Query feature based data structure retrieval of predicted values

A system of content/query feature based data structure retrieval of predicted values is provided. The system can create a data structure having a plurality of rows corresponding to individual content/query features and a plurality of columns corresponding to individual predicted values. The processors can obtain a set of session features associated with a selection by a computing device in response to a query, and a set of content/query features associated with the selection of the content item. The processors can retrieve, from the data structure, a set of predicted values for each of the set of content/query features. The processors can generate, for each of the set of content/query features, a set of aggregate predicted values for each of the set of content/query features, and can include the set of aggregate predicted values in the data structure.

BACKGROUND

Field of the Invention

Implementations described herein relate generally to measuring and predicting values associated with an online resource retrieved by a computing device following computing device activity in a computer network environment.

Description of Related Art

Computer networked content delivery systems can deliver content items to computing devices over the computer network. Activation of a content item by a computing device can cause the computing device to retrieve an online resource from a server via the computer network. The content delivery system may seek to measure or predict rates of activation of the delivered content items; however, the rate of activation for a delivered content item may not yield useful data about the corresponding online resource.

SUMMARY

According to one aspect, a method may include determining quality values associated with multiple selections of an advertisement, each of the quality values estimating the likelihood that the advertisement is a good advertisement. The method may further include aggregating the quality values and using the aggregated quality values to predict a future likelihood that the advertisement is good.

According to another aspect, a method may include providing one or more advertisements to users in response to search queries and logging user behavior associated with user selection of the one or more advertisements. The method may further include logging features associated with selected ones of the one or more advertisements, or associated with the search queries and using a statistical model and the logged user behavior to estimate quality scores associated with the selected advertisements. The method may also include aggregating the estimated quality scores and predicting the quality of an advertisement of the one or more advertisements using the aggregated quality scores.

According to a further aspect, a method may include receiving a search query from a user and providing a group of advertisements to the user based on the search query. The method may further include receiving, from the user, an indication of a selection of an advertisement from the group of advertisements and logging features associated with the search query or with the selected advertisement. The method may also include retrieving past quality scores from memory using the logged features and predicting a future quality of the selected advertisement based on the retrieved past quality scores.

DETAILED DESCRIPTION

The following detailed description of the invention refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Also, the following detailed description does not limit the invention.

Systems and methods consistent with aspects of the invention may use multiple observations of user behavior (e.g., real-time observations or observations from recorded user logs) associated with user selection of on-line advertisements to more accurately estimate advertisement quality as compared to conventional determinations of quality based solely on CTR. Quality ratings associated with known rated advertisements, and corresponding measured observed user behavior associated with selections (e.g., “clicks”) of those known rated advertisements, may be used to construct a statistical model. The statistical model may subsequently be used to estimate qualities associated with unrated advertisements based on observed user behavior associated with selections of the unrated advertisements.

A “document,” as the term is used herein, is to be broadly interpreted to include any machine-readable and machine-storable work product. A document may include, for example, an e-mail, a web page or site, a business listing, a file, a combination of files, one or more files with embedded links to other files, a news group posting, a blog, an on-line advertisement, etc. Documents often include textual information and may include embedded information (such as meta information, images, hyperlinks, etc.) and/or embedded instructions (such as Javascript, etc.). A “link,” as the term is used herein, is to be broadly interpreted to include any reference to/from a document from/to another document or another part of the same document.

Overview

FIGS. 1 and 2illustrate an exemplary overview of an implementation in which a statistical model, and observed user behavior associated with selection of advertisements is used to estimate predictive values that are further aggregated to provide a future prediction of advertisement quality. The future predictions of ad quality may be used in filtering, ranking or promoting advertisements.

As shown inFIG. 1, each one of multiple rated advertisements100-1through100-N (collectively referred to herein as ad100) may be associated with a corresponding document105-1through105-N (collectively referred to herein as document105). Each document105may include a set of search results resulting from a search executed by a search engine based on a search query provided by a user and may further include one or more advertisements in addition to a rated ad100. Each advertisement100may be associated with ratings data120provided by human raters who have rated a quality of each rated advertisement100. Each advertisement100may advertise various products or services.

In response to receipt of an advertisement100, the receiving user may, based on the “creative” displayed on the advertisement, select110the advertisement (e.g., “click” on the displayed advertisement using, for example, a mouse). After ad selection110, an ad landing document115may be provided to the selecting user by a server hosting the advertisement using a link embedded in ad100. The ad landing document115may provide details of the product(s) and/or service(s) advertised in the corresponding advertisement100.

Before, during and/or after each ad selection110by a user, session features125associated with each ad selection110during a “session” may be measured in real-time or logged in memory or on disk. A session may include a grouping of user actions that occur without a break of longer than a specified period of time (e.g., a group of user actions that occur without a break of longer than three hours).

The measured session features125can include any type of observed user behavior or actions. For example, session features125may include a duration of the ad selection110(e.g., a duration of the “click” upon the ad100), the number of selections of other advertisements before and/or after a given ad selection, the number of selections of search results before and/or after a given ad selection, the number of selections on other types of results (e.g., images, news, products, etc.) before and/or after a given ad selection, a number of document views (e.g., page views) before and/or after a given ad selection (e.g., page views of search results before and/or after the ad selection), the number of search queries before and/or after a given ad selection, the number of queries associated with a user session that show advertisements, the number of repeat selections on a same given advertisement, or an indication of whether a given ad selection was the last selection in a session, the last ad selection in a session, the last selection for a given search query, or the last ad selection for a given search query. Other types of observed user behavior associated with ad selection, not described above, may be used consistent with aspects of the invention.

Using the measured session features125and ad ratings data120, associated with each ad selection110of a corresponding rated advertisement100, a statistical model130may be constructed (as further described below). The statistical model may include a probability model derived using statistical techniques. Such techniques may include, for example, logistic regression, regression trees, boosted stumps, or any other statistical modeling technique. Statistical model130may provide a predictive value that estimates the likelihood that a given advertisement100is good given measured session features associated with a user selection of the advertisement100(e.g., P(good ad|ad selection)=fg(session features)).

Subsequent to construction of statistical model130, ad qualities of unrated advertisements selected by one or more users may be estimated. An unrated ad135, associated with a document140and hosted by a server in a network, may be provided to an accessing user. Session features155associated with user selection145of unrated ad135may be measured or logged in memory or on disk, and the measurements may be provided as inputs into statistical model130. Statistical model130may determine a likelihood that unrated ad135is a good ad, given the measured session features, and may generate a predictive value160for unrated ad135.

Ad/query features165, associated with the selection of unrated ad135, may also be observed and logged. Ad/query features165may include different features associated with the ad135or the advertiser that hosted or generated the ad, or features associated with a query issued by a user that resulted in display of the ad135. For example, ad/query features165may include an identifier associated with the advertiser of ad135(e.g., a visible uniform resource locator (URL) of the advertiser), a keyword that the ad135targets, words in the query issued by the user that ad135did not target, and/or a word in the query issued by the user that ad135did not target but which is similar to a word targeted by ad135. Other types of ad or query features, not described above, may be used consistent with principles of the invention. The estimated predictive value160may be stored in a data structure170according to the associated ad/query features165, as described in further detail below.

ThoughFIG. 1depicts the estimation of a predictive quality value associated with a single unrated ad135, predictive values160may be estimated for each unrated ad135selected by one or more users over a span of time to produce multiple ad predictive values160, with each predictive value160being associated with one or more ad/query feature(s)165. The multiple ad predictive values160may be aggregated in data structure170to produce aggregated predictive values200, as shown inFIG. 2. Aggregation of predictive values is described below with respect toFIG. 14. As further shown inFIG. 2, odds may be estimated210for each ad/query feature in data structure170. The estimated odds may predict a quality of an advertisement given a specific ad/query feature. Further exemplary details of odds estimation is described below with respect toFIGS. 17 and 18. The estimated odds for each ad/query feature may be stored in data structure170.

Ad/query features associated with the selection of an advertisement220may then be obtained220. When a user selects an advertisement from a document (e.g., a search result document), ad/query features associated with that selection may be noted. Estimated odds for each of the ad/query features obtained with respect to the selection of the advertisement may be retrieved230from data structure170. An overall ad quality may then be predicted240using the retrieved estimated odds for each ad/query feature associated with the ad selection. Further exemplary details of the prediction of an overall ad quality is described below with respect toFIG. 19.

Exemplary Network Configuration

FIG. 3is an exemplary diagram of a network300in which systems and methods consistent with the principles of the invention may be implemented. Network300may include multiple clients310connected to one or more servers320-330via a network340. Two clients310and two servers320-330have been illustrated as connected to network340for simplicity. In practice, there may be more or fewer clients and servers. Also, in some instances, a client may perform a function of a server and a server may perform a function of a client.

Clients310may include client entities. An entity may be defined as a device, such as a personal computer, a wireless telephone, a personal digital assistant (PDA), a lap top, or another type of computation or communication device, a thread or process running on one of these devices, and/or an object executable by one of these devices. One or more users may be associated with each client310. Servers320and330may include server entities that access, fetch, aggregate, process, search, and/or maintain documents in a manner consistent with the principles of the invention. Clients310and servers320and330may connect to network340via wired, wireless, and/or optical connections.

In an implementation consistent with the principles of the invention, server320may include a search engine system325usable by users at clients310. Server320may implement a data aggregation service by crawling a corpus of documents (e.g., web documents), indexing the documents, and storing information associated with the documents in a repository of documents. The data aggregation service may be implemented in other ways, such as by agreement with the operator(s) of data server(s)330to distribute their hosted documents via the data aggregation service. In some implementations, server320may host advertisements (e.g., creatives, ad landing documents) that can be provided to users at clients310. Search engine system325may execute a query, received from a user at a client310, on the corpus of documents stored in the repository of documents, and may provide a set of search results to the user that are relevant to the executed query. In addition to the set of search results, server320may provide one or more advertising creatives, associated with results of the executed search, to the user at client310.

Server(s)330may store or maintain documents that may be crawled by server320. Such documents may include data related to published news stories, products, images, user groups, geographic areas, or any other type of data. For example, server(s)330may store or maintain news stories from any type of news source, such as, for example, the Washington Post, the New York Times, Time magazine, or Newsweek. As another example, server(s)330may store or maintain data related to specific products, such as product data provided by one or more product manufacturers. As yet another example, server(s)330may store or maintain data related to other types of web documents, such as pages of web sites. Server(s)330may further host advertisements, such as ad creatives and ad landing documents.

Network340may include one or more networks of any type, including a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network, such as the Public Switched Telephone Network (PSTN) or a Public Land Mobile Network (PLMN), an intranet, the Internet, a memory device, or a combination of networks. The PLMN(s) may further include a packet-switched sub-network, such as, for example, General Packet Radio Service (GPRS), Cellular Digital Packet Data (CDPD), or Mobile IP sub-network.

While servers320-330are shown as separate entities, it may be possible for one of servers320-330to perform one or more of the functions of the other one of servers320-330. For example, it may be possible that servers320and330are implemented as a single server. It may also be possible for a single one of servers320and330to be implemented as two or more separate (and possibly distributed) devices.

FIG. 4is an exemplary diagram of a client or server entity (hereinafter called “client/server entity”), which may correspond to one or more of clients310and/or servers320-330, according to an implementation consistent with the principles of the invention. The client/server entity may include a bus410, a processor420, a main memory430, a read only memory (ROM)440, a storage device450, an input device460, an output device470, and a communication interface480. Bus410may include a path that permits communication among the elements of the client/server entity.

Processor420may include a processor, microprocessor, or processing logic that may interpret and execute instructions. Main memory430may include a random access memory (RAM) or another type of dynamic storage device that may store information and instructions for execution by processor420. ROM440may include a ROM device or another type of static storage device that may store static information and instructions for use by processor420. Storage device450may include a magnetic and/or optical recording medium and its corresponding drive.

Input device460may include a mechanism that permits an operator to input information to the client/server entity, such as a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. Output device470may include a mechanism that outputs information to the operator, including a display, a printer, a speaker, etc. Communication interface480may include any transceiver-like mechanism that enables the client/server entity to communicate with other devices and/or systems. For example, communication interface480may include mechanisms for communicating with another device or system via a network, such as network340.

The client/server entity, consistent with the principles of the invention, may perform certain operations or processes, as will be described in detail below. The client/server entity may perform these operations in response to processor420executing software instructions contained in a computer-readable medium, such as memory430. A computer-readable medium may be defined as a physical or logical memory device and/or carrier wave.

The software instructions may be read into memory430from another computer-readable medium, such as data storage device450, or from another device via communication interface480. The software instructions contained in memory430may cause processor420to perform operations or processes that will be described later. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes consistent with the principles of the invention. Thus, implementations consistent with the principles of the invention are not limited to any specific combination of hardware circuitry and software.

Exemplary Process for Constructing a Statistical Model of User Behavior Associated with Ad Selections

FIG. 5is a flowchart of an exemplary process for constructing a statistical model of user behavior associated with the selections of multiple on-line advertisements. As one skilled in the art will appreciate, the process exemplified byFIG. 5can be implemented in software and stored on a computer-readable memory, such as main memory430, ROM440, or storage device450of server320, server330or a client310, as appropriate.

The exemplary process may begin with obtaining ratings data associated with rated advertisements (block500). The ratings data may include human generated data that rates the quality of each of the rated ads (e.g., one way of rating an ad is to rate how relevant is the ad relative to the query issued). Session features associated with each selection of a rated advertisement may then be obtained (block510). The session features may be obtained in real-time by observing actual user behavior during a given user session, that occurred before, during and after the presentation of each ad impression to a user, or may be obtained from recorded logs of session features (i.e., user behavior and actions) that were stored in a data structure before, during and/or after the presentation of each ad impression to a user. The obtained session features125can include any type of observed user behavior. Each of the session features125may correspond to an indirect measurement of user satisfaction with a given advertisement. Certain ones of the session features125may be factors in determining how different users have different values for other ones of the session features125(e.g., users with dial-up connections may have longer ad selection durations than users who have high speed Internet connections).

Session features125may include, but are not limited to, a duration of an ad selection (e.g., a duration of the “click” upon the advertisement), a number of selections of other advertisements before and/or after a given ad selection, a number of selections of search results before and/or after a given ad selection, a number of selections of other results before and/or after a given ad selection, a number of document views (e.g., page views) before and/or after a given ad selection, a number of search queries before and/or after a given ad selection, a number of search queries associated with a user session that show advertisements, a number of repeat selections on a same given advertisement, or an indication of whether a given ad selection was the last selection in a session, the last ad selection in a session, a last selection for a given search query, or the last ad selection for a given search query.FIGS. 6-13below depict various exemplary types of user behavior, consistent with aspects of the invention, that may be measured as session features.

FIG. 6illustrates the measurement of a duration of an ad selection as a session feature600. As shown inFIG. 6, an ad605, that is associated with a document610, may be provided to a user. In response to receipt of ad605, the user may select615ad605, and an ad landing document620may be provided to the user. A duration625of the ad selection (e.g., the period of time from selection of the advertisement until the user's next action, such as clicking on another ad, entering a new query, etc.) may be measured as a session feature600.

FIG. 7illustrates the measurement of a number of other ad selections before and/or after a particular ad selection as a session feature700. Given a particular selection705of an ad N710, and provision of an ad landing document715in response to the ad selection705, a number of one or more previous ad selections720of ads N−x725, corresponding to provisions of previous ad landing documents730, may be measured. Additionally, or alternatively, given a particular selection705of an ad N710, a number of one or more subsequent ad selections735of ads N+x740, corresponding to provisions of subsequent ad landing documents745, may be measured. The number of other ad selections before and/or after a particular ad selection may be measured as a session feature700.

FIG. 8illustrates the measurement of a number of search result selections before and/or after a particular ad selection as a session feature800. Given a particular selection805of an ad N810, and provision of an ad landing document815in response to the ad selection805, a number of search result documents820viewed by the user before the ad selection805may be measured as a session feature800. The search result documents may be provided to the user based on the execution of a search using a search query issued by the user. Additionally, or alternatively, a number of search result documents825viewed by the user after the ad selection805may be measured as a session feature800.

FIG. 9illustrates the measurement of a number of documents viewed by a user before and/or after a particular ad selection as a session feature900. Given a particular selection905of an ad910, and provision of an ad landing document915in response to the ad selection905, a number of documents920viewed by a user (e.g., page views) before the ad selection905may be measured as a session feature900. Additionally, or alternatively, a number of documents925viewed by a user (e.g., page views) after the ad selection905may be measured as a session feature900.

FIG. 10illustrates the measurement of a number of search queries issued by a user before and/or after a particular ad selection as a session feature1000. Given a particular selection1005of an ad1010, and provision of an ad landing document1015in response to the ad selection1005, a number of search queries1020issued by a user before the ad selection1005may be measured as a session feature1000. Additionally, or alternatively, a number of search queries1025issued by a user after the ad selection1005may be measured as a session feature1000.

FIG. 11illustrates the measurement of a number of search queries, in a session that includes a particular ad selection, that results in the display of an advertisement as a session feature1100. Given a session that includes a particular ad selection, a number of search queries1105may be measured that result in the display of a corresponding ad1110-1through1110-N. The number of search queries may be measured as a session feature1100. The number of search queries1105resulting in the display of an advertisement may indicate the commercial nature of a given user session.

FIG. 12illustrates the measurement of a number of repeat selections of the same advertisement by a user as a session feature1200. As shown inFIG. 12, an ad1205, that may be associated with multiple documents1210-1through1210-N, may be provided to a user one or more times. In response to each receipt of the ad1205, the user may select1215ad1205, and an ad landing document1220may be provided to the user for each of the repeated user selections. The number of repeat selections of the same advertisement by the user may be measured as a session feature1200.

FIG. 13illustrates the determination of whether an ad selection is the last ad selection for a given search query, or whether the ad selection is the last ad selection for a user session as a session feature1300. As shown inFIG. 13, a user may issue a search query1305during a given session1310, and one or more ads1315may be provided to the user subsequent to issuance of search query1305. In response to each receipt of the ad(s)1315, the user may select1320ad1315, and an ad landing document1325may be provided to the user. A determination may be made whether the ad selection1320is the last ad selection for search query1305. Thus, if multiple ads were selected by the user that issued search query1305, then only the last ad selection for search query1305may be identified. A determination may also be made whether the ad selection1320was the last ad selection for session1310. Therefore, if multiple ad selections have been made by the user during a given session, then only the last ad selection for the session may be identified.

Other types of user behavior, not shown inFIGS. 6-13, may be used as session features consistent with principles of the invention. The following lists numerous examples of other exemplary session features:1) instead of an ad selection duration, a ratio of a given ad selection duration relative to an average ad selection duration for a given user may be used as a session feature.2) a ratio of a given ad selection duration relative to all selections (e.g., search result selections or ad selections);3) how many times a user selects a given ad in a given session.4) a duration of time, from an ad result selection, until the user issues another search query. This may include time spent on other pages (reached via a search result click or ad click) subsequent to a given ad click.5) a ratio of the time, from a given ad result selection until the user issues another search query, as compared to all other times from ad result selections until the user issued another search query.6) time spent, given an ad result selection, on viewing other results for the search query, but not on the given ad result.7) a ratio of the time spent in 6) above (i.e., the time spent on other results rather than the click duration) to an average of the time spent in 6) across all queries.8) how many searches (i.e., a unique issued search query) that occur in a given session prior to a given search result or ad selection;9) how many searches that occur in a given session after a given search result or ad selection.10) rather than searches, how many result page views that occur for a given search query before a given selection. This can be computed within the query (i.e., just for a unique query), or for the entire session;11) rather than searches, how many search result page views that occur for a given search query after this selection. This can be computed within the query (i.e., just for the unique query), or for the entire session;12) the total number of page views that occur in the session;13) the number of page views in the session that show ads;14) the ratio of the number of page views in the session that show ads to the total number of page views that occur in the session;15) total number of ad impressions shown in the session;16) average number of ads shown per query that shows ads, another measure of the commerciality of the session;17) query scan time—how long from when the user sees the results of a query to when the user does something else (click on an ad, search result, next page, new query, etc.);18) ratio between a given query scan time and all other query scan times;19) total number of selections (e.g., clicks) that occurred on a given search. These selections include all types of selections (e.g., search, onebox, ads) rather than just ad selections;20) total number of selections that occurred on a search before a given ad selection;21) total number of selections that occurred on a search after a given ad selection;22) total number of ad selections that occurred on a search. May need to be normalized by the number of ads on the page;23) total number of ad selections that occurred on a search before a given ad selection;24) total number of ad selections that occurred on a search after a given ad selection;25) total number of ad selections, that occurred on a search, whose ad positions on a document were located above a position of a given ad on the document;26) total number of ad selections, that occurred on a search, whose ad positions on a document were located below a position of a given ad on the document;27) total number of ad selections that occurred on a search that are not on a given ad;28) total number of search result selections that occurred on a search;29) total number of search selections that occurred on a search before a given ad selection;30) total number of search result selections that occurred on a search after a given ad selection;31) total number of search result selections of a long duration that occurred in the session;32) total number of search result selections of a short duration that occurred in the session;33) total number of search result selections that are last that occurred in the session. A given user may end a session by clicking on a search result, with no subsequent actions, or the user may end a session in some other fashion (e.g., ad result click, issuing a query and not clicking, etc.);34) total number of non-search result and non-ad selections that occurred on a search;35) an indication of whether there was a conversion from this ad selection;36) an indication of the connection speed of the user (e.g., dialup, cable, DSL);37) an indication of what country the user is located in. Different cultures might lead to users reacting differently to the same ad or having different cultural reactions or staying on sites differently;38) an indication of what region of the world (e.g., APAC==asia pacific) that the user is located in;39) was the keyword for a given ad an exact match to the search query (i.e., has all of the same terms as the query) or is missing one word, more than one word, or has rewrite terms. Often, the quality of an ad can vary (the more exact the match, the higher the quality) and keyword matching can be a reasonable way to segment ads, and predict whether an ad is good or bad separately for different match types.40) an indication of an estimated click through rate (CTR) for a given ad;41) what cost per click (CPC) did the advertiser pay for a given ad selection? The likelihood that an ad is good may depend on how much the advertiser paid (more is higher quality);42) what CPC was the advertiser willing to pay? In ad auctioning, advertiser bidding may be used to set ad ranking and the ad/advertiser ranked lower than a given ad/advertiser sets the price that is actually paid by the next higher ranked ad/advertiser;43) effective CPC*predicted CTR; or44) bid CPC*predicted CTR.
The above describes numerous examples of session features that may be used for the statistical model. However, one skilled in the art will recognize that other session features may be used, alternatively, or in conjunction with any of the above-described session features.

Returning toFIG. 5, a statistical model may then be derived that determines the probability that each selected ad is a good quality ad given the measured session features associated with the ad selection (block520). An existing statistical technique, such as, for example, logistic regression may be used to derive the statistical model consistent with principles of the invention. Regression involves finding a function that relates an outcome variable (dependent variable y) to one or more predictors (independent variables x1, x2, etc.). Simple linear regression assumes a function of the form:
y=c0+c1*x1+c2*x2+ . . .  Eqn. (1)
and finds the values of c0, c1, c2, etc. (c0is called the “intercept” or “constant term”). In the context of the present invention, each predictor variable x1, x2, x3, etc. corresponds to a different session feature measured during ad selection. Logistic regression is a variation of ordinary regression, useful when the observed outcome is restricted to two values, which usually represent the occurrence or non-occurrence of some outcome event, (usually coded as 1 or 0, respectively), such as a good advertisement or a bad advertisement in the context of the present invention.

Logistic regression produces a formula that predicts the probability of the occurrence as a function of the independent predictor variables. Logistic regression fits a special s-shaped curve by taking the linear regression (Eqn. (1) above), which could produce any y-value between minus infinity and plus infinity, and transforming it with the function:
P=exp(y)/(1+exp(y))  Eqn. (2)
which produces P-values between 0 (as y approaches minus infinity) and 1 (as y approaches plus infinity). Substituting Eqn. (1) into Eqn. (2), the probability of a good advertisement, thus, becomes the following:

A fit of the statistical model may be tested to determine which session features are correlated with good or bad quality advertisements. If a logistic regression technique is used to determine the statistical model, the goal of logistic regression is to correctly predict the outcome for individual cases using the most parsimonious model. To accomplish this goal, a model is created that includes all predictor variables (e.g., session features) that are useful in predicting the outcome of the dependent y variable. To construct the statistical model, logistic regression can test the fit of the model after each coefficient (cn) is added or deleted, called stepwise regression. For example, backward stepwise regression may be used, where model construction begins with a full or saturated model and predictor variables, and their coefficients, are eliminated from the model in an iterative process. The fit of the model is tested after the elimination of each variable to ensure that the model still adequately fits the data. When no more predictor variables can be eliminated from the model, the model construction has been completed. The predictor variables that are left in the model, each corresponding to a measured session feature, identify the session features that are correlated with good or bad advertisements. Logistic regression, thus, can provide knowledge of the relationships and strengths among the different predictor variables. The process by which coefficients, and their corresponding predictor variables, are tested for significance for inclusion or elimination from the model may involve several different known techniques. Such techniques may include the Wald test, the Likelihood-Ratio test, or the Hosmer-Lemshow Goodness of Fit test. These coefficient testing techniques are known in the art and are not further described here. In other implementations, existing techniques for cross validation and independent training may be used instead of techniques of classical estimation and testing of regression coefficients, as described above.

Other existing statistical techniques, instead of, or in addition to logistic regression, may be used to derive a statistical model consistent with principles of the invention. For example, a “stumps” model, using “boosting” techniques may be used to derive the statistical model. As one skilled in the art will recognize, “boosting” is a machine learning technique for building a statistical model by successively improving an otherwise weak statistical model. The basic idea is to repeatedly apply the same algorithm to an entire training data set, but differentially weight the training data at each stage. The weights are such that cases that are well-fit by the model through stage k receive relatively small weights at stage k+1, while cases that are ill-fit by the model through stage k receive relatively large weights at stage k+1.

Stumps are a weak statistical model that can be applied at each stage. A stump is a 2-leaf classification tree consisting of a root node and a binary rule that splits the cases into two mutually exclusive subsets (i.e., the leaf nodes). A rule could take the form “ClickDuration<120 sec” and all cases with ClickDuration satisfying the rule go into one leaf node and those not satisfying the rule go into the other leaf node. Another rule could take the form “AdSelection was the last ad selection” and all cases with AdSelection satisfying the rule go into one leaf node and those not satisfying the rule go into the other leaf node.

Various algorithms can be used to fit the “boosted stump” model including, for example, gradient-based methods. Such algorithms may proceed as follows: given a set of weights, among all possible binary decision rules derived from session features that partition the cases into two leaves, choose that one which minimizes the (weighted) loss function associated with the algorithm. Some examples of loss functions are “Bernoulli loss” corresponding to a maximum likelihood method, and “exponential loss” corresponding to the well-known ADABoost method. After choosing the best binary decision rule at this stage, the weights may be recomputed and the process may be repeated whereby the best binary rule is chosen which minimizes the new (weighted) loss function. This process may be repeated many times (e.g., several hundred to several thousand) and a resampling technique (such as cross-validation) may be used to define a stopping rule in order to prevent over-fitting.

Boosted stumps have been shown to approximate additive logistic regression models whereby each feature makes an additive nonlinear contribution (on the logistic scale) to the fitted model. The sequence of stumps define the relationship between session features and the probability that an ad is rated “good”. The sequence can be expressed by the statistical model:

Though logistic regression and boosted stumps have been described above as exemplary techniques for constructing a statistical model, one skilled in the art will recognize that other existing statistical techniques, such as, for example, regression trees may be used to derive the statistical model consistent with principles of the invention.

Exemplary Process for Determining Predictive Values Related to Ad Quality

FIG. 14is a flowchart of an exemplary process for determining predictive values relating to the quality of an advertisement according to an implementation consistent with the principles of the invention. As one skilled in the art will appreciate, the process exemplified byFIG. 14can be implemented in software and stored on a computer-readable memory, such as main memory430, ROM440, or storage device450of servers320or330or client310, as appropriate.

The exemplary process may begin with the receipt of a search query (block1400). A user may issue the search query to server320for execution by search engine system325. A set of ads that match the received search query may be obtained by search engine system325(block1405). Search engine system325may execute a search, based on the received search query, to ascertain the set of ads, and other documents, that match the search query. Search engine system325may provide the set of ads, and a list of the other documents, to the user that issued the search query.

Session features associated with the selection of an ad from the set of ads may be obtained (block1410). The session features may be measured in real-time during user ad selection or may be obtained from logs of recorded user behavior associated with ad selection. As shown inFIG. 15, a user may select1500an ad1505associated with a document1510(e.g., a document containing search results and relevant ads). An ad landing document1515may be provided to the user in response to selection of the ad1505. As shown inFIG. 15, session features1520associated with the selection1500of ad1505may be measured. The measured session features may include any type of user behavior associated with the selection of an advertisement, such as those described above with respect to block510(FIG. 5).

The statistical model, derived in block520above, and the obtained session features may be used to determine predictive values1530that the ad is a good ad and/or a bad ad (block1415). The predictive values may include a probability value (e.g., derived using Eqn. (3) or (5) above) that indicate the probability of a good ad given session features associated with user selection of that ad. The predictive values may also include a probability value (Eqn. (4) above) that indicates the probability of a bad ad given measured session features associated with user selection of that ad. Therefore, session feature values may be input into Eqn. (3), (4) and/or (5) to obtain a predictive value(s) that the selected ad is good or bad. For example, values for session features x1, x2, x3and x4may be input into Eqn. (3) to obtain a probability value for P(good ad|session features x1, x2, x3, x4). As shown inFIG. 15, the measured session features1520may be input into statistical model130and statistical model130may output predictive values1530for the ad1505.

Ad/query features associated with the selection of the advertisement may be obtained (block1420). As shown inFIG. 15, the ad/query features1535may be obtained in association with selection1500of the ad1505. The ad/query features1535may include an identifier associated with the advertiser of ad1505(e.g., a visible uniform resource locator (URL) of the advertiser), a keyword that ad1505targets, words in the search query issued by the user that ad1505did not target, and/or a word in the search query issued by the user that the advertisement did not target but which is similar to a word targeted by advertisement1505. Other types of ad or query features, not described above, may be used consistent with principles of the invention. For example, any of the above-described ad/query features observed in combination (e.g., a pairing of two ad/query features) may be used as a single ad query/feature.

For each obtained ad/query feature (i.e., obtained in block1420above), the determined predictive values may be summed with stored values that correspond to the ad/query feature (block1425). The determined predictive values may be summed with values stored in a data structure, such as, for example, data structure1600shown inFIG. 16. As shown inFIG. 16, data structure1600may include multiple ad/query features1610-1through1610-N, with a “total number of ad selections”1620, a total “good” predictive value1630and a total “bad” predictive value1640being associated with each ad/query feature1610. Each predictive value determined in block1405can be summed with a current value stored in entries1630or1640that corresponds to each ad/query feature1610that is further associated with the advertisement and query at issue. As an example, assume that an ad for “1800flowers.com” is provided to a user in response to the search query “flowers for mother's day.” The session features associated with the selection of the ad return a probability P(good ad|ad selection) of 0.9. Three ad/query features are associated with the ad and query: the query length (the number of terms in the query), the visible URL of the ad, and the number of words that are in the query, but not in the keyword that's associated with the ad. For each of the three ad/query features, a corresponding “total number of ad selections” value in entry1620is incremented by one, and 0.9 is added to each value stored in the total good predictive value1630that corresponds to each of the ad/query features.

As shown inFIG. 15, each of the determined predictive values1530may be summed with a current value in data structure1600. Blocks1400through1425may be selectively repeated for each selection of an ad, by one or more users, to populate data structure1600with numerous summed predictive values that are associated with one or more ad/query features.

Exemplary Odds Estimation Process

FIGS. 17 and 18are flowcharts of an exemplary process for estimating odds of good or bad qualities associated with advertisements using the total predictive values1630or1640determined in block1425ofFIG. 14. As one skilled in the art will appreciate, the process exemplified byFIGS. 17 and 18can be implemented in software and stored on a computer-readable memory, such as main memory430, ROM440, or storage device450of servers320or330or client310, as appropriate.

The estimated odds that a given advertisement is good or bad is a function of prior odds that the given advertisement was good or bad, and one or more model parameters associated with ad/query features associated with selection of the given advertisement. The model parameters may be calculated using an iterative process that attempts to solve for the parameter values that produce the best fit of the predicted odds of a good or bad advertisement to the actual historical data used for training.

The model parameters associated with each ad/query feature may consist of a single parameter, such as a multiplier on the probability or odds of a good advertisement or bad advertisement. Alternatively, each ad/query feature may have several model parameters associated with it that may affect the predicted probability of a good or bad advertisement in more complex ways.

In the following description, various odds and probabilities are used. The odds of an event occurring and the probability of an event occurring are related by the expression: probability=odds/(odds+1). For example, if the odds of an event occurring are 1/2 (i.e., the odds are “1:2” as it is often written), the corresponding probability of the event occurring is 1/3. According to this convention, odds and probabilities may be considered interchangeable. It is convenient to express calculations in terms of odds rather than probabilities because odds may take on any non-negative value, whereas probabilities must lie between 0 and 1. However, it should be understood that the following implementation may be performed using probabilities exclusively, or using some other similar representation such as log(odds), with only minimal changes to the description below.

FIG. 17is a flow diagram illustrating one implementation of a prediction model for generating an estimation of the odds that a given advertisement is good or bad based on ad/query features associated with selection of the advertisement. In accordance with one implementation of the principles of the invention, the odds of a good or bad ad may be calculated by multiplying the prior odds (q0) of a good ad or bad ad by a model parameter (mi) associated with each ad/query feature (ki), henceforth referred to as an odds multiplier. Such a solution may be expressed as:
q=q0·m1·m2·m3. . . mm.

In essence, the odds multiplier m for each ad/query feature k may be a statistical representation of the predictive power of this ad/query feature in determining whether or not an advertisement is good or bad.

In one implementation consistent with principles of the invention, the model parameters described above may be continually modified to reflect the relative influence of each ad/query feature k on the estimated odds that an advertisement is good or bad. Such a modification may be performed by comparing the average predicted odds that advertisements with this query/ad feature are good or bad, disregarding the given ad/query feature, to an estimate of the historical quality of advertisements with this ad/query feature. In this manner, the relative value of the analyzed ad/query feature k may be identified and refined.

Turning specifically toFIG. 17, for each selected ad/query feature (ki), an average self-excluding probability (Pi) may be initially calculated or identified (act1700). In one implementation, the self-excluding probability (Pi) is a value representative of the relevance of the selected ad/query feature and may measure the resulting odds that an advertisement is good or bad when the selected ad/query feature's model parameter (mi) is removed from the estimated odds calculation. For ad/query feature 3, for example, this may be expressed as:
P3n+((qo·m1·m2·m3. . . mn)/m3/(((qo·m1·m2·m3. . . mn)/m3+1).

In one embodiment, the self-excluding probability for each ad/query feature may be maintained as a moving average, to ensure that the identified self-excluding probability converges more quickly following identification of a model parameter for each selected ad/query feature. Such a moving average may be expressed as:
Pin(avg)=αPi(n−1)(avg)+(1−α)Pin,
where α is a statistically defined variable very close to 1 (e.g., 0.999) used to control the half-life of the moving average. As shown in the above expression, the value of Pifor the current number of ad selections (n) (e.g., a current value for “total number of ad selections”1620for ad/query feature ki) is weighted and averaged by the value of Pias determined at the previous ad selection (e.g., n−1).

Next, the average self-excluding probability (Pi(avg)), may be compared to historical information relating to the number of advertisement selections observed and the odds of a good or bad advertisement observed for the observed selections (act1710). The model parameter miassociated with the selected ad/query feature kimay then be generated or modified based on the comparison of act1710(act1720) (as further described below with respect to blocks1820and1830ofFIG. 18).

FIG. 18is a flow diagram illustrating one exemplary implementation of blocks1710-1720ofFIG. 17. Initially, a confidence interval relating to the odds of a good ad or bad ad may be determined (act1800). Using a confidence interval technique enables more accurate and stable estimates when ad/query features k having lesser amounts of historical data are used. In one implementation, the confidence interval includes a lower value Liand an upper value Uiand is based on the number of ad selections (ni) (e.g., a current value in “total number of ad selections”1620in data structure1600for ad/query feature ki) and total goodness/badness (ji) observed for the selected ad/query feature (e.g., a current total “good” predictive value1630or total “bad” predictive value1640in data structure1600for ad/query feature ki). For example, the confidence interval may be an 80% confidence interval [Li,Ui] calculated in a conventional manner based on the number of ad selections (e.g., a current value in “total number of ad selections”1620in data structure1600for ad/query feature ki) and total goodness or badness observed (e.g., a current total “good” predictive value1630or total “bad” predictive value1640in data structure1600for ad/query feature ki). Following confidence interval calculation, it may then be determined whether the average self-excluding probability (Pi(avg)) falls within the interval (act1810). If so, it may be determined that the selected ad/query feature (ki) has no effect on the odds of a good ad or bad ad and its model parameter (m1) may be set to 1, effectively removing it from the estimated odds calculation (act1820). However, if it is determined that Pi(avg) falls outside of the confidence interval, then the model parameter (mi) for the selected ad/query feature kimay be set to the minimum adjustment necessary to bring the average self-excluding probability (Pi(avg)) into the confidence interval (act1830). This calculation may be expressed mathematically as:
mi=[Li(1−Pi(avg))]/[Pi(avg)(1−Li)]

Returning now toFIG. 17, once the model parameter mifor the selected ad/query feature kiis calculated, it may be determined whether additional ad/query features (e.g., of ad/query features1610-1through1610-N ofFIG. 16) remain to be processed (i.e., whether ki<km, where m equals the total number of ad/query features in data structure1600) (act1730). If additional ad/query features remain to be processed, the counter variable i may be incremented (act1740) and the process may return to act1700to process the next ad/query feature ki. Once model parameters for all ad/query features have been calculated or modified, the odds of a good ad or bad ad may be estimated using the equation q=q0·m1·m2·m3. . . mm(act1750). The estimated odds of a good ad (e.g., ODDS(good ad|ad query feature)) may be stored in a “good” ad odds entry1650of data structure1600that corresponds to the ad/query feature1610. The estimated odds of a bad ad (e.g., ODDS(bad ad|ad/query feature)) may be stored in “bad” ad odds entry1660of data structure1600that corresponds to the ad/query feature1610.

In one implementation consistent with principles of the invention, the odds prediction model may be trained by processing log data as it arrives and accumulating the statistics mentioned above (e.g., ad selections, total goodness or badness, self-including probabilities, etc.). As additional ad selections occur, the confidence intervals associated with each ad/query feature may shrink and the parameter estimates may become more accurate. In an additional implementation, training may be accelerated by reprocessing old log data. When reprocessing log data, the estimated odds of a good ad or bad ad may be recalculated using the latest parameter or odds multiplier values. This allows the prediction model to converge more quickly.

Exemplary Ad Quality Prediction Process

FIG. 19is a flowchart of an exemplary process for predicting the quality of advertisements according to an implementation consistent with the principles of the invention. As one skilled in the art will appreciate, the process exemplified byFIG. 19can be implemented in software and stored on a computer-readable memory, such as main memory430, ROM440, or storage device450of servers320or330or client310, as appropriate.

The exemplary process may begin with the receipt of a search query from a user (block1900). The user may issue the search query to server320for execution by search engine system325. A set of ads that match the received search query may be obtained by search engine system325(block1910). Search engine system325may execute a search, based on the received search query, to ascertain the set of ads, and other documents, that match the search query. For each ad of the set of ads, every ad/query feature that corresponds to the received search query and the ad may be determined (block1920). The ad/query features for each search query and ad pair may include an identifier associated with the advertiser (e.g., a visible uniform resource locator (URL) of the advertiser), a keyword that the ad targets, words in the search query issued by the user that ad did not target, and/or a word in the search query issued by the user that the advertisement did not target but which is similar to a word targeted by the advertisement. Other types of ad or query features, not described above, may be used consistent with principles of the invention. For example, any of the above-described ad/query features observed in combination (e.g., a pairing of two ad/query features) may be used as a single ad query/feature.

For each ad of the set of ads, stored ODDSi(e.g., ODDS (good ad|ad/query feature)1650, ODDS (bad ad|ad/query feature)1660), for every one of the determined ad/query features i, may be retrieved from data structure1600(block1930). As shown inFIG. 20, data structure1600may be indexed with ad/query features2000that correspond to the search query and the ad to retrieve one or more ODDSi2010associated with each ad/query feature. For example, as shown inFIG. 16, a “good” ad odds value1650corresponding to each ad/query feature1610may be retrieved. As another example, as shown inFIG. 16, a “bad” ad odds value1660corresponding to each ad/query feature1610may be retrieved.

For each ad of the set of ads, the retrieved ODDSifor each ad/query feature i may be multiplied together (block1940) to produce a total ODDS value (ODDSt):
ODDSt=ODDS1*ODDS2*ODDS3* . . .  Eqn. (6)
For example, the “good” ad odds values1650for each ad/query feature may be multiplied together to produce a total good ad odds value ODDSt_GOOD AD. As another example, the “bad” ad odds values1660for each ad/query feature may be multiplied together to produce a total bad ad odds value ODDSt_BAD AD. As shown inFIG. 20, the ODDS2010retrieved from data structure1600may be multiplied together to produce a total odds value ODDSt2020.

For each ad of the set of ads, a quality parameter that may include a probability that the ad is good (PGOOD AD) and/or that the ad is bad (PBAD AD) may be determined (block1950):
PGOOD AD=ODDSt_GOOD AD/(1+ODDSt_GOOD AD)  Eqn. (7)
PBAD AD=ODDSt_BAD AD/(1+ODDSt_BAD AD)  Eqn. (8)
As shown inFIG. 20, the total odds value ODDSt1820, and equations (7) or (8) may be used to derive a quality parameter (P)2030. The derived quality parameter P may subsequently be used, for example, to filter, rank and/or promote advertisements as described in U.S. application Ser. No. 11/328,064, now issued as U.S. Pat. No. 7,827,060, entitled “Using Estimated Ad Qualities for Ad Filtering, Ranking and Promotion,” filed on Dec. 30, 2005, and incorporated by reference herein in its entirety.

CONCLUSION

The foregoing description of preferred embodiments of the present invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings, or may be acquired from practice of the invention. For example, while series of acts have been described with regard toFIGS. 5, 14, and 17-19, the order of the acts may be modified in other implementations consistent with the principles of the invention. Further, non-dependent acts may be performed in parallel.

In addition to the session features described above, conversion tracking may optionally be used in some implementations to derive a direct calibration between predictive values and user satisfaction. A conversion occurs when a selection of an advertisement leads directly to user behavior (e.g., a user purchase) that the advertiser deems valuable. An advertiser, or a service that hosts the advertisement for the advertiser, may track whether a conversion occurs for each ad selection. For example, if a user selects an advertiser's ad, and then makes an on-line purchase of a product shown on the ad landing document that is provided to the user in response to selection of the ad, then the advertiser, or service that hosts the ad, may note the conversion for that ad selection. The conversion tracking data may be associated with the identified ad selections. A statistical technique, such as, for example, logistic regression, regression trees, boosted stumps, etc., may be used to derive a direct calibration between predictive values and user happiness as measured by conversion.