7a-7b are depictions of graphical representations of relationships between keywords and items or entities, in accordance with various embodiments.
  • Figure 8 is a block diagram illustrating an example computing device suitable for use to practice the keyword effectiveness prediction and/or keyword generation aspects of the present invention, in accordance with various embodiments.
  • Illustrative embodiments of the present invention include, but are not limited to, methods and apparatuses for receiving one or more indicators indicating one or more degrees of relevance or irrelevance of one or more items or entities to advertising keywords of interest, generating one or more advertising keyword suggestions suggesting one or more advertising keywords based at least in part on whether there are predetermined relationships among the suggested one or more advertising keywords, and between some of the suggested one or more advertising keywords and the one or more items or entities.
  • Illustrative embodiments of the present invention may also additionally or alternatively compute a predictive measure for an advertising effectiveness metric for each of one or more advertising keywords based at least in part on one or more feature values of the keywords, employing, for example, a prediction function of the effectiveness metric.
  • the prediction function may have been generated based on a plurality of other keywords and feature values of the one or more features of the other keywords.
  • Various aspects of the illustrative embodiments will be described using terms commonly employed by those skilled in the art to convey the substance of their work to others skilled in the art. However, it will be apparent to those skilled in the art that alternate embodiments may be practiced with only some of the described aspects. For purposes of explanation, specific numbers, materials, and configurations are set forth in order to provide a thorough understanding of the illustrative embodiments. However, it will be apparent to one skilled in the art that alternate embodiments may be practiced without the specific details. In other instances, well-known features are omitted or simplified in order not to obscure the illustrative embodiments.
  • key may refer to any word, string, token, phrase, or collection of words, strings, tokens, or linguistic constructs that may be searched upon by a user.
  • phrase "in one embodiment” is used repeatedly. The phrase generally does not refer to the same embodiment; however, it may.
  • A/B means “A or B”.
  • a and/or B means “(A), (B), or (A and B)”.
  • at least one of A, B and C means “(A), (B), (C), (A and B), (A and C) 1 (B and C) or (A, B and C)”.
  • (A) B means "(B) or (A B)", that is, A is optional.
  • FIG. 1 illustrates an overview of the present invention, in accordance with various embodiments.
  • some embodiments of the present invention include one or more items or entities of interest 102 as well as indicators of relevance or irrelevance of the one or more items/entities 102.
  • the items/entities and indicators 102 may be entered into a generator 104, the generator capable of generating one or more advertising keyword suggestions 108, in some embodiments by retrieving keyword and item/entity relationship data from a keyword relationship database 106 and by analyzing the retrieved data. Components and data capable of performing these operations are further illustrated in Figure 5 and are described in greater detail below.
  • the items/entities 502 may represent the items/entities 102
  • the generator 504 may represent the generator 104.
  • the results of the above described keyword generation aspects of the present invention may be further refined by keyword effectiveness prediction aspects of the present invention.
  • the keywords 108 may be received by a prediction function 110 capable of computing predictive measures 112 of advertising effectiveness metrics for each of the keywords 108.
  • the predictive measures 112 may inform a bidding strategy.
  • a merchant can choose which keywords to bid on, or the predictive measures 112 may be input to a system that empirically learns the true value of these keywords for a merchant.
  • the predictive measures may also serve as input to optimization operations.
  • FIG. 2a The process for generating such a prediction function 110 is illustrated by Figure 2a and is described in greater detail below, where the keyword salient property prediction function 210 may represent prediction function 110. Additionally, the use of prediction function 210 to compute predictive measures 112 is illustrated by Figure 2b and is also described in greater detail below. In various embodiments, keyword effectiveness prediction may also be practiced for manually provided keywords.
  • Figures 2a-2b illustrate an overview of the keyword effectiveness prediction aspects of the present invention, in accordance with various embodiments.
  • Figure 2a depicts data and components employed by embodiments of the present invention to generate a keyword salient property prediction function 210 ("prediction function") capable of computing predictive measures of advertising effectiveness metrics for one or more keywords 212.
  • prediction function 210 involves training data 202 comprised of keywords and salient properties known about the keywords.
  • the training data 202 may have one or more feature values generated to describe the features of its keywords by a feature calculator 204, in some embodiments.
  • the refined collection of training data 206 comprised of keywords and their associated feature values and salient properties, may then be provided to and employed by a machine learning tool 208 to generate prediction function 210.
  • the data and components shown in Figure 2a may all be located on the same computing device, or may be located on a plurality of computing devices, in some embodiments connected by a network.
  • the computing devices may include any sort of devices known in the art, such as PCs, workstations, servers, PDAs, and mobile phones.
  • Exemplary networks connecting computing devices may also be of any sort known in the art, including Ethernet, Bluetooth, and 802.11 networks.
  • Such networks may also utilize any sort of communication protocol known in the art, such as TCP/IP or ATM.
  • data may be written onto a storage medium by one computing device and read from the medium by another device.
  • the storage media may be of any sort known in the art, including floppy diskettes, CDs, and thumb drives.
  • training data 202 comprises a collection of keywords about which certain salient properties are known, such as dick- through and conversion rate metrics. Such metrics may be indicative of advertising effectiveness (and thus are hereafter referred to as advertising effectiveness metrics) and may be empirically gathered for a merchant, industry, or product, in various embodiments.
  • a keyword, such as those included in training data 202, may be bid on by a merchant, and the winning bidder or bidders may have their webpages and/or advertisements listed on the search results page for that keyword when the keyword or a related phrase incorporating the keyword is searched upon by a user of the search engine that is auctioning off the keyword.
  • the search engine may look for ads keyed on related keywords that may not contain the base keyword, based, in one embodiment, on a semantic relatedness measure.
  • a click-through rate metric associated with the keyword may then be gathered by tracking the number of clicks a merchant's, industry's, or product's webpages and/or advertisements displayed by the search results page receive by search engine users who have searched based upon the keyword.
  • the click-through rate metric may represent the total number of clicks for a day, a month, or a year, but may be for any duration.
  • a conversion rate metric associated with the keyword may then also be gathered by tracking the number of sales or other indicators reflective of consumption of transactions arising from the above described keyword search resultant clicks.
  • the conversion rate may, in some embodiments, represent the total number of sales or transactions for the same duration as the click-through rate metric, or may represent the number of sales for a different duration. Further, if additional financial information is available for a particular merchant, industry, or product, the conversion rate may be optimized for revenue, profit, cost-per-customer-acquisition, profit- per-customer, and any number of other metrics.
  • the salient measure with respect to conversion may be an absolute number of conversions, a number of conversions per time, or some value-scaled measure of conversions per time.
  • a feature calculator may generate one or more feature values for the training data.
  • Features may comprise any attribute or aspect descriptive of the keyword or its usage. For example, a feature of a keyword may simply be one or more words found within a keyword. Thus, if the keyword is the phrase "Internet router," both router and Internet may be features of the keyword.
  • Features may also include an association between two or more words of a keyword. In various embodiments, it may be useful to analyze the underlying combinatorics of a keyword.
  • the set of color words (“yellow”, “blue”, etc.) combines with the set of clothing words ("pants”, “shirt”, etc.) to form valid keywords ("yellow pants”, “yellow shirt”, “blue pants”, etc..) but not with, for example, the names of software products (“yellow msword”?)
  • the word “fencing” may be presumed to mean a constructed barrier, while in “Olympic fencing", it may be presumed to mean fencing-as-sport. Such sense-disambiguation may permit more effective predictions to be made.
  • feature calculator 204 may also generate feature values for other features beyond those identified with the presence/absence of a word within a keyword. For example, the frequency with which a keyword appears in the search logs of queries issued against a corpus of relevant documents, the frequency of appearance of a keyword in a document section for a corpus of documents (where the corpus is selected, for instance, based on the relatedness of its constituents to the vertical in question), and/or a distance of a keyword from another (or a number of other) keyword(s) (where distance, in some instances, is a mathematical measure of semantic similarity), may also be considered a feature or features of the keyword. Where distance of a keyword is a feature, the various notions of distance may be measured.
  • “Edit distance”, for instance, may measure the lexical distance between strings. Thus, “trap” and “tap” would have a distance of 1. "Semantic distance”, in contrast, may measure the difference in meaning between strings. Accordingly, “trap” and “tap” would not be close, but “tap” and “faucet” would be close. These examples are however by no means exhaustive of the possible features that feature calculator 204 may calculate feature values for.
  • the feature values generated by feature calculator 204 for keyword features of training data 202 may, in various embodiments, be Boolean, integer, or real valued. For example, if a keyword contains a certain word, it may have a feature value of 1 , and if it does not contain that word, it may have a feature value of 0.
  • a keyword of the training data 202 may be described by more than one feature, and may thus have multiple feature values generated for it by feature calculator 204, corresponding to the multiple features of the keyword.
  • feature values such as the integers 1 and 0 and the Booleans TRUE and FALSE are illustrated by Figure 4 and are described in greater detail below.
  • feature calculator 204 may be any sort of process or processes known in the art capable of analyzing training data 202 and generating feature values for the data 202 based on the analysis.
  • the feature calculator may be implemented as an application program operated by the processor of a computer device and stored in the memory of the device (as is depicted in Figure 8), or may have some hardware implementation, such as that of an application specific integrated circuit (hereinafter "ASIC").
  • ASIC application specific integrated circuit
  • feature calculator 204 may then output refined training data 202 which may include the feature values generated by feature calculator 204.
  • the refined training data 206 may consist of sets of pairs for each keyword of the training data 202/206, each pair containing the set of all feature values for that keyword and also the salient property metrics of interest for that keyword.
  • the refined training data 206 may be implemented as a table having columns for keywords, feature values, and salient property metrics, and one or more rows for each keyword. An example of such a table is illustrated by Figure 4.
  • the refined training data need not, however, be implemented as a table, but may have any sort of data structure known in the art.
  • the machine learning tool 208 may, in some embodiments, receive the refined training data 206 as input. Upon receiving such input, the machine learning tool 208 may process the refined training data and output a prediction function 210.
  • machine learning methods There are a number of machine learning methods that may be implemented by tool 208 to generate the prediction function 210.
  • Two such methods used in various embodiments are back-propagation methods and support vector machines. Such methods enable machine learning tool 208 to bin outputs - that is, to discretize the space of possible outputs, if an embodiment requires a discrete feature space. The binning of outputs may thereby reduce the complexity of the learning problem and permit a tool to work at a level of specificity that does not exceed the uncertainty inherent in the data.
  • any other machine learning method known in the art may be implemented as machine learning tool 208. Such methods are known to those skilled in the art, and accordingly will not be described further.
  • machine learning tool may be any sort of process or processes known in the art capable of analyzing refined training data 206 and generating a prediction function 210 based on the analysis.
  • the machine learning tool 208 may be implemented as an application program operated by the processor of a computer device and stored in the memory of the device (as is depicted in Figure 8), or may have some hardware implementation, such as that of an ASIC.
  • the prediction function 210 generated by tool 208 may be particularized for a selected one of a merchant, an industry, or a product, in various embodiments. If the refined training data 206 is already particularized for a merchant, product, or industry, the prediction function 210 generated by the machine learning tool 208 will by nature be particularized for the merchant, product, or industry as well. In alternate embodiments, the tool 208 may be equipped with an addition filter capable of recognizing keywords in the refined training data 206 associated with a merchant, industry, or product, and then processing only those keywords and their associated feature values and salient property metrics.
  • prediction function 210 takes a set of feature values for a received keyword (presumably not a keyword in the training data 202/206) and computes a predictive measure of advertising effectiveness for that keyword.
  • Figure 2b illustrates data and components employed by embodiments of the present invention to compute a predictive measure 214 for an advertising effectiveness metric for a received keyword 212.
  • the computation of the predictive measure 214 for a keyword 212 involves, in some embodiments, receiving a keyword 212 as well as receiving pre- generated feature values of the keyword 212. The keyword 212 and feature values may then be input to prediction function 210, which may then compute the predictive measure 214.
  • the keyword 212 may represent any sort of keyword for which a merchant or group of merchants are interested in knowing one or more advertising effectiveness metrics.
  • the keyword 212 may be one for which such advertising effectiveness metrics are not known, and thus one for which a predictive measure of such metrics may be desirable.
  • the keyword 212 may have had its feature values pre-generated by some other source or process, such as feature calculator 204, in various embodiments.
  • the prediction function 210 or some other component or process, such as feature calculator 204 may be equipped or utilized to generate feature values for keyword 212. The process for generating feature values, as well as feature calculator 204, are described in greater detail above.
  • the prediction function 210 may be generated by the process described above and illustrated by Figure 2a.
  • the prediction function 210 may compute the predictive measure for a keyword 212 by comparing feature values of the keyword 212 to feature values of keywords of training data 202/206. If the feature values are the same or similar for keyword 212 and a keyword of the training data 202/206, the salient property metric associated with the keyword of the training data 202/206 may be utilized in computing the predictive measure.
  • the prediction function 210 has been generated by a back propagation method and/or a support vector machine, other or additional operations may be involved in computing the predictive measures 214.
  • keywords of training data 202/206 are found the same or similar to keyword 212, their salient properties may be averaged, weighted and summed, or listed separately. For instance, if two keywords of training data 202/206 are found to have the same feature values as keyword 212, their salient property metrics, such as click-through rates for a week of one thousand clicks and four hundred clicks respectively, may be averaged, here resulting in a predictive measure of seven hundred clicks per week.
  • the prediction function 210 is also described in detail above.
  • the predictive measure 214 computed by prediction function 210 may be for any sort of advertising effectiveness metric known in the art, such as the salient property measures of click-through and conversion rates known for training data 202. Such click-through and conversion rates are described in greater detail above in regard to training data 202.
  • the predictive measure 214 may be computed for a particular merchant, industry, or product. Such computing may depend on particularizing the prediction function 210 for a merchant, industry, or product. An exemplary method for particularizing the function 210 computing the predictive measure 214 is also described above. Further, prediction functions 210 of greater complexity may also be developed to compute predictive measures 214.
  • Keywords on which low bids are placed get little traffic and so little conversion data would be generated. It is possible that these keywords would convert well if they got more traffic, but there is no good way to find out - getting this information would require bidding them up (and possibly spending money for nothing.)
  • Predictive models of keyword conversion such as the prediction function 210, may be used to highlight keywords that merit exploratory up-bidding. Thus, the predictive model may reduce the cost of gathering empirical data.
  • Figures 3a-3b illustrate flow chart views of selected operations of the keyword effectiveness prediction methods of various embodiments of the present invention.
  • Figure 3a depicts selected operations of methods of embodiments of the present invention for generating a prediction function capable of computing predictive measures of advertising effectiveness metrics for one or more keywords.
  • training data comprising a collection of keywords about which certain salient properties are known, such as click-through and conversion rate metrics, may first be received, block 302.
  • metrics may be indicative of advertising effectiveness and may be empirically gathered for a merchant, industry, or product, in various embodiments.
  • a keyword such as those included in the training data, may be bid on by a merchant, and the winning bidder or bidders may have their webpages and/or advertisements listed on the search results page for that keyword when the keyword or a related phrase incorporating the keyword is searched upon by a user of the search engine that is auctioning off the keyword.
  • a search engine may look for ads keyed on related keywords that may not contain the base keyword, based, in one embodiment, on a semantic relatedness measure.
  • a click-through rate metric associated with the keyword may then be gathered by tracking the number of clicks a merchant's, industry's, or product's webpages and/or advertisements displayed by the search results page receive by search engine users who have searched based upon the keyword.
  • the click-through rate metric may represent the total number of clicks for a day, a month, or a year, but may be for any duration.
  • a conversion rate metric associated with the keyword may then also be gathered by tracking the number of sales arising from the above described keyword search resultant clicks.
  • the conversion rate may, in some embodiments, represent the total number of sales for the same duration as the click-through rate metric, or may represent the number of sales for a different duration. Further, if additional financial information is available for a particular merchant, industry, or product, the conversion rate may be optimized for revenue, profit, cost-per-customer-acquisition, profit- per-customer, and any number of other metrics.
  • the salient measure with respect to conversion may be an absolute number of conversions, a number of conversions per time, or some value-scaled measure of conversions per time. Also, the uncertainty in empirically estimated conversion probabilities decreases with larger sample size, so for better accuracy, keywords for which the sample size (number of clicks, number of conversions) is below some threshold may be excluded from the training data.
  • a feature calculator may generate one or more feature values for the training data, block 304.
  • Features may comprise any attribute or aspect descriptive of the keyword or its usage. For example, a feature of a keyword may simply be one or more words found within a keyword. Thus, if the keyword is the phrase "Internet router," both router and Internet may be features of the keyword.
  • Features may also include an association between two or more words of a keyword. In various embodiments, it may be useful to analyze the underlying combinatorics of a keyword. That is, to find what sets of words combine with other sets of words to form keywords.
  • the set of color words (“yellow”, “blue”, etc.) combines with the set of clothing words ("pants”, “shirt”, etc.) to form valid keywords ("yellow pants”, “yellow shirt”, “blue pants”, etc..) but not with, for example, the names of software products (“yellow msword”?)
  • the word “fencing” may be presumed to mean a constructed barrier, while in “Olympic fencing", it may be presumed to mean fencing-as-sport. Such sense-disambiguation may permit more effective predictions to be made.
  • features of the keywords of the training data may also include features beyond the presence/absence of a word within a keyword.
  • the frequency with which a keyword appears in the search logs of queries issued against a corpus of relevant documents the frequency of appearance of a keyword in a document section for a corpus of documents (where the corpus is selected, for instance, based on the relatedness of its constituents to the vertical in question), and/or a distance of a keyword from another (or a number of other) keyword(s) (where distance, in some instances, is a mathematical measure of semantic similarity), may also be considered a feature or features of the keyword. Where distance of a keyword is a feature, the various notions of distance may be measured.
  • Keywords may measure the lexical distance between strings. Thus, “trap” and “tap” would have a distance of 1. "Semantic distance”, in contrast, may measure the difference in meaning between strings. Accordingly, “trap” and “tap” would not be close, but “tap” and “faucet” would be close. These examples are however by no means exhaustive of the possible features that a keyword may have.
  • the feature values generated for keyword features of the training data may, in various embodiments, be Boolean, integer, or real valued. For example, if a keyword contains a certain word, it may have a feature value of 1, and if it does not contain that word, it may have a feature value of 0. Often, a keyword may be described by more than one feature, and may thus have multiple feature values generated for it, corresponding to the multiple features of the keyword.
  • refined training data which includes the feature values generated above, may next be provided to a machine learning tool, block 306.
  • the refined training data may consist of sets of pairs for each keyword of the training data, each pair containing the set of all feature values for that keyword and also the salient property metrics of interest for that keyword.
  • the refined training data may be implemented as a table having columns for keywords, feature values, and salient property metrics, and one or more rows for each keyword.
  • the refined training data need not, however, be implemented as a table, but may have any sort of data structure known in the art.
  • the machine learning tool may process the refined training data and generate a prediction function, block 308.
  • a prediction function There are a number of machine learning methods that may be implemented by the machine learning tool to generate a prediction function. Two such methods used in various embodiments are back-propagation methods and support vector machines. Such methods enable the machine learning tool to bin outputs - that is, to discretize the space of possible outputs, if an embodiment requires a discrete feature space. The binning of outputs may thereby reduce the complexity of the learning problem and permit a tool to work at a level of specificity that does not exceed the uncertainty inherent in the data.
  • the prediction function generated by the machine learning tool may be particularized for a selected one of a merchant, an industry, or a product, in various embodiments. If the refined training data is already particularized for a merchant, product, or industry, the prediction function generated by the machine learning tool will by nature be particularized for the merchant, product, or industry as well.
  • the machine learning tool may be equipped with an additional filter capable of recognizing keywords in the refined training data associated with a merchant, industry, or product, and then processing only those keywords and their associated feature values and salient property metrics.
  • Figure 3b illustrates selected operations of methods of embodiments of the present invention for computing a predictive measure for an advertising effectiveness metric for a received keyword.
  • a keyword and one or more feature values of one or more features of the keyword may first be received, block 310.
  • the received keyword may represent any sort of keyword for which a merchant or group of merchants are interested in knowing one or more advertising effectiveness metrics. Also, the keyword may be one for which such advertising effectiveness metrics are not known, and thus one for which a predictive measure of such metrics may be desirable.
  • methods of an embodiment may then determine if feature values have also been received with the keyword, block 312.
  • the keyword may have had its feature values pre-generated by some other source or process, such as a feature calculator, in various embodiments.
  • methods of an embodiment of the present invention may then generate feature values for the keyword, block 314. The process for generating feature values for keywords is described in greater detail above.
  • methods of an embodiment of the present invention may then compute a predictive measure for a keyword by employing the prediction function such as the function generated by the selected operation illustrated in Figure 3a and described above, block 316.
  • the predictive measure may be computed by comparing feature values of the received keyword to feature values of keywords of the training data. If the feature values are the same or similar for the received keyword and a keyword of the training data, the salient property metric associated with the keyword of the training data may be utilized in computing the predictive measure.
  • the prediction function has been generated by a back propagation method and/or a support vector machine, other or additional operations may be involved in computing the predictive measure.
  • the predictive measure computed by the prediction function may be for any sort of advertising effectiveness metric known in the art, such as the salient property measures of click-through and conversion rates known for the training data. Such click-through and conversion rates are described in greater detail above in regard to the training data.
  • the predictive measure may be computed for a particular merchant, industry, or product. Such computing may depend on particularizing the prediction function for a merchant, industry, or product. An exemplary method for particularizing the prediction function computing the predictive measure is also described above.
  • Figure 4 illustrates an exemplary table for storing refined training data utilized by various components of embodiments of the present invention.
  • the table contains a plurality of keywords, a feature value associated with each feature of a keyword, and a salient property value metric associated with each keyword.
  • the keywords, feature values, and salient property value metrics illustrated by the table, as well as the process for generating the refined training data 206 illustrated by the table of Figure 4, are all described in greater detail above in reference to Figures 2a-2b.
  • FIG. 5 illustrates an overview of the keyword generation aspects of the present invention, in accordance with various embodiments.
  • a user may provide one or more items or entities 502 of interest regarding advertising keywords as well as indicators of relevance or irrelevance of the items or entities 502.
  • Such items/entities 502 may be received by a generator 504 capable of generating one or more advertising keyword suggestions 508 based on the relationships of the received items/entities to various keywords, those keyword and item/entity relationships stored, in some embodiments, in a keyword relationship database 506.
  • the data and components shown in Figure 5 may all be located on the same computing device, or may be located on a plurality of computing devices, in some embodiments connected by a network.
  • the computing devices may include any sort of devices known in the art, such as PCs, workstations, servers, PDAs, and mobile phones.
  • Exemplary networks connecting computing devices may also be of any sort known in the art, including Ethernet, Bluetooth, and 802.11 networks.
  • Such networks may also utilize any sort of communication protocol known in the art, such as TCP/IP or ATM.
  • data may be written onto a storage medium by one computing device and read from the medium by another device.
  • the storage media may be of any sort known in the art, including floppy diskettes, CDs, and thumb drives.
  • some or all of the data and components shown in Figure 5 may be located on the same computer system as some or all of the data and components shown in Figure 2.
  • items or entities 502 may be any sort of criteria useful to identify keywords of interest, such as keywords, words, websites, merchants (i.e., merchants in the same space), and categories in an ontology. Additionally, the items or entities 502 may include weighted Boolean indicators of relevance and/or irrelevance. In some embodiments, the degree of relevance or irrelevance is selected by a user. In various embodiments, irrelevant keywords, words, merchants, etc, may be assigned negative weights to reflect their irrelevancy. In various embodiments, relevant and irrelevant keywords may be learned from a variety of sources, including but not limited to search engines, such as Google, or Overture.
  • items or entities 502 may be characterized based on other criteria, such as regular expressions like "home (re)?financ.*,” to characterize keywords like "home finances” and “home refinancing”.
  • items or entities 502 may include context-free grammars.
  • items or entities 502 and their indicators of relevance and/or irrelevance may be input into a generator 504 adapted to receive as input items or entities 502, as well as indicators of relevance and/or irrelevance.
  • the possible indicators of relevance and/or irrelevance and weights may be presented to users in a drop down list, from which a user may select the indicator and weight to accompany the item or entity 502.
  • the list or some other type of user interface element may allow a user to select whether an item or entity is relevant or irrelevant, and the generator 504 (described below) may assign the item or entity a weight based on an operation. For example, different query types may be assigned fixed, preset weights.
  • a user may enter the name of the keyword, word, merchant, etc. in a user interface element, and may select an item or entity type, such as "keyword" or
  • generator 504 may be a keyword search engine capable of generating advertising keyword suggestions 508. Such a keyword search engine 504 may be different from an Internet search engine such as Google or Overture.
  • a user may enter items or entities 502 and indicators of relevance and/or irrelevance into a user interface of generator 504.
  • generator 504 may be any sort of application software or application specific integrated circuit (ASIC) known in the art capable of receiving items or entities 504, retrieving keywords from a database 506 of keyword relationships, performing a spreading activation analysis on a graph constructed from the retrieved keywords, and generating advertising keyword suggestions 508 based on the analysis.
  • keyword relational database 506 may be any sort of relational database known in the art. Database 506 may contain tables of keywords, tables of merchants, tables of other items or entities 502, or some combination of two or more of those sorts of tables. Additionally, the relationships between the tables of database 506 may be achieved through the use of keywords and/or items or entities 502 as keys that are present in two or more of the tables.
  • database 506 may comprises a plurality of tables of merchants.
  • Each merchant table may contain at least one or more keywords that have been bid upon by the merchant, as well as the number of times each of the contained keywords have been bid upon.
  • the keywords bid upon by each merchant may serve as keys connecting the merchant tables of database 506.
  • the keyword and item or entity pairs stored in database 506 may be gathered from any number of sources.
  • the keywords and the items or entities related to the keywords may be gathered via some web monitoring process, such as a process that monitors which keywords a group of merchants bids upon, or a process that identifies which keywords are associated with a group of webpages.
  • Such a process may be performed prior to the operations of embodiments of the present invention, or concurrently with those operations.
  • a process or a user may store the gathered keyword and item or entity 502 pairs in keyword relational database 506 by creating a plurality of tables and storing the gathered data in the tables. Methods for creating tables and storing data in tables are well known in the art.
  • generator 504 may retrieve all or a subset of the data stored in the database 506. The generator 504 may retrieve the data through the use of a query, and may use items or entities 502 as parameters of the query, thus retrieving only relevant results.
  • the retrieved data may comprise all or a portion of the tables retrieved.
  • spreading activation analysis may then be applied to the retrieved data by generator 504 to determine which advertising keywords to suggest.
  • spreading activation analysis may comprise generator 504 first generating a data structure for a graph having nodes and segments connecting the nodes, with at least one of the retrieved keywords and/or items or entities 502 occupying the nodes or the segments.
  • merchant entities 502 may be represented in the graph as nodes, and keywords that merchants 502 have bid upon may be represented as edges, with a keyword/edge connecting two merchants/nodes 502 if both merchants have bid on that keyword.
  • the graph may be constructed with bid upon keywords represented as nodes and merchant entities 502 represented as edges, with a merchant/edge 502 connecting two keyword/nodes if both keywords have been bid upon by that merchant 502.
  • the graph generated may be a bipartite graph in which one set of nodes may represent merchant entities 502 and another may represent keywords. Edges may then connect a merchant 502 node and a keyword node if the merchant 502 has bid on that keyword. Examples of such graphs are illustrated by Figures 7a-7b and are discussed in further detail below.
  • generator 504 may generate a data structure implementing any one of the above described graph arrangements, or some other arrangement, by any method known in the art.
  • generator 504 may create a C++ class to represent the graph, the graph class containing node child classes or data structures, the child class or structure containing a variable representing the edge, the variable comprising a pointer to another node child class or structure. Any given child class or structure node may contain a plurality of variables representing a plurality of edges. Further, each child class/structure representing a node may contain a variable indicating a degree of activation of that node. Upon constructing such a graph, generator 504 may assign the retrieved data to its intended locations within the graph.
  • each node may be assigned to a retrieved merchant, and each edge variable may represent a keyword that the merchant whose node has the variable has bid upon.
  • the edge variable may represent a pointer to a node of another bidding merchant.
  • the retrieved data may be represented by a graph generated by generator 504.
  • one or more of the nodes of the generated graph may be assigned a degree of activation based upon the indicators of relevance and/or irrelevance.
  • the activation may be a positive or negative integer, with a positive integer indicating relevance, and a negative integer indicating irrelevance.
  • an extremely relevant indicator may be associated with a greater integer, such as '5', and a somewhat relevant indicator may be associated with a lesser integer, such as '2.
  • a node associated with the first may be assigned an activation of '5,' and a node associated with the second may be assigned an activation of '-2.
  • the assigned activation may then be iteratively propagated away from each node having an assigned activation to all of the nodes to which it is connected, until a finishing criterion has been met.
  • a convergence threshold might be reached, or a certain number of iterations may have taken place.
  • some fixed fraction of its activation may be evenly divided up between all of the related nodes. Each of these may then have its activation decreased, and further additional nodes that are connected will each then received some degree of activation.
  • a convergence threshold such as a number of propagation cycles having passed, has been reached, the iterative propagation of activation may stop.
  • generator 504 may then determine which nodes are activated. If the activated nodes represent items or entities 502, each keyword associated with an activated node (perhaps represented by an edge) may then be generated as an advertising keyword suggestion 508. In other embodiments, if the activated nodes represent keywords, the keywords associated with the activated nodes may then be generated by generator 504 as advertising keyword suggestions.
  • the advertising keyword suggestions 508 comprise a non-final set of keywords, and are subject to filtering or constraining based on one or more criteria.
  • An exemplary criterion may be the predictive measures of advertising effectiveness generated for keywords described above and illustrated by Figure 2. Filtering based upon such predictive measures is further illustrated above by Figure 1.
  • advertising keyword suggestions 508 serve as input to a prediction function 110/210, which may compute a predictive measure of each of the advertising keyword suggestion. Upon computing the predictive measure, only those keywords meeting a certain threshold predictive measure may comprise a final set of advertising keyword suggestions.
  • Figure 6 illustrates a flow chart view of selected operations of the keyword generation methods of various embodiments of the present invention.
  • keyword generation methods may first comprise receiving items or entities and indicators of relevance and/or irrelevance of each of the items or entities, block 602.
  • the items or entities may be any sort of criteria useful to identify keywords of interest, such as keywords, words, websites, merchants (i.e., merchants in the same space), and categories in an ontology.
  • the items or entities may include weighted Boolean indicators of relevance and/or irrelevance.
  • the degree of relevance or irrelevance may be selected by a user. Irrelevant keywords, words, merchants, etc, may be assigned negative weights to reflect their irrelevancy.
  • the relevant and irrelevant keywords may be learned from a variety of sources, including but not limited to search engines, such as Google, or Overture.
  • items or entities may be characterized based on other criteria, such as regular expressions like "home (re)?financ.*,” to characterize strings like “home finances” and “home refinancing”. Additionally, items or entities may include context-free grammars.
  • the items or entities and their indicators of relevance and/or irrelevance may be received via a generator adapted to receive items or entities as well as indicators of relevance and/or irrelevance, block 602.
  • the possible indicators of relevance and/or irrelevance and weights may be presented to users in a drop down list, from which a user may select the indicator and weight to accompany the item or entity.
  • the list or some other type of user interface element may allow a user to select whether an item or entity is relevant and/or irrelevant, and a weight may be assigned to an item or entity based on an operation. For example, different query types may be assigned fixed, preset weights.
  • a user may enter the name of the keyword, word, merchant, etc. in a user interface element, and may select an item or entity type, such as "keyword" or "merchant" to correspond to the entered name. In alternate embodiments, the user may enter both the name and the type of the item or entity.
  • keyword generation methods may store pairs of keywords and related items of entities in a database, block 606.
  • the database may be any sort of database known in the art.
  • the database may contain tables of keywords, tables of merchants, tables of other items or entities, or some combination of one or more of those sorts of tables. Additionally, the relationships between the tables of database may be achieved through the use of keywords and/or items or entities as keys that are present in two or more of the tables.
  • database may comprise a plurality of tables of merchants. Each merchant table may contain at least one or more keywords that have been bid upon by the merchant, as well as the number of times each of the contained keywords has been bid upon.
  • the keywords bid upon by each merchant may serves as keys connecting the merchant tables of database.
  • the keyword and item or entity pairs stored in the database, block 606, may be gathered from any number of sources.
  • the keywords and the items or entities related to the keywords may be gathered via some web monitoring process, such as a process that monitors which keywords a group of merchants have bid upon, or a process that identifies which keywords are associated with a group of webpages. Such a process may be performed prior to the operations of the keyword generation methods, or concurrently with those operations.
  • a process or a user may store the gathered keyword and item or entity pairs in a database, block 606, by creating a plurality of tables and storing the gathered data in the tables. Methods for creating tables and storing data in tables are well known in the art.
  • the keyword generation method for generating advertising keyword suggestions, block 604 may comprise the operations of retrieving keyword and item or entity pairs from the database, block 608, and performing spreading activation analysis on the retrieved pairs, block 610.
  • Retrieval operations, block 608, may involve retrieving all or a subset of the data stored in the database. The data may be retrieved through the use of a query, and may use items or entities as parameters of the query, thus retrieving only relevant results.
  • keyword generation methods may then perform spreading activation analysis to determine which advertising keywords to suggest, block 610.
  • spreading activation analysis may comprise the operations of generating a data structure for a graph to represent the retrieved pairs, block 612, assigning an amount of activation to node of the graph, block 614, and iteratively propagating the activation to other graph nodes, block 616.
  • a data structure for a graph having nodes and segments connecting the nodes, with at least one of the retrieved keywords and/or items or entities occupying the nodes or the segments may then be generated, block 612.
  • merchant entities may be represented in the graph as nodes
  • keywords that merchants have bid upon may be represented as edges, with a keyword/edge connecting two merchants/nodes if both merchants have bid on that keyword.
  • the graph may be constructed with bid upon keywords represented as nodes and merchant entities represented as edges, with a merchant/edge connecting two keyword/nodes if both keywords have been bid upon by that merchant.
  • the graph generated may be a bipartite graph in which one set of nodes may represent merchant entities and another may represent keywords. Edges may then connect a merchant node and a keyword node if the merchant has bid on that keyword.
  • the data structure implementing any one of the above described graph arrangements, or some other arrangement, may be generated by any method known in the art, block 612.
  • the graph may be represented by a C++ class, the graph class containing node child classes or data structures, the child class or structure containing a variable representing the edge, the variable comprising a pointer to another node child class or structure.
  • Any given child class or structure node may contain a plurality of variables representing a plurality of edges.
  • each child class/structure representing a node may contain a variable indicating a degree of activation of that node.
  • the retrieved data may be assigned to its intended locations within the graph.
  • each node may be assigned to a retrieved merchant, and each edge variable may represent a keyword that the merchant whose node has the variable has bid upon.
  • the edge variable may represent a pointer to a node of a second bidding merchant.
  • one or more of the nodes of the generated graph may be assigned a degree of activation based upon the indicators of relevance and/or irrelevance, block 614.
  • the activation may be a positive or negative integer, with a positive integer indicating relevance, and a negative integer indicating irrelevance.
  • an extremely relevant indicator may be associated with a greater integer, such as '5', and a somewhat relevant indicator may be associated with a lesser integer, such as '2.
  • a node associated with the first may be assigned an activation of '5,' and a node associated with the second may be assigned an activation of '-2.
  • the assigned activation may then be iteratively propagated away from each node having an assigned activation to all of the nodes to which it is connected, until a finishing criterion has been met, block 616.
  • a convergence threshold might be reached, or a certain number of iterations may have taken place.
  • some fixed fraction of its activation may be evenly divided up between all of the related nodes. Each of these may then have its activation decreased, and further additional nodes that are connected will each then received some degree of activation.
  • a convergence threshold such as a number of propagation cycles having passed, has been reached, the iterative propagation of activation may cease.
  • advertising keyword suggestions corresponding to some or all of the activated nodes or edges of such nodes may then be generated, block 604. If the activated nodes represent items or entities, each keyword associated with an activated node (perhaps represented by an edge) may then be generated as an advertising keyword suggestion. In other embodiments, if the activated nodes represent keywords, the keywords associated with the activated nodes may then be generated as advertising keyword suggestions.
  • the advertising keyword suggestions comprise a non-final set of keywords, and are subject to filtering or constraining based on one or more criteria, block 618 (shown as an optional operation).
  • An exemplary criterion may be the predictive measures of advertising effectiveness generated for keywords described above and illustrated by Figure 2. Filtering based upon such predictive measures is further illustrated above by Figure 1.
  • the advertising keyword suggestions serve as input to a prediction function, which may compute a predictive measure of each of the advertising keyword suggestion. Upon computing the predictive measure, only those keywords meeting a certain threshold predictive measure may comprise a final set of advertising keyword suggestions.
  • FIGS 7a-7b are depictions of graphical representations of relationships between keywords and items or entities, in accordance with various embodiments.
  • generator 504 may generate a data structure comprising a graphical representation ("graph") that includes nodes and edges.
  • the nodes may represent items or entities, such as merchants of interest, and the edges may represent keywords common to the items or entities.
  • Figure 7a Such an embodiment is illustrated by Figure 7a.
  • the keywords may represent nodes, and items or entities may represent edges connecting the keyword nodes.
  • both keywords and items or entities may be represented by nodes, and an edge may connect a keyword node and an item/entity node if some relationship exists between the keyword and the item/entity.
  • Figure 7b illustrates such embodiments, where merchant (entity) and keyword nodes are connected by an edge if the merchant represented by the merchant node has bid on the keyword represented by the keyword node.
  • Such graphs as those illustrated by Figures 7a-7b may facilitate spreading activation analysis, the manner in which such analysis is facilitated described in greater detail above in reference to Figure 5.
  • FIG. 8 is a block diagram illustrating an example computing device suitable for use to practice the keyword effectiveness prediction and/or keyword generation aspects of the present invention, in accordance with various embodiments.
  • computing system/device 800 includes one or more processors 802, and system memory 804. Additionally, computing system/device 800 includes mass storage devices 806 (such as diskette, hard drive, CDROM and so forth), input/output devices 808 (such as keyboard, cursor control and so forth) and communication interfaces 810 (such as network interface cards, modems and so forth).
  • the elements are coupled to each other via system bus 812, which represents one or more buses. In the case of multiple buses, they are bridged by one or more bus bridges (not shown).
  • system memory 804 and mass storage 806 may be employed to store a working copy and a permanent copy of the programming instructions implementing the keyword effectiveness prediction and/or keyword generation aspects of the above described teachings to practice the present invention, here shown as computational logic 822.
  • the programming instructions may be implemented as assembler instructions supported by processor(s) 802 or high level languages, such as C, that can be compiled into such instructions.
  • the permanent copy of the programming instructions may be placed into permanent storage 806 in the factory, or in the field, through e.g. a distribution medium (not shown) or through communication interface 810 (from a distribution server (not shown)).
  • a distribution medium not shown
  • communication interface 810 from a distribution server (not shown)
  • the constitution of these elements 802-812 are known, and accordingly will not be further described.

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Abstract

La présente invention concerne des procédés, appareils, et articles pour recevoir un ou plusieurs indicateurs indiquant un ou plusieurs degrés de pertinence ou non-pertinence d'un ou plusieurs items ou entités pour des mots vedettes publicitaires pris en considération, générer une ou plusieurs suggestions de mots vedettes publicitaires suggérant un ou plusieurs mots vedettes publicitaires sur la base au moins en partie de l'éventualité qu'ils y ait des relations prédéterminées parmi un ou plusieurs des mots vedettes publicitaires, et entre certains des mots vedettes publicitaires suggérés et un ou plusieurs des items ou entités. Certains modes de réalisation de l'invention comportent également, éventuellement en remplacement, un calcul de mesure prédictive destiné à une mesure d'efficacité publicitaire pour chacun des différents mots vedettes sur la base au moins en partie de l'une des valeurs de représentation des mots vedettes, utilisant, notamment, une fonction prédictive des mesures d'efficacité.
EP06737681A 2005-03-10 2006-03-09 Procede et appareil de generation et/ou de prediction d'efficacite de mots vedettes Withdrawn EP1861820A4 (fr)

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US11/370,679 US20060206479A1 (en) 2005-03-10 2006-03-08 Keyword effectiveness prediction method and apparatus
US11/371,267 US10515374B2 (en) 2005-03-10 2006-03-08 Keyword generation method and apparatus
PCT/US2006/008525 WO2006099105A2 (fr) 2005-03-10 2006-03-09 Procede et appareil de generation et/ou de prediction d'efficacite de mots vedettes

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