Scoring of content items having a messaging application as a landing page

An online system receives a content item from a content provider, the content item having a bid price and including a link to a messaging application. The link is configured to initiate a direct message in the messaging application between the content provider and a user who interacts with the content item. The online system receives a request for content items from a target user and determines a compatibility score between the target user and the content item based on one or more sets of criteria. A first set of criteria indicates a compatibility between the user and the messaging application. The online system determines a subsidy for the content item based on the compatibility score. The online system adjusts the bid price with the determined subsidy to determine an auction price and includes the content item with the auction price in a selection process for presentation to the user of the online system.

BACKGROUND

This disclosure relates generally to determining a compatibility score for a user and an advertisement, in particular for an advertisement that includes a link to a messaging application.

Some advertisements attempt to engage a user by including a link to a browser, an application, a pop-up window, etc. In a specific example, an advertisement includes a link to a messaging application. Responsive to a user interacting with the advertisement, a server initiates a direct message between the advertiser and the user in the messaging application. These types of advertisements allow the advertiser to engage with an individual user, but they can be costly and impractical to implement. These types of advertisements often use a human moderator to monitor the advertiser account 24 hours a day in order to live chat with users via the messaging application. Alternatively, a computerized bot may monitor the advertiser account, but it may be difficult to train the bot to effectively engage with a user. Furthermore, a user may be concerned with privacy when engaging with an advertiser via a messaging application as the online system may monitor the certain aspects of the communication between the user and the advertiser to gather data regarding whether the communication was successful (e.g., whether the communication resulted in a conversion). Accordingly, a system that protects user privacy and allows advertisers to more effectively engage with users is needed.

SUMMARY

A system and method for determining compatibility between a user and an advertisement. An online system receives an advertisement (“ad”) from an advertiser, the ad having a bid price and including a link to a messaging application. The link is configured to initiate a direct message in the messaging application between the advertiser and a user who interacts with the ad. The online system receives an ad request from a target user and determines a compatibility score for the advertisement and the target user based on one or more sets of criteria, including a first set of criteria indicating compatibility between the target user and the messaging application. The compatibility score can be based on characteristics of the target user (e.g., gender, location), historical interaction between the target user and the messaging application, feedback from one or more users of the online system related to the messaging application, etc. Additional criteria in the one or more sets of criteria can indicate a compatibility between the target user and the advertisement (e.g., based on past interaction of the target user with similar content) and/or between the target user and the advertiser (e.g., based on purchasing habits of the target user). In some embodiments, the compatibility score is a likelihood of deep conversation, where the likelihood of deep conversation is a likelihood that the target user and the advertiser will exchange at least three messages in the messaging application. The online system determines a subsidy for the ad based on the compatibility score. The online system determines an auction price for the ad by adjusting the bid price with the determined subsidy and includes the ad with the auction price in a selection process for presentation to the user of the online system.

DETAILED DESCRIPTION

System Architecture

FIG. 1is a block diagram of a system environment100for an online system140. The system environment100shown byFIG. 1comprises one or more client devices110, a network120, one or more third party systems130, and the online system140. In alternative configurations, different and/or additional components may be included in the system environment100. For example, the online system140is a social networking system, a content sharing network, or another system providing content to users.

One or more third party systems130may be coupled to the network120for communicating with the online system140, which is further described below in conjunction withFIG. 2. In one embodiment, a third party system130is an application provider communicating information describing applications for execution by a client device110or communicating data to client devices110for use by an application executing on the client device. In other embodiments, a third party system130provides content or other information for presentation via a client device110. In one embodiment, the third party system130is an advertiser. A third party system130may also communicate information to the online system140, such as advertisements, content, or information about an application provided by the third party system130.

FIG. 2is a block diagram of an architecture of the online system140. The online system140shown inFIG. 2includes a user profile store205, a content store210, an action logger215, an action log220, an edge store225, an ad request store230, a content selection module235, a web server240. In other embodiments, the online system140may include additional, fewer, or different components for various applications. Conventional components such as network interfaces, security functions, load balancers, failover servers, management and network operations consoles, and the like are not shown so as to not obscure the details of the system architecture.

One or more content items included in the content store210include content for presentation to a user and a bid amount. The content is text, image, audio, video, or any other suitable data presented to a user. In various embodiments, the content also specifies a page of content. For example, a content item includes a landing page specifying a network address of a page of content to which a user is directed when the content item is accessed. The bid amount is included in a content item by a user and is used to determine an expected value, such as monetary compensation, provided by an advertiser to the online system140if content in the content item is presented to a user, if the content in the content item receives a user interaction when presented, or if any suitable condition is satisfied when content in the content item is presented to a user. For example, the bid amount included in a content item specifies a monetary amount that the online system140receives from a user who provided the content item to the online system140if content in the content item is displayed. In some embodiments, the expected value to the online system140of presenting the content from the content item may be determined by multiplying the bid amount by a probability of the content of the content item being accessed by a user.

In various embodiments, a content item includes various components capable of being identified and retrieved by the online system140. Example components of a content item include: a title, text data, image data, audio data, video data, a landing page, a user associated with the content item, or any other suitable information. The online system140may retrieve one or more specific components of a content item for presentation in some embodiments. For example, the online system140may identify a title and an image from a content item and provide the title and the image for presentation rather than the content item in its entirety.

Various content items may include an objective identifying an interaction that a user associated with a content item desires other users to perform when presented with content included in the content item. Example objectives include: installing an application associated with a content item, indicating a preference for a content item, sharing a content item with other users, interacting with an object associated with a content item, or performing any other suitable interaction. As content from a content item is presented to online system users, the online system140logs interactions between users presented with the content item or with objects associated with the content item. Additionally, the online system140receives compensation from a user associated with content item as online system users perform interactions with a content item that satisfy the objective included in the content item.

Additionally, a content item may include one or more targeting criteria specified by the user who provided the content item to the online system140. Targeting criteria included in a content item request specify one or more characteristics of users eligible to be presented with the content item. For example, targeting criteria are used to identify users having user profile information, edges, or actions satisfying at least one of the targeting criteria. Hence, targeting criteria allow a user to identify users having specific characteristics, simplifying subsequent distribution of content to different users.

In one embodiment, targeting criteria may specify actions or types of connections between a user and another user or object of the online system140. Targeting criteria may also specify interactions between a user and objects performed external to the online system140, such as on a third party system130. For example, targeting criteria identifies users that have taken a particular action, such as sent a message to another user, used an application, joined a group, left a group, joined an event, generated an event description, purchased or reviewed a product or service using an online marketplace, requested information from a third party system130, installed an application, or performed any other suitable action. Including actions in targeting criteria allows users to further refine users eligible to be presented with content items. As another example, targeting criteria identifies users having a connection to another user or object or having a particular type of connection to another user or object.

The content selection module235selects one or more content items for communication to a client device110to be presented to a user. Content items eligible for presentation to the user are retrieved from the content store210or from another source by the content selection module235, which selects one or more of the content items for presentation to the viewing user. A content item eligible for presentation to the user is a content item associated with at least a threshold number of targeting criteria satisfied by characteristics of the user or is a content item that is not associated with targeting criteria. In various embodiments, the content selection module235includes content items eligible for presentation to the user in one or more selection processes, which identify a set of content items for presentation to the user. For example, the content selection module235determines measures of relevance of various content items to the user based on characteristics associated with the user by the online system140and based on the user's affinity for different content items. Based on the measures of relevance, the content selection module235selects content items for presentation to the user. As an additional example, the content selection module235selects content items having the highest measures of relevance or having at least a threshold measure of relevance for presentation to the user. Alternatively, the content selection module235ranks content items based on their associated measures of relevance and selects content items having the highest positions in the ranking or having at least a threshold position in the ranking for presentation to the user.

Content items eligible for presentation to the user may include content items associated with bid amounts. The content selection module235uses the bid amounts associated with content items when selecting content for presentation to the user. In various embodiments, the content selection module235determines an expected value associated with various content items based on their bid amounts and selects content items associated with a maximum expected value or associated with at least a threshold expected value for presentation. An expected value associated with a content item represents an expected amount of compensation to the online system140for presenting the content item. For example, the expected value associated with a content item is a product of the bid amount and a likelihood of the user interacting with the content item.

In some embodiments, the interaction of the user with the content item includes sending messages through a messaging application. That is, the content item may redirect the user to a messaging application to initiate a messaging thread using the messaging application. For example, clicking a content item can cause a messaging application installed in a mobile device of a user to load and open a new messaging thread between the user and the advertiser.

For content items having a specified interaction of a communication between the user and an advertiser through a messaging app, a compatibility module245of the content selection module determines a compatibility score between a user and an advertisement including a link to a messaging application based on one or more sets of criteria. The compatibility module245can evaluate information stored by the user profile store205, edge store225, and action log220to determine a likelihood of user interaction with an advertiser (e.g., a third party system130) in the messaging application. In some embodiments, the compatibility score is a likelihood of deep conversation. Described herein, deep conversation is defined as an exchange of three or more messages between two or more entities associated with the online system140(e.g., a user and an advertiser). The compatibility module245is described in greater detail below in relation toFIG. 3.

The machine learning module255applies machine learning techniques to train the compatibility module245configured for determining a compatibility score between an advertisement and a user. The machine learning module255uses training data and features stored in the training data store260, user profile store205, content store210, edge store225and action log220. In one embodiment, the machine learning module255retrieves, from the training data store260, content items (e.g., advertisements) to train the compatibility module245using various machine learning techniques.

In an embodiment, the compatibility module245generates compatibility scores for content items for a user of the online system140when a request to provide a content item to the user is received. The compatibility module245generates the compatibility scores based on user compatibility with a messaging application, an advertiser, an advertisement, or some combination thereof. In one embodiment, the machine learning module255periodically retrains (e.g., using the training data store260) the compatibility module245at a determined frequency of time. The frequency of time may be based on information from the client device110such as the average time a user engages with news feed stories of the online system140on the client device110each day.

In an embodiment where the compatibility score indicates a likelihood of deep conversation, as part of the generation of the compatibility module245, the machine learning module255forms a training set of advertisements, each advertisement including a link to a messaging application, by identifying a positive training set of advertisements that have yielded deep conversation between the user and an advertiser and, in some embodiments, forms a negative training set of content items that do not yield deep conversation. In alternative embodiments, the machine learning module255trains the compatibility module245using training sets of advertisements that have a compatibility score above or below a threshold.

The machine learning module255extracts feature values from the advertisements of the training set, the features being variables deemed potentially relevant to whether or not the advertisements yield deep conversation. Specifically, the feature values extracted by the machine learning module255include which messaging application is included in the advertisement, the type of advertisement, the type of creatives in the advertisement, the advertiser associated with the advertisement, and any other suitable features. An ordered list of the features for a content item is herein referred to as the feature vector for the advertisement. In one embodiment, the machine learning module255applies dimensionality reduction (e.g., via linear discriminant analysis (LDA), principle component analysis (PCA), or the like) to reduce the amount of data in the feature vectors for advertisement to a smaller, more representative set of data.

The machine learning module255uses supervised machine learning to train the compatibility module245with the feature vectors of the positive training set and the negative training set serving as the inputs. Different machine learning techniques—such as linear support vector machine (linear SVM), boosting for other algorithms (e.g., AdaBoost), neural networks, logistic regression, naïve Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, or boosted stumps—may be used in different embodiments. The machine learning module255, when applied to the feature vector extracted from an advertisement, outputs an indication of whether the content item has the property in question, such as a Boolean yes/no estimate, or a scalar value representing a probability.

In some embodiments, a validation set is formed of additional advertisements, other than those in the training sets, which have already been determined to generate deep conversation. The machine learning module255applies the trained validation compatibility module245to the advertisements of the validation set to quantify the accuracy of the compatibility module245. Common metrics applied in accuracy measurement include: Precision=TP/(TP+FP) and Recall=TP/(TP+FN), where precision is how many the compatibility module245correctly predicted (TP or true positives) out of the total it predicted (TP+FP or false positives), and recall is how many the compatibility module245correctly predicted (TP) out of the total number of advertisements that did yield deep conversation (TP+FN or false negatives). The F score (F-score=2*PR/(P+R)) unifies precision and recall into a single measure. In one embodiment, the machine learning module255iteratively re-trains the compatibility module245until the occurrence of a stopping condition, such as the accuracy measurement indication that the model is sufficiently accurate, or a number of training rounds having taken place.

The content selection module235may rank content items based on their associated bid amounts and select content items having at least a threshold position in the ranking for presentation to the user. In some embodiments, the content selection module235ranks both content items not associated with bid amounts and content items associated with bid amounts in a unified ranking based on bid amounts and measures of relevance associated with content items. Based on the unified ranking, the content selection module235selects content for presentation to the user. Selecting content items associated with bid amounts and content items not associated with bid amounts through a unified ranking is further described in U.S. patent application Ser. No. 13/545,266, filed on Jul. 10, 2012, which is hereby incorporated by reference in its entirety.

For content items having a specified interaction of a communication between the user and an advertiser through a messaging app, a subsidy module250of the content selection module235determines a subsidy based on the compatibility score determined by the compatibility module245. The subsidy may be a function of the compatibility score. In one embodiment, the subsidy module250adjusts the bid price upwards or downwards during an ad campaign if the ad campaign is paced. A paced ad campaign increases or decreases the bid for the ad campaign to spend the budget for the campaign evenly during the length of the campaign. The subsidy module250further determines an auction price for the ad. The auction for the ad is the determined bid price plus the determined amount of subsidy. The auction price is used when comparing the ad to other ads for an impression opportunity. For example, for the impression opportunity, the ads are compared based in part on their auction prices to select an ad to present. Advertisement bidding is further described in U.S. patent application Ser. No. 13/294,094 entitled “Budget-Based Bidding in a Social Networking System,” filed on Nov. 10, 2011, and is incorporated by reference in its entirety. In one embodiment, the subsidy module250increases the likelihood that an advertisement is selected for presentation to a user.

For example, the content selection module235receives a request to present a feed of content to a user of the online system140. The feed may include one or more content items associated with bid amounts and other content items, such as stories describing actions associated with other online system users connected to the user, which are not associated with bid amounts. The content selection module235accesses one or more of the user profile store205, the content store210, the action log220, and the edge store225to retrieve information about the user. For example, information describing actions associated with other users connected to the user or other data associated with users connected to the user are retrieved. Content items from the content store210are retrieved and analyzed by the content selection module235to identify candidate content items eligible for presentation to the user. For example, content items associated with users who are not connected to the user or stories associated with users for whom the user has less than a threshold affinity are discarded as candidate content items. Based on various criteria, the content selection module235selects one or more of the content items identified as candidate content items for presentation to the identified user. The selected content items are included in a feed of content that is presented to the user. For example, the feed of content includes at least a threshold number of content items describing actions associated with users connected to the user via the online system140.

In various embodiments, the content selection module235presents content to a user through a newsfeed including a plurality of content items selected for presentation to the user. One or more content items may also be included in the feed. An example feed is shown inFIG. 4Aand described in greater detail below. The content selection module235may also determine the order in which selected content items are presented via the feed. For example, the content selection module235orders content items in the feed based on likelihoods of the user interacting with various content items.

The action log220may be used by the online system140to track user actions on the online system140, as well as actions on third party systems130that communicate information to the online system140. Users may interact with various objects on the online system140, and information describing these interactions is stored in the action log220. Examples of interactions with objects include: commenting on posts, sharing links, checking-in to physical locations via a client device110, accessing content items, and any other suitable interactions. Additional examples of interactions with objects on the online system140that are included in the action log220include: commenting on a photo album, communicating with a user, establishing a connection with an object, joining an event, joining a group, creating an event, authorizing an application, using an application, expressing a preference for an object (“liking” the object), and engaging in a transaction. Additionally, the action log220may record a user's interactions with advertisements on the online system140as well as with other applications operating on the online system140. In some embodiments, data from the action log220is used to infer interests or preferences of a user, augmenting the interests included in the user's user profile and allowing a more complete understanding of user preferences.

In the embodiment ofFIG. 2, the action log220may store information related to historical interaction of the user with one or more applications accessed by the user via a client device110. In particular, the action log220can store information related to user interaction with one or more messaging applications (e.g., Facebook Messenger, Whatsapp, Instagram). The action log220can store information related to a number of times the user accesses a messaging application (e.g., per day, per week), an amount of time spent on a messaging application, a number of messages exchanged within a messaging application (e.g., per day, per hour), other users that the user interacts with via a messaging application, and any other additional details related to user interaction with a messaging application.

In some embodiments, the action log220receives the information directly from the messaging application. That is, the messaging application sends a notification to the online system140when a user accesses the messaging application. For privacy reasons, the online system140may not keep track of the people user sent messages to or the messages sent by the user.

In some embodiments, the messages sent through the messaging application are encrypted. Here, the messaging application or the online system140can determine which user sent the message and when the message was sent (e.g., by identifying the source of the message and the time the message was received), but the messaging application and the online system140are not able to decrypt the messages. Instead, the messaging application routes the message to the recipient, who then is capable of decrypting the message. As such, the online system140is able to determine that the user has interacted with the messaging application but the sensitive information is kept out of the reach of the online system140.

In one embodiment, the online system140uses a tracking pixel or piece of HTML code placed by the third party system130on third party websites to monitor users visiting the websites that have not opted out of tracking. A tracking pixel might be included on various pages, including on a product page describing a product, on a shopping cart page that the user visits upon putting something into a shopping cart, on a checkout page that the user visits to checkout and purchase a product, etc. For example, a tracking pixel results in a transparent 1×1 image, an iframe, or other suitable object being created for third party pages. When a user's browser loads a page having the tracking pixel, the tracking pixel results in the user's browser attempting to retrieve the content for that pixel, and the browser contacts the online system140to retrieve the content. The request sent to the online system140, however, actually includes various data about the user's actions taken on the third party website. The third party website can control what data is sent to the online system140. For example, the third party system130may include information about the page the user is loading (e.g., is it a product page, a shopping cart page, a checkout page, etc.), about information on the page or about a product on the page of interest to the user (e.g., the SKU number of the product, the color, the size, the style, the current price, any discounts offered, the number of products requested, etc.), about the user (e.g., the third party's user identifier (UID) for the user, contact information for the user, etc.), and other data. In some embodiments, a cookie set by the online system140can also be retrieved by the online system, which can include various data about the user, such as the online systems' UID for the user, information about the client device and the browser, such as the Internet Protocol (IP) address of the client device, among other data. Tracking can also be performed on mobile applications of content providers by using a software development kit (SDK) of the online system or via an application programming interface (API) of the online system to track events (e.g., purchases) that occur by users on the content provider's app that are reported to the online system.

The online system140can then use this data received about the user to serve better content to the user in the future (e.g., since the online system has further data about products purchased or browsed that might be of interest), and can also use this in retargeting of that product to the user (e.g., where the user went to the product page on the third party site by clicking on an of the third party presented to the user in the online system). The online system can also use this data in conversion tracking and reporting results of advertising campaigns to the third party. For example, if the third party has spent money at the online system140to have the online system140serve ads for its products, and a particular user views an ad on the online system and then purchases the product advertised (possibly at some point later, and possibly on a different device), the online system140can link the purchase/conversion to the ad originally shown on the online system140. Thus, the online system140can include this data in its reporting to the third party system of how many conversions the ad campaign received.

In one embodiment, the edge store225stores information describing connections between users and other objects on the online system140as edges. Some edges may be defined by users, allowing users to specify their relationships with other users. For example, users may generate edges with other users that parallel the users' real-life relationships, such as friends, co-workers, partners, and so forth. Other edges are generated when users interact with objects in the online system140, such as expressing interest in a page on the online system140, sharing a link with other users of the online system140, and commenting on posts made by other users of the online system140. Edges may connect two users who are connections in a social network, or may connect a user with an object in the system. In one embodiment, the nodes and edges form a complex social network of connections indicating how users are related or connected to each other (e.g., one user accepted a friend request from another user to become connections in the social network) and how a user is connected to an object due to the user interacting with the object in some manner (e.g., “liking” a page object, joining an event object or a group object, etc.). Objects can also be connected to each other based on the objects being related or having some interaction between them.

The ad request store230stores information describing ad campaigns received from one or more third party systems130. Each ad campaign includes one or more ads described by ad requests, so the ad request store230also includes information describing various ad requests comprising ad campaigns. An ad campaign is associated with a set of delivery parameters (e.g., a budget, impression goal, a target average price paid, etc.) Additionally, an ad campaign may be associated with one or more objectives and a duration.

A target average price paid represents an amount of compensation an advertiser desires to pay the online system140per action associated with ads in the ad campaign. For example, the target average price paid specifies an average amount of compensation the advertiser seeks to provide the online system140for each presentation of an ad in the ad campaign to an online system user (an “impression”) or for each conversion event associated with an ad from the ad campaign presented to an online system user (e.g., an interaction with an ad, an interaction with an object associated with an ad, etc.).

The budget represents a total amount of revenue an advertiser has allocated to compensate the online system140for presenting ads in an ad campaign. An amount is deducted from the budget each time an ad from an ad campaign is presented and an action associated with the ad by an advertiser occurs (e.g., an impression of an ad, a conversion event associated with the ad). When the budget is exhausted, the online system140ceases presentation of ads from an ad campaign associated with the budget.

An objective associated with an ad campaign describes one or more goals for actions associated with the ad campaign. For example, an objective specifies a total number of impressions of ads in the ad campaign to online system users (an “impression objective”), specifies a total number of unique online system users to be presented with at least one ad from the ad campaign (a “reach objective”), a number of times an ad from the ad campaign is presented to an online system user (a “frequency objective”), or any other suitable criteria. Additionally, the duration associated with the ad campaign specifies a time interval during which ads from the ad campaign are presented to online system users. For example, if the duration of an ad campaign is 30 days, ads included in the ad campaign are presented to online system users for 30 days after the ad campaign is provide to the online system140or until the budget is exhausted. In some embodiments, the advertiser specifies a start date for the ad campaign, so the duration is measured from the specified start date.

Method of Determining a Compatibility Score

FIG. 3illustrates a method of determining a compatibility score, in accordance with one embodiment. The steps shown inFIG. 3are performed by one or more components of the online system140shown inFIG. 2(e.g., action logger215, content selection module235). The steps can be performed in a different order, or may be performed by different entities than described herein. Additionally, the method300can include different or additional steps than described below.

The online system140receives310an advertisement (“ad”) with a respective bid price. The online system140may receive the advertisement from a third party system130(e.g., an advertiser). The advertisement can include one or more creatives, where the creatives may include text, images, hyperlinks, videos, interactive content, and/or any other suitable content as shown below inFIG. 4A. In one embodiment, the advertisement includes a link configured to direct a user to a messaging application. The messaging application included in the advertisement is selected by the third party system130. In other embodiments, the messaging application may be selected by the online system140, by the user of the client device110, or by any other suitable component. In some embodiments, the third party system130identifies the messaging applications that are supported by the third party system130, and the online system140selects one of the supported messaging applications before the advertisement is provided to the user based on information associated with the user. The link included in the advertisement is configured to initiate a direct message between the user and the advertiser in the messaging application. The direct message allows the advertiser to engage in a discussion with an individual user, promoting user engagement and enhancing user experience. The messaging application may be an application installed on a client device110, a messaging window in a web browser, a pop-up window, or any other suitable interface. In other embodiments, the messaging application may be an application associated with a particular online system140.

The online system140receives320an advertisement request from a client device110. A user viewing a content feed on the client device110, described below in relation toFIG. 4A, may generate a request for an advertisement as the user scrolls through the feed. The online system140can use information stored in the user profile store205, the action log220, and the edge store225to identify one or more advertisements for presentation to the user in response to the advertisement request. Each advertisement may include a respective bid amount, as described above in relation toFIG. 2. In some embodiments, the online system140may determine whether the user satisfies targeting criteria associated with one or more advertisements.

The compatibility module245determines330a compatibility score between the user and an advertisement based on one or more sets of criteria. The compatibility score can represent a likelihood of user interaction with the advertiser in the messaging application. In one embodiment, as described above, the compatibility score represents a likelihood of deep conversation. The compatibility module245can determine the compatibility score based on information stored in the edge store225, action log220, and the user profile store205, described below.

In on embodiment, the compatibility module245first determines whether the messaging application is installed on the client device110of the user. If the messaging application is not installed on the client device110, the compatibility module245may determine the compatibility score is zero (i.e. likelihood of user interaction with the messaging application is zero). If the application is installed on the client device110, the compatibility module245determines the compatibility score based on one or more sets of criteria. A first set of criteria indicates a level of compatibility between the user and the messaging application included in the advertisement. The first set of criteria can include historical trends of user interaction with the messaging application (e.g., time spent on the messaging application, a number of messages exchanged on the messaging application over a period of time), characteristics of the user (e.g., geographic location of a user, gender), interactions of additional users of the online system140with the messaging application, user feedback (e.g., user rating of the application), etc. The first set of criteria can include any additional information related to a likelihood of user interaction with a messaging application. In one embodiment, the compatibility score is a likelihood of deep conversation, i.e., a likelihood that a user and an advertiser will exchange at least three messages in the direct message interface. As such, the first set of criteria can include historical trends of deep conversation in the messaging application (e.g., between the user and other users, between the user and an advertiser, between other users associated with the user and the advertiser, etc.). In one embodiment, the compatibility module245weights the first set of criteria to generate a first weighted score.

The compatibility can additionally or alternatively analyze a second set of criteria may indicating a level of compatibility between the user and the advertiser. The second set of criteria can include historical interaction between the user and the advertiser (e.g., a number of times the user has interacted with advertisements provided by the advertiser), explicit user preference for the advertiser (e.g., the user has liked advertiser's page on the online system140), implicit user preference for the advertiser, user purchasing history related to the advertiser, etc. The criteria can include any additional features indicating preferences of the user related to the advertiser. In one embodiment, the computability module245weights the second set of criteria to generate a second weighted score.

The compatibility module245can evaluate a third set of criteria indicating a level of compatibility between the user and the advertisement. The third set of criteria can include determining whether the user satisfies targeting criteria (e.g., age, gender, location) associated with the advertisement, historical interaction between the user and different types of advertisements (e.g., videos, interactive content, images), etc. The third set of criteria can include any other information related to a likelihood of a user interacting with the advertisement. In one embodiment, the compatibility module245weights the third set of criteria to generate a third weighted score.

The compatibility module245can generate a compatibility score based on the first set of criteria, the second set of criteria, the third set of criteria, or some combination thereof. In some embodiments, the compatibility module245generates a weighted compatibility score based on the one or more sets of criteria or one or more of the first, second, and third weighted scores. In other embodiments, the compatibility module245can generate a compatibility score based on additional criteria not described herein. In some embodiments, the compatibility module345compares the compatibility score to a threshold to determine whether deep conversation is likely or unlikely. For example, if the compatibility score is greater than the threshold, the compatibility module245determines that deep conversation is likely, and if the compatibility score is less than the threshold, deep conversation is unlikely. In other embodiments, the compatibility score is a likelihood of deep conversation (e.g., on a non-binary scale).

The subsidy module250determines340a subsidy for the ad based on the compatibility score. The online system140subsidizes the ad to promote content that may be desirable for users of the online system140and encourage engagement between the user and the advertiser (e.g., in the messaging application). The subsidy increases the advertiser's chance of winning an ad auction to present an ad to present such content that may be desirable for users to view. For example, for a higher compatibility score, the subsidy module250determines a higher subsidy, such that the advertisement is more likely to be selected for presentation to the user. For lower compatibility scores, the subsidy module250may determine a smaller subsidy or no subsidy at all. In other embodiments, the subsidy may act as a reduction of the cost incurred to the third party system130. The online system140can determine the subsidy as a function of the compatibility score. In one embodiment, the subsidy is the monetary value associated with the user multiplied by the function of the compatibility score. The monetary value of the user may be related to user interaction with the online system140(e.g., click rate).

The online system140can determine340an auction price by adjusting the bid price with the determined subsidy. In some embodiments, the online system140computes the auction price by adding the bid price and the subsidy. If the compatibility score is zero, the auction price may be equal to the bid price. The online system140includes350the advertisement and the determined auction price in one or more selection processors for selecting advertisements. The selection processes may select one or more advertisements with the highest auction price. Thus, reducing the auction price increases the likelihood that an ad for which a user is more likely to interact with an advertiser in a messaging application is selected, enhancing user and advertiser interaction. In one embodiment, the online system140selects the advertisement including the link to the messaging application for presentation to the user.

The online system140may track the user response (e.g., user interaction with the advertiser in the messaging application) and add the advertisement to one or more training sets to train the compatibility module245, as described above in relation toFIG. 2. The online system140tracks the user response in a manner that protects privacy of the user (e.g., such that the content of the messages is not available the online system140). The messages between the user and the advertiser in the messaging application are encrypted such that the online system140cannot decipher the information exchanged in the messages. The online system140may be able to determine when a message was sent by the user or the advertiser (e.g., by a notification from the messaging application), but the online system140cannot see the content of the messages. Thus, the online system140can count a number of messages exchanged between the user and the advertiser to determine whether deep conversation occurred without breaching user privacy.

The online system140may use the tracked user response information to determine whether a deep conversation occurred. For example, the tracked user response information is used to further train the compatibility module245. If the online system140determines that a deep conversation occurred, the advertisement impression is stored in the training data store260as a positive training sample. Conversely, if the online system140determines that a deep conversation did not occur (e.g., if the online system140does not receive at least three encrypted messages from the user to the advertiser within a specified time period), the advertisement impression is stored in the training data store260as a negative training sample.

In some embodiments, the online system140identifies whether three rounds of back and forth messages were sent. That is, the online system140determines if the user sent a message to the advertiser after the advertiser had sent a message to the user three times. For instance, the online system140may increase the value of a counter when the online system determines that a user has sent a message to the advertiser after the advertiser had sent a message to the user. If the counter for a conversation reaches a value of three or more within a predefined time period, the online system140identifies the conversation as a deep conversation.

In some embodiments, the tracked user response information is further used to determine a payment amount from the advertiser to the online system140for presenting the advertisement to the user. For example, the online system may only charge the advertiser if a deep conversation occurred between a user and the advertiser. The tracked user response information is then used to determine if the advertiser will be charged for a specific ad impression. If the online system140determines that a deep conversation occurred, the advertiser is charged a fee that is determined based on the bid amount specified by the advertiser when creating the ad campaign.

In some embodiments, to increase the privacy of the user, the online system140only stores an identification of whether an ad impression resulted in a deep conversation. The online system140may not store the number of messages or the time the messages were received. Moreover, since the online system140does not have access to the content of the messages, the online system140does not store any information regarding the content of the messages sent by the user or by the advertiser.

Example User Interface

FIG. 4Aillustrates an example content feed, in accordance with one or more embodiments. The online system140provides one or more content items (e.g.,405A,405B,405C,405D) to a user via a content feed400. The content items405can be sponsored content (e.g., advertisements), organic content, or any other type of content. The content items405may be provided by a third party system130, another user, may be generated by the online system140, or provided by any other suitable source. In the embodiment ofFIG. 4A, a third party system130(e.g., an advertiser) provides the content item405C (e.g., an advertisement). The content item405C includes a plurality of creatives410,415, and420. The creative410includes an image and text associated with vacation. The creative415includes an indication that the user can chat with the advertiser in a messaging application (e.g., Messenger). The creative420includes the logo of the messaging application (e.g., Messenger logo). In some embodiments, one or more of the creatives includes a hyperlink to an external application. For example, responsive to the user clicking on the creative420, the content item405C can direct the user to a direct message interface in the messaging application associated with the logo. In some embodiments, the link is a deep link that opens the messaging application in a specific state to enable the user to directly communicate with the third party system. In other embodiments, the user may be directed to a direct message interface responsive to selecting creative410or415, or the user can select anywhere within the content item405C.

Responsive to interacting with the content item405C (e.g., clicking on the content item405C), the user may be directed to a direct message interface illustrated inFIG. 4B. The third party system130(e.g., Gotham Airlines) can initiate a conversation between the user the advertiser. In the example ofFIG. 4B, the third party system130sends a message including one or more creatives (e.g., creative410) of the content item405C. The third party system130can send additional content or text with the creative410, such as the message430a. The message430arequests information from the user. The user may respond with a message435aproviding the information to the advertiser. In other embodiments, the user may respond with a message that does not satisfy the information requested by the third party system130. The third party system130can follow up with another message to continue engaging the user. Alternatively, the third party system130can provide a message430bwith a link to a website associated with the third party system130. The link may be configured to direct the user to a browser where a user can complete a purchase, browse an advertiser's website, etc. Alternatively, responsive to the user selecting a return button440a, the user can be directed back to the content feed400. Alternatively, responsive to the user selecting a close button440b, the user can be directed to another interface in the messaging application. In the embodiment shown, the success of the advertisement may be measured based on a level of user interaction with the advertiser in the messaging application (e.g., how many messages are exchanged, whether the user clicks on the provided link, whether the user completes a purchase on the link, whether the user closes the messaging application, etc.).

In a second example, responsive to interacting with the content item405C, the third party system130sends a message455ato a user prompting the user to provide information. The third party system130and the user can exchange a series of messages455and460. To protect the privacy of the user, the messages sent between the user and the third party system are encrypted. As such, the online system in not able to access the content of the messages. The third party system130can respond to the user with a series of preset messages or may respond based on user responses. In one embodiment, shown inFIG. 4C, the success of the advertisement is based on whether deep conversation occurred. In the embodiment ofFIG. 4C, deep conversation occurred (e.g., messages445a-455cand460a-460c). The online system140can determine whether deep conversation occurred using cryptic versions of the messages exchanged between the user and the advertiser, such that user privacy is protected. That is, although the messages are encrypted and the online system is unable to access the content of the messages, the online system can track the occurrence of a deep conversation by identifying the presence or absence of messages directed to each party. As such, the online system140may be able to count the number of messages exchanged between the advertiser and the user without accessing the message content.

The user interaction with the advertiser in the messaging application in the examples ofFIG. 4BandFIG. 4Ccan be used to train the compatibility module245. For example, where the compatibility score is a likelihood of deep conversation, in the example ofFIG. 4B, the content item405C may be included in a negative training set (i.e., because there was no deep conversation). Alternatively, the example ofFIG. 4C, the content item405C may be included in a positive training set (i.e., because the advertisement yielded deep conversation). In other embodiments, the advertisement may be included in a positive or negative training set based on whether the user interacts with the advertiser, whether the user responds to a message from the advertiser, whether the user clicks on a link to the advertiser's website, etc. The online system140can determine whether to include the content item in one or more training sets based on any suitable criteria. The online system140can store the training sets in the training data store260.

Conclusion