Patent Application
20160180401
1. Field
Systems and methods consistent with the exemplary embodiments relate to online advertising. More particularly, systems and methods consistent with the exemplary embodiments relate to real-time targeting of audience for online contextual advertising.
2. Description of the Related Art
Over the years, the use of the Internet has risen. Recently, the Internet as a platform for commercial activities has gained popularity. One of the major reasons of increased use of the Internet is its easy accessibility by way of improved infrastructure and a wide range of devices to access it.
With the growth of the Internet, the online advertising sector has seen a major boom. Nowadays, online advertising is one of the major media of advertising products and services for various companies. Online advertising involves publishers and advertisers. A publisher is an entity that displays advertisements (ads) on its website. An advertiser is an entity that provides ads to be displayed on the publisher's website. Online advertising includes electronic mails (emails), search engine marketing, display advertising, and mobile advertising. Display advertising uses text, logos, pictures, videos, and the like to advertise on a website. Display advertisers often track a user's activity on the Internet to target ads to the most potential user. This is referred to as ‘targeted advertising’. Behavioral targeting and contextual advertising are types of targeted advertising.
Behavioral targeting includes monitoring and tracking user activities such as sites visited, content viewed, duration of time spent on a particular website, and the like by way of cookies. A cookie is a piece of data that is sent from a website and stored in a user's web browser operating in a device. The cookie records and tracks the web browser activities, such as clicks, websites visited, time of the day, and the like, thus tracking the online behavior of the user. Advertisers provide ads to the users based on their online behavioral pattern.
Contextual advertising involves displaying ads to users based on the content of the website. A contextual advertising system parses the content on the website and identifies a set of keywords associated therewith. When users visit a website, advertisers provide ads to the users based on the identified set of keywords. For example, a user reading an article related to mobile phones on a technology website is provided with ads by various mobile phone companies on the technology website. The online advertisers may also combine behavioral targeting and contextual advertising to identify a potential audience segment.
An online advertising architecture further includes ad exchanges and real-time bidding (RTB) servers. Ad exchanges, such as ADECN™, Doubleclick™ by Google™ and RightMedia™ are online platforms that facilitate bidded buying and selling of advertisements from multiple ad networks. RTB servers facilitate real-time bidding through which ad inventory is bought or sold via programmatic auction. Advertisers have advertising campaigns running on various publisher websites. Ads are served as impressions on these publisher websites to the target audience segment. With real time bidding, ad buyers bid based on impressions, and if the bid is successfully won, the ad is instantaneously displayed on the publisher website.
The success of an advertising campaign is determined by way of a click-through rate (CTR). The CTR represents a ratio of the number of clicks to the number of impressions for an ad and hence, measures the success of an ad campaign. A high CTR implies that more impressions are converted in to clicks, i.e., the advertising campaign is a success. However, for an impression to get clicked or converted it is important that the ad is directed to the correct audience. The correct audience segment is identified by tracking activities of users in the computer network over a period of time and understanding their interests. In recent times, with increasing and varying user activities, it is difficult to keep a track of the user activities and define a specific intent for a particular user over a period of time. For example, when a user is interested in buying a pair of headphones, she may read reviews of various available models on the internet. If the headphone ads are displayed to her at a later time, there is a possibility that she might have purchased the desired headphones from an online store or a brick and mortar store between the time of her reading the reviews and display of the relevant ad. In other words, the ad impression is wasted because the user is no longer interested in the advertised product. Thus, there is a need to identify user intents and interests in real-time.
Further, the online behavior of users is not limited to regular page-views or landing on web pages through search engines. A page-view refers to the number of times a web page is viewed by a user. Users visit a number of social networking sites such as Facebook™, LinkedIn™, Twitter™, Quora™, and so on, and share content of their interest on such sites regularly. Thus, such social activity of users is required to be utilized to determine user interests and intent in real-time and consequently target relevant ads to such an audience segment. Also, the social activities of users need to be used to expand audience segments and achieve high CTRs.
In light of the aforementioned drawbacks of the traditional methods of contextual advertising, it is desirable to provide a method and system that expand audience segments in real-time based on social activities of users and perform real-time audience targeting for contextual advertising.
An aspect of an exemplary embodiment provides a method and system for real-time audience targeting for contextual advertising in a computer network.
An aspect of an exemplary embodiment provides a method and system for real-time expansion of an audience segment based on social sharing activities of users.
An exemplary embodiment provides a system for real-time audience targeting for contextual advertising. The system includes a memory storage device and a processor. The memory storage device stores a first plurality of keywords associated with a brand, a first plurality of user identifications (IDs) associated with a first event, a second plurality of keywords associated with the first plurality of user IDs and the first event, and a third plurality of user IDs associated with a second event. An audience segment is served a plurality of advertisements corresponding to the brand. The processor is connected to the memory storage device. The processor receives the first plurality of user IDs from the memory storage device. The first plurality of user IDs corresponds to a first plurality of users and the first event includes a sharing activity performed by the first plurality of users. The processor receives the second plurality of keywords from the memory storage device and compares the first and second plurality of keywords. The processor generates a second plurality of user IDs when a first set of the first plurality of keywords matches a first set of the second plurality of keywords. The second plurality of user IDs corresponds to a second plurality of users and the second plurality of user IDs is a set of the first plurality of user IDs. The processor generates a set of user profiles corresponding to the second plurality of user IDs based on a plurality of sets of features corresponding to the second plurality of user IDs. The memory storage device stores the plurality of sets of features. The processor generates a set of model files corresponding to a set of features of the plurality of sets of features based on the set of user profiles. A first model file of the set of model files includes a first subset of the set of features and a corresponding first subset of a set of feature scores. The processor calculates a set of feature threshold values corresponding to the plurality of sets of features based on the corresponding first subset of the set of feature scores, a time of day, a day of week, and a social activity. The processor calculates a set of feature weights corresponding to the plurality of sets of features based on a logistic regression model and calculates a threshold value based on the set of feature threshold values and the set of feature weights. The memory storage device stores the plurality of sets of features, the set of feature threshold values, the set of feature weights, and the threshold value. A real-time bidding (RTB) server is connected to the processor and the memory storage device. The RTB server receives the third plurality of user IDs. The third plurality of user IDs correspond to a third plurality of users and the second event includes a sharing activity performed by the third plurality of users. The processor calculates a set of model scores corresponding to the third plurality of user IDs based on the plurality of sets of features, the set of feature threshold values, and the set of feature weights. The processor compares each model score of the set of model scores with the threshold value. The processor generates a fourth plurality of user IDs by combining a first set of the third plurality of user IDs with the second plurality of user IDs when each model score of a first subset of the set of model scores is at least one of greater than and equal to the threshold value. The first subset of the set of model scores corresponds to the first set of the third plurality of user IDs. The processor stores the fourth plurality of user IDs in the memory storage device.
Another exemplary embodiment provides a method for real-time audience targeting for contextual advertising. An audience segment is served a plurality of advertisements corresponding to a brand. A first plurality of keywords associated with the brand is received. A first plurality of user IDs associated with a first event is received. The first plurality of user IDs corresponds to a first plurality of users and the first event includes a sharing activity performed by the first plurality of users. A second plurality of keywords associated with the first event is received. The first and second pluralities of keywords are compared. A second plurality of user IDs is generated when a first set of the first plurality of keywords matches a first set of the second plurality of keywords. The second plurality of user IDs corresponds to a second plurality of users. The second plurality of user IDs is a set of the first plurality of user IDs. A set of user profiles corresponding to the second plurality of user IDs is generated based on a plurality of sets of features corresponding to the second plurality of user IDs. A set of model files corresponding to a set of features of the plurality of sets of features is generated based on the set of user profiles. A first model file of the set of model files includes a first subset of the set of features and a corresponding first subset of a set of feature scores. A set of feature threshold values corresponding to the plurality of sets of features is calculated based on the corresponding first subset of the set of feature scores, a time of day, a day of week, and a social activity. A set of feature weights corresponding to the plurality of sets of features is calculated based on a logistic regression model. A threshold value is calculated based on the set of feature threshold values and the set of feature weights. A third plurality of user IDs associated with a second event is received. The third plurality of user IDs corresponds to a third plurality of users and the second event includes a sharing activity performed by the third plurality of users. A set of model scores corresponding to the third plurality of user IDs is calculated based on the plurality of sets of features, the set of feature threshold values, and the set of feature weights. Each model score of the set of model scores is compared with the threshold value. A fourth plurality of user IDs is generated by combining a first set of the third plurality of user IDs with the second plurality of user IDs when each model score of a first subset of the set of model scores is at least one of greater than and equal to the threshold value. The first subset of the set of model scores corresponds to the first set of the third plurality of user IDs.
Yet another exemplary embodiment provides a computer program product comprising a non-transitory machine-readable medium that stores a program. The program is executed by a machine for expanding an audience segment for contextual advertising. The audience segment is served a plurality of advertisements corresponding to a brand. A first plurality of keywords associated with the brand is received. A first plurality of user IDs associated with a first event is received. The first plurality of user IDs corresponds to a first plurality of users and the first event includes a sharing activity performed by the first plurality of users. A second plurality of keywords associated with the first event is received. The first and second pluralities of keywords are compared. A second plurality of user IDs is generated when a first set of the first plurality of keywords matches a first set of the second plurality of keywords. The second plurality of user IDs corresponds to a second plurality of users. The second plurality of user IDs is a set of the first plurality of user IDs. A set of user profiles corresponding to the second plurality of user IDs is generated based on a plurality of sets of features corresponding to the second plurality of user IDs. A set of model files corresponding to a set of features of the plurality of sets of features is generated based on the set of user profiles. A first model file of the set of model files includes a first subset of the set of features and a corresponding first subset of a set of feature scores. A set of feature threshold values corresponding to the plurality of sets of features is calculated based on the corresponding first subset of the set of feature scores, a time of day, a day of week, and a social activity. A set of feature weights corresponding to the plurality of sets of features is calculated based on a logistic regression model. A threshold value is calculated based on the set of feature threshold values and the set of feature weights. A third plurality of user IDs associated with a second event is received. The third plurality of user IDs corresponds to a third plurality of users and the second event includes a sharing activity performed by the third plurality of users. A set of model scores corresponding to the third plurality of user IDs is calculated based on the plurality of sets of features, the set of feature threshold values, and the set of feature weights. Each model score of the set of model scores is compared with the threshold value. A fourth plurality of user IDs is generated by combining a first set of the third plurality of user IDs with the second plurality of user IDs when each model score of a first subset of the set of model scores is at least one of greater than and equal to the threshold value. The first subset of the set of model scores corresponds to the first set of the third plurality of user IDs.
The features of the exemplary embodiments, which are believed to be novel, are set forth with particularity in the appended claims. Exemplary embodiments will hereinafter be described in conjunction with the appended drawings provided to illustrate and not to limit the scope of the claims, wherein like designations denote like elements, and in which:
As used in the specification and claims, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “an article” may include a plurality of articles unless the context clearly dictates otherwise.
Those with ordinary skill in the art will appreciate that the elements in the figures are illustrated for simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated, relative to other elements, in order to improve the understanding of the exemplary embodiments.
There may be additional components described in the foregoing application that are not depicted on one of the described drawings. In the event such a component is described, but not depicted in a drawing, the absence of such a drawing should not be considered as an omission of such design from the specification.
Before describing the exemplary embodiments in detail, it should be observed that the exemplary embodiments can utilize computer-implemented method for real-time targeting of audience for online advertising. Accordingly, the system components and the method steps have been represented where appropriate by conventional symbols in the drawings, showing only specific details that are pertinent for an understanding of the exemplary embodiments so as not to obscure the disclosure with details that will be readily apparent to those with ordinary skill in the art having the benefit of the description herein. While the specification concludes with the claims defining the features of the exemplary embodiments that are regarded as novel, it is believed that the exemplary embodiments will be better understood from a consideration of the following description in conjunction with the drawings, in which like reference numerals are carried forward.
Detailed exemplary embodiments are disclosed herein; however, it is to be understood that the disclosed exemplary embodiments are merely exemplary and can be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present exemplary embodiments in virtually any appropriately detailed structure. Further, the terms and phrases used herein are not intended to be limiting but rather to provide an understandable description of the exemplary embodiments.
Advertisement campaign (Ad campaign): An advertisement campaign is a sequence of advertisement messages based on a product or a service that are delivered to one or more users through the Internet and World Wide Web. Examples of advertisement campaigns may include, but are not limited to, contextual advertisements on web pages, banner advertisements, rich media advertisements, social network advertisements, online classified advertisements, and advertisements via e-mail marketing, including e-mail spam and/or the like.
Event: An event is an action performed by a user on various websites. The event is also referred to as a user activity. Examples of events include, but are not limited to, sharing through a tracking component such as a widget, a button, a social optimizing pixel, a retargeting pixel, a hypertext, a HyperText Markup Language (HTML) tag, and a link, viewing a web page, clicking a web link, visiting a web page or searching for a keyword. The actions could be either social, where the user shares a Universal Resource Link (URL) to social networks or clicks back to the URL from a social network, or non-social such as a regular page view or landing on the URL through search engines. A page-view refers to the number of times a web page is viewed by a user.
Advertisement data (Ad data): Advertisement data may include, but are not limited to, creatives, impressions, advertisement inventory, channels, timelines, budgets, and other information, including historical information relating to use and distribution of advertisements. The advertisement data may also include advertisement campaign descriptors. The advertisement data may also include an identifier for the user e.g., a cookie, the web page content category, time, price paid, advertisement message shown, and resulting user actions or behavior, or some other type of advertisement campaign and heuristic logs. The advertisement data may also include business statistical data, which may describe dynamic and/or static marketing objectives, or may describe the operation of the advertising server. Finally, the advertisement data in context of the present exemplary embodiments is not limited to the examples cited herein. Any other data related to online advertisements may be used appropriately and the examples cited herein do not restrict the scope of the exemplary embodiments in any way.
Advertisement space (Ad space): An ad space is a space on a web page reserved for displaying the advertisement campaigns. Generally, the ad space is located at the top, bottom, right, or left columns of the web page. The location depends on the size of the advertisement campaign to be displayed, the layout of the page, and the advertising space available on the page.
Advertisement impression (Ad impression): An ad impression (hereinafter referred to as “impression”) refers to the number of times an advertisement is viewed. For example, if an advertisement campaign is viewed by 1,000 users over the course of a day, it is said that the advertisement campaign had 1,000 impressions, as each user viewed goods or services advertised therein. The impression is counted in different ways depending upon format of the ad space situated on the web page, as well as the number of times the web page is shown where the advertisement appears. A number of impressions corresponding to the advertisement campaign is tracked by a tracking component to measure the success of the advertisement campaign.
Audience Segment: An audience segment corresponds to a class or group of the audience. An advertisement campaign finely tuned to an audience segment offers better results, a higher response rate, and a higher conversion rate. Targeting the advertisements to an appropriate audience segment not only enhances the visits but also increases the conversion rates many times. Conversion rate (also known as conversion marketing) is a ratio of the number of transactions performed to the total number of users visiting the website.
Advertisement Conversion (Ad conversion): Advertisement conversion happens when the user performs an action on an advertiser's website after viewing an advertisement. The advertisement conversion may include, but is not limited to, clickthrough, viewthrough, browsethrough, game conversion, and/or the like.
Publisher: A publisher is a group, organization, company, or an individual responsible for originating a production of or maintaining a website. A publisher's revenue comprises advertising revenue paid for by the advertising servers in exchange for placements of their advertisement campaigns on the publisher's website. Advertising servers are servers connected to database servers that store advertisements to be displayed on to websites maintained by the publishers. For example, a site such as Facebook is a publisher and generates revenues by displaying advertisements from various advertisers such as Amazon, Walmart, and the like.
Time of day: The hours of the day during which events occur at a device. The hour of the day is measured according to the local time zone of the device.
Day of the week: The days of the week during which events occur at the device.
Referring now to
In an example, an advertising campaign is initiated by a car manufacturing company. A brand of the car manufacturing company is identified by a set of keywords. For example, an automobile brand like Toyota™ is associated with a set of keywords such as autos, Toyota Camry, Lexus, Scion, airbag, Toyota Vitz, car dealerships, Toyota RAV, Toyota Land Cruiser, Toyota Prado, car insurance, Prius, auto repair, Innova, and so on. The Ad Platform 104 stores the set of keywords associated with the advertising campaign.
To advertise the brand Toyota™ to the most relevant audience segment and to expand the audience segment, a seed audience segment that includes users with interest in automobiles is identified. A user is identified by way of a cookie. However, the user may be identified by way of a persistent device signature as well. For example, a user A reads an article related to automobiles and shares a URL to the article on a social networking website, such as Facebook™ by way of the ShareThis™ widget. The ShareThis™ widget is a web-application written in Java-script that may be embedded in third-party sites through which users can share page content to a number of social channels, including Gmail™, Twitter™, Whatsapp™, Facebook™, Google™, Instagram™, Picasa™, Skype™, StumbleUpon™, DropBox™, and the like. User B, who is a Facebook friend of the user A, receives the URL in his news feed, and visits the web page of the shared article. When the user B visits the shared web page and also shares the URL, a set of keywords associated with the shared URL are recorded in the widget logs 102. Similarly, sets of keywords associated with various URLs are recorded in the widget logs 102 for various events, such as page-view events and click-back events (social as well as non-social) performed by various users. The sets of keywords may be captured by the widget logs 102 by way of a high throughput distributed messaging system known as real-time Kafka system. Kafka may be implemented as a cluster of servers including one or more servers. The sets of keywords are captured in a keyword in context (KWIC) format. It should be noted that Kafka and the generation of n-grams in KWIC is well known in the art and further description of them is avoided so as not to obfuscate the present specification. The captured sets of keywords represent the users' intent and interest.
Referring now to
A user may reset his/her cookies that results in the cookie being deleted. Moreover, certain websites and browsers prevent setting of cookies. This phenomenon of cookies being deleted or expired is referred to as cookie churn. Due to cookie churn, a seed audience segment size is limited and sufficient impressions cannot be served. Therefore, a contextual feature extraction-based method is used in which certain important features like page-view category, user demographics, and other social user segments (e.g. finance, lifestyle, personal-travel based segments) are used to expand the audience size. The selection of these features is based on click-through rate (CTR) optimization. A logistic regression-based method uses click logs to generate a prediction model, which outputs these relevant features.
Referring now to
The user profile generator 120 receives inputs from the bid logs 114, the user logs 116, and the ad logs 118 and generates a profile for each user in the seed audience segment 202 using aggregated feature values. The aggregation of feature values is done over a window of days. The number of days is configurable. The seed audience segment 202 profiles are stored in the storage device 110. The seed audience segment 202 profiles may be stored as flat files on a distributed file system when the storage device 110 is a cluster of machines (distributed set of machines) in a cloud based system, such as the Amazon cloud. An example of a user profile record of the seed audience segment 202 (in tab-delimited Java Script Object Notation (JSON) format with 2 columns) is shown below:
CglhxVJ8P7AlzxX15M NcAg=={“categoryList”:[{“ANX”:{ }, “ADX”:{“20”:0.75, “1001”:0.26830283, “1077”:1, “998”:0.15179452}}], “sqi”:[{“176”:1}], “domains”:[{“1820”:1}], “segmentList”:[{“ANX”:{“100588”:1, “11100574”:1, “11100373”:1, “100372”:1, “100618”:1, “100366”:1, “11100641”:1}, “ADX”:{“100 588”:1, “100574”:1, “100373”:1, “100372”:1, “100618”:1, “100366”:1, “4161900”:1, “100641”:1}}], “ip”:[[“MA|Chicopee|543”]]}
The first column “CglhxVJ8P7AlzxX15MNcAg” is the cookie or the user ID. The second column “{“categoryList”:[{“ANX”:{}, “ADX”:{“20”:0.75, “1001”:0.26830283, “1077”:1, “998”:0.15179452}}], “sqi”:[{“176”:1}], “domains”:[{“1820”:1}], “segmentList”:[{“ANX”:{“100588”:1, “100574”:1, “10037 3”:1, “100372”:1, “100618”:1, “100366”:1, “100641”:1}, “ADX”:{“100588”:1, “100574”:1, “100373”:1, “100372”:1, “100618”:1, “100366”:1, “4161900”:1, “100641”:1}}], “ip”:[[“MA|Chicopee|543”]]}” represents the aggregated feature values. The aforementioned example illustrates a category list for 2 types of ad-networks, ADX and ANX. ADX refers to Double-click by Google™ ad-exchange and ANX represents Appnexus™ ad-exchange. The JSON represents key-value pairs. For example, the first entry in the category list for ADX represents “20”:0.75. In this example, “20” is the category ID and 0.75 is the corresponding score. The corresponding score represents the probability that the cookie CgIhxVJ8P7AlzxX15MNcAg is associated with the category ID 20. As described earlier, examples of category include finance, electronics, automobile, and the like. Such user profiles of the seed audience segment 202 are stored in the storage device 110.
Referring now to
The model files are stored as flat files on the distributed file system in a cluster of machines such as the Amazon cloud. In this exemplary embodiment, the model files are stored in the storage device 110.
Further, the model generator 122 calculates a feature threshold score based on the feature scores and ad traffic requirements of each advertisement campaign. An ad traffic requirement of an advertisement campaign is estimated based on the daily impressions required for the advertisement campaign. To calculate an individual feature threshold score corresponding to a feature, the feature scores in the Table A are arranged in a decreasing order. The feature threshold score is calculated based on the ad traffic requirement of the advertising campaign. For example, the ad traffic requirement is 70 percent. Each feature score is multiplied by 100 and then the feature score that corresponds to a cumulative sum that is greater than or equal to 70 percent represents the feature threshold score. Following is an example of a threshold file for various ad group IDs:
The feature threshold scores are auto-adjusted to optimize for the amount of traffic and the CTR. For example, a 7-day moving average of social activity (i.e., a sum of social events) is used to predict the current social trends and the feature threshold scores are adjusted accordingly. This dynamic calculation and adjustment of the feature threshold scores is done on an hourly basis. The following general formula is used to detect the social trends. For each hour ‘k’ on a particular day, the model generator 122 determines whether the social activity lies within a range of values.
((Average−1.5*standard_deviation)<=social_activity<=(Average+1.5*standard_deviation)) (1)
If equation 1 holds true, there is no change in the impression delivery trend. Here, the average represents an average of the social activity and the standard deviation represents the amount of deviation from a mean value of the social activity. However, if the equation 1 does not hold true, then the model generator 122 multiplies the feature threshold score by a factor that is a function of the social activity, average and standard_deviation. The factor is represented by the following formulae:
(social_activity/(Average+1.5*standard_deviation)) (2)
(social_activity/(Average+1.5*standard_deviation)) (3)
When the (Average−1.5*standard_deviation) value is less than the social_activity value, the model generator 122 multiplies the feature threshold score with the equation 2. When the (Average−1.5*standard_deviation) value is greater than the social_activity value, the model generator 122 multiplies the feature threshold score with the equation 3. Similarly, the feature threshold scores are optimized for clicks of impressions as well. It is desirable to deliver maximum number of impressions, when the CTR is high. The following general formula is used to detect the CTR.
((Average−1.5*standard_deviation)<=CTR<=(Average+1.5*standard_deviation)) (4)
If the equation 4 holds true, there is no change in the impression delivery trend. However, if the equation 4 does not hold true, the model generator 122 multiplies the feature threshold score by a factor that is a function of the CTR, average and standard_deviation. The factor is represented by the following formulae:
(CTR/(Average+1.5*standard_deviation)) (5)
(CTR/(Average−1.5*standard_deviation)) (6)
When the (Average−1.5*standard_deviation) value is less than the CTR value, the model generator 122 multiplies the feature threshold score with the equation 5. When the (Average−1.5*standard_deviation) is greater than the CTR value, the model generator 122 multiplies the feature threshold score with equation 6. Similarly, the feature threshold scores are optimized for day of the week as well.
Each feature is associated with a feature weight. A weight is representative of the importance of the feature. A logistic-regression based optimization method is used to generate a feature weight. The logistic-regression method is a statistical classification model that is used to predict the effect of features on a dependent variable. For example, if the effect of feature such as category has to be predicted on the probability of a click, the probability is obtained by the logistic regression method. In the exemplary embodiment, the model generator 122 receives inputs from the click logs 302. A click log records the clicks associated with the impressions of an ad campaign besides the aforementioned user features. The model generator 122 determines the probability of the click associated with the feature category using the logistic-regression method and generates the corresponding feature weight for the feature category based on the probability of the click. Thus, the logistic-regression based optimization method is used to generate the feature weight. The model generator 122 optimizes the weight of the features using the logistic-regression based optimization method so as to achieve a high CTR. Further, the feature weights are adjusted periodically to account for shift in the CTR trends.
Once the feature threshold scores and the feature weights corresponding to the features are generated, an overall feature threshold score over all the feature threshold scores is generated by the model generator 122. The following is a general formula for calculating the overall feature threshold score:
Overall feature threshold score=feature1-wt*feature1-threshold+feature2-wt*feature2-threshold++feature n-wt*feature n-threshold, where ‘wt’ represents a weight of a corresponding feature. The feature threshold scores, the feature weights, and the overall threshold score in the form of model files are stored in the storage device 110.
To serve an impression to a cookie that is not a part of the seed audience segment 202, the RTB server 124 scores the cookie using the model files from the storage device 110. Thus, the RTB server 124 receives the model files from the storage device 110. When the RTB server 124 receives a bid request from an ad exchange (e.g. Google double-click) (not shown), an overall model score for the cookie is generated and evaluated using summation (over all features) of the product of feature value and feature weight. An overall model score is calculated by using the following equation: Overall model score=feature1-wt*feature 1-score of the cookie +. . . +feature-n-wt*feature-n-score, where ‘wt’ represents a weight of a corresponding feature. This equation is referred to as a model scoring equation. The model score is a value that ranges between 0 and 1. Further, the RTB server 124 compares the model score for the cookie with the overall feature threshold score and if the overall model score is above the overall feature threshold score, the RTB server bids and if successful, an impression is served to the cookie. The aforementioned process is repeated for each cookie resulting in an expansion of the seed audience segment 202.
It is well known in the art that the impression can be placed anywhere on the world-wide-web (WWW) network of webpages which are served by an ad-exchange. For example, an ad-network such as Double-click by Google™ sends a bid-request to the ShareThis™ RTB server when a user lands on a webpage. As described above, the RTB server 124 evaluates the cookie score using the model files and decides whether to bid for it or not. If the ShareThis™ RTB server decides to and wins the bid, the impression is served to the user.
Referring now to
The computer system 500 includes a processor 502, an input/output (10) port 504, a system bus 506, and a memory 508. The memory 508 includes an operating system 510 and a software 512. A non-exhaustive list of examples of suitable commercially available operating systems 510 is as follows: a Windows operating system, a Netware operating system, a Macintosh operating system, a UNIX operating system, a LINUX operating system, a run time Vxworks operating system, or an appliance-based operating system, such as that implemented in handheld computers or personal data assistants (PDAs), or any other suitable operating system, including a customized operating system such as may come installed on a communications network base station, etc. The software 512 includes instructions to be executed to perform the process described here.
The memory 508 may include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.). Moreover, the memory 508 may incorporate electronic, magnetic, optical, and/or other types of storage media. The 10 port 504 is an interface between the computer system 500 and an external network 514, such as the Internet. The 10 port 504 may be connected to 10 devices 516. Examples of the input devices include keyboards, touch sensitive input devices, microphones, and so on to accept inputs from a user. Further, the 10 port 504 may be connected to an output device such as a display screen. The 10 port 504 and the memory 508 communicate by way of the system bus 506. The processor 502 fetches and executes the sets of instructions from the memory 508. The computer system 500 operates in a computer network that may include wired and wireless networks, such as the Internet, local area networks (LAN), metropolitan area networks (MAN), mobile networks and the like. In the exemplary embodiment of this specification, the computer network is the Internet.
The exemplary embodiments may be a program stored and provided on a non-transitory computer-readable medium. The non-transitory computer readable medium refers to a medium which does not store data for a short time such as a register, a cache memory, a memory, or the like but semi-permanently stores data and is readable by a device. In detail, the above-described applications or programs may be stored and provided on a non-transitory computer readable medium such as a CD, a DVD, a hard disk, a blue-ray disk, a universal serial bus (USB), a memory card, a ROM, or the like.
Referring now to
Various exemplary embodiments offer the following advantages: The method and system provide means for real-time audience targeting for contextual advertising in a computer network. The method and system achieve real-time expansion of an audience segment based on social sharing activities of users.
In general, the various exemplary embodiments may be implemented in hardware or special purpose circuits, software, logic, or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a processor, such as a controller, microprocessor or other computing device, although the exemplary embodiments is not limited thereto. While various aspects of the exemplary embodiments may be illustrated and described as block diagrams or flow charts, it will be understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
Thus, the inventive concepts have been described herein with reference to a particular exemplary embodiment for a particular application. Although selected exemplary embodiments have been illustrated and described in detail, it may be understood that various substitutions and alterations are possible. Those having ordinary skill in the art and access to the present teachings may recognize additional various substitutions and alterations are also possible without departing from the spirit and scope, and as defined by the following claims.
