Model compression for selecting content

To present one or more content items to users of an online system, the online system identifies a content evaluation pipeline including an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item. The content evaluation pipeline selects a decreasing number of content items, from each stage of the order, according to the order of the stages. The online system optimizes the selection of content items selected at the plurality of stages of the content evaluation pipeline by training the computer models to predict content selection values that the subsequent model would generate for a content items in a training data set and content items that the subsequent model would select for input to the next stage of the content evaluation pipeline.

BACKGROUND

This disclosure relates generally to presenting content to users of an online system, and more specifically to generating a feed of content for presentation to a user of the online system.

Online systems, such as social networking systems, allow users to connect to and to communicate with other users of the online system. Users may create profiles on an online system that are tied to their identities and include information about the users, such as interests and demographic information. The users may be individuals or entities such as corporations or charities. Online systems allow users to easily communicate and to share content with other online system users by providing content to an online system for presentation to other users. Content provided to an online system by a user may be declarative information provided by a user, status updates, check-ins to locations, images, photographs, videos, text data, or any other information a user wishes to share with additional users of the online system. An online system may also generate content for presentation to a user, such as content describing actions taken by other users on the online system.

Additionally, many online systems commonly allow publishing users (e.g., businesses) to sponsor presentation of content on an online system to gain public attention for a user’s products or services or to persuade other users to take an action regarding the publishing user’s products or services. Many online systems receive compensation from a publishing user for presenting online system users with certain types of content provided by the publishing user. Frequently, online systems charge a publishing user for each presentation of content to an online system user or for each interaction with content by an online system user. For example, an online system receives compensation from a publishing user each time a content item provided by the publishing user is displayed to another user on the online system or each time another user is presented with a content item on the online system and interacts with the content item (e.g., selects a link included in the content item), or each time another user performs another action after being presented with the content item.

However, selecting relevant content for a particular user from among a very large (e.g., 10,000) number of content items may be challenging to perform in an effective and accurate manner. The effectiveness of this computation may depend on the ability of models in a content evaluation pipeline to accurately select content items for input to the subsequent model. Since the complexities of these models may differ, filtering a large number of content items for those that are likely to be of high value for a given user is challenging. A less complex model at an early stage in the content evaluation pipeline might filter out a content item that would have been selected by a more complex model at a later stage in the pipeline, resulting in loss of relevant content to select between the stages of the content selection process and in the ultimate selection of content items for display to the user.

SUMMARY

To present one or more content to users of an online system, the online system identifies a content evaluation pipeline configured to select one or more content items that the users are more likely to interact with. The online system includes an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item. The content evaluation pipeline selects a decreasing number of content items ineach stage of the pipeline to continue to the next stage. One example content evaluationpipeline has three stages. The first stage of the content evaluation pipeline receives 10,000 content items, and a first stage model ranks the 10,000 content items based on the evaluated likelihood of user interaction with each of the 10,000 content items, and selects top 800 content items as an input for a second stage model. The second model evaluates these 800 content items to subsequently outputs 200 content items, and a third stage model receives the 200 content items as an input and outputs 3 content items for display to the user.

To optimize the content evaluation pipeline and select the most high-value content items for a given user, the online system trains the models to mimic the output of the subsequent model in the pipeline. A pipeline training module generates a first training set of content items for which a given user satisfies targeting criteria and inputs the first training set to the second stage model for evaluation. For each of the content items in the first training set, the second stage model generates a content selection value or range of values representing a likelihood that the given user will interact with the content items and an amount of compensation that the online system would receive as a result of the interaction. The content selection values are output by the second stage model, along with an indication of which of the content items in the first training set would be selected (by the second stage model) for evaluation by the third stage model. The first stage model is then trained with the content selection values in the first training set, such that the first stage model learns to output values similar to those of the second stage model.

The pipeline training module similarly trains the second stage model to predict the content selection value or range of values that the third stage model would calculate for each content item and which three content items the third stage model would select for display to the user. Content items in a second training set are input to the third stage model, which outputs content selection values or ranges of values and an indication of which content items from the second training set would be selected for output to the user. Responsive to receiving the output of the third stage model, the pipeline training module trains the second stage model with the content selection values in the second training set to mimic the output of the third stage model. In other embodiments, the pipeline training module trains the models in the content evaluation pipeline from most to least complex (i.e., trains the third stage model, then trains the second stage model based on predicted values from the trained third stage model, then trains the first stage model based on predicted values from the trained second stage model.

DETAILED DESCRIPTION

An online system uses a content evaluation pipeline to select one or more content items that a user of an online system is more likely to interact with. The content evaluation pipeline includes an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item. The content evaluation pipeline selects a decreasing number of content items, from each stage of the order, according to the order of the stages in the order.

FIG.1Ais a block diagram of an environment where a content evaluation pipeline100A operates, in accordance with an embodiment. In the embodiment ofFIG.1A, a feed display110has three available slots (e.g.120A,120B, and120C) to generate content opportunities125. In this example, the content store140provides 10,000 content items145(e.g., social networking content items and advertisements) to the content evaluation pipeline100A. The content evaluation pipeline100A ultimately selects three content items170A,170B and170C for the three available slots in the feed display110. The content evaluation pipeline100A includes three stages, each having a computer model (e.g., a first stage model130A, a second stage model150, and a third stage model160) for evaluating a likelihood of a user interaction with a content item. The content evaluation pipeline100A selects a decreasing number of content items, from each stage of the order, according to the order of the stages in the order. In this example, the first stage model130A receives the 10,000 content items145from the content store140and ranks the 10,000 content items based on the evaluated likelihood of user interaction with each of the 10,000 content items, and selects top800content items135A as an input for the second stage. The top one content item of the top800content items135A is a content item that the user is most likely to interact with. The second stage model150uses the800content items135A as an input and selects200content items155A as an input for the third stage model160. The third stage model160selects top three content items170A,170B and170C for presenting in the feed display110.

FIG.1Bis a block diagram of an environment where a content evaluation pipeline100B operates, in accordance with another embodiment. In this example, compared with the content evaluation pipeline100A, the content evaluation pipeline100B includes an additional first stage model130B. In this example, the different first stage models (e.g.,130A and130B) may represent different models to be used based on various selection characteristics, such as a type of content, user characteristic, and interaction associated with the content. For these different characteristics, different models may be applied in the first stage to evaluate the content items for the second stage. In this example, different user characteristics may determine which model is used in the first stage. Thus, when the user characteristic matches first stage model130B, it is used rather than first stage model130A. The first stage model130B receives the 10,000 content items145and selects 800 content items135B. The content evaluation pipeline100B selects the 800 content items as an input for the second stage model150using the first stage model130B. The second stage model150selects200content items155B as an input for the third stage model160that selects three content items175A,175B, and175C for presenting in the feed display110. These different first stage models may represent different computing complexities and associated accuracy of the models. As a result, the effective pipeline used for evaluating a particular content item or for a particular user may differ. These different pipelines may provide different accuracy and require different computational requirements. Though shown here as a separate model for the user in a given stage, many different modifications to the pipeline may be implemented as discussed below to adjust the effective pipeline (and thereby computational requirements) for different selection characteristics.

FIG.2is a block diagram of a system environment200where an online system240operates, in accordance with an embodiment. The system environment200shown byFIG.2comprises one or more client devices210, a network220, one or more external systems230, and the online system240. In alternative configurations, different and/or additional components may be included in the system environment200. For example, the online system240is a social networking system, a content sharing network, or another system providing content to users. The embodiments described herein can be adapted to online systems that are not social networking systems.

The client devices210are one or more computing devices capable of receiving user input as well as transmitting and/or receiving data via the network220. In one embodiment, a client device210is a conventional computer system, such as a desktop or a laptop computer. Alternatively, a client device210may be a device having computer functionality, such as a personal digital assistant (PDA), a mobile telephone, a smartphone, a smartwatch, or another suitable device. A client device210is configured to communicate via the network220. In one embodiment, a client device210executes an application allowing a user of the client device210to interact with the online system240. For example, a client device210executes a browser application to enable interaction between the client device210and the online system240via the network220. In another embodiment, a client device210interacts with the online system240through an application programming interface (API) running on a native operating system of the client device210, such as IOS® or ANDROID™.

One or more external systems230may be coupled to the network220for communicating with the online system240, which is further described below in conjunction withFIG.2. In one embodiment, an external system230is an application provider communicating information describing applications for execution by a client device210or communicating data to client devices210for use by an application executing on the client device. In other embodiments, an external system230provides content or other information for presentation via a client device210. An external system230may also communicate information to the online system240, such as advertisements, content, or information about an application provided by the external system230.

Various external systems230provide content to users of the online system240. For example, an external system230maintains pages of content that users of the online system240may access through one or more applications executing on a client device210. The external system230may provide content items to the online system240identifying content provided by the online system240to notify users of the online system240of the content provided by the external system230. For example, a content item provided by the external system230to the online system240identifies a page of content provided by the online system240that specifies a network address for obtaining the page of content. If the online system240presents the content item to a user who subsequently accesses the content item via a client device210, the client device210obtains the page of content from the network address specified in the content item.

The online system240optimizes one or more content evaluation pipelines to select one or more content items that are likely to have high content selection values for a given user of the online system240. A content selection value represents how much compensation that the online system receives for presenting one or more content items selected by a content evaluation pipeline if the one or more content items are presented to a user, if the one or more content items receive a user interaction when presented, or if any suitable condition is satisfied when the one or more content items are presented to a user. For example, the content selection value is determined by a bid amount that specifies an amount of compensation the online system240receives from a publishing user associated with a content item, if the content item is displayed. Alternatively, the content selection value is determined by multiplying the bid amount by a prediction likelihood indicating that how likely a user will interact with a content item. Examples of content items include social networking content items (e.g., stories, photos, videos, and invitations), campaigns, and advertisements. A content evaluation pipeline uses stages to filter a very large number of content items to those that are likely to be a high content selection value for a given user. At each stage, the content evaluation pipeline evaluates, ranks, and then filers the very larger number of content items to proceed to the next stage to narrow candidate content items to a small number of content items that high content selection values.

If the online system240has a limited amount of compute power to effectively select content items, the fewer content items that move to the next stage, the less compute time value is required at the next stage. A compute time value represents how long the content evaluation pipeline takes to generate an output. While accounting for a complexity of the content evaluation pipeline (e.g., the more complex the content evaluation pipeline is, the higher accurate the content item selection is, and the more compute time is required), this creates a tradeoff between a complexity of the content evaluation pipeline and a compute time value. The online system optimizes the content evaluation pipelines to increase content selection values as further described below.

In the embodiment ofFIG.2, the online system240shown inFIG.2includes a user profile store242, a content store243, an action logger245, an action log250, an edge store225, a model store260, a content evaluation pipeline module265, a pipeline training module270, a newsfeed manager275, a content serving module280, and a web server285. In other embodiments, the online system240may 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.

Each user of the online system240is associated with a user profile, which is stored in the user profile store242. A user profile includes declarative information about the user that was explicitly shared by the user and may also include profile information inferred by the online system240. In one embodiment, a user profile includes multiple data fields, each describing one or more attributes of the corresponding user of the online system240. Examples of information stored in a user profile include biographic, demographic, and other types of descriptive information, such as work experience, educational history, gender, hobbies or preferences, location and the like. A user profile may also store other information provided by the user, for example, images or videos. In certain embodiments, images of users may be tagged with identification information of users of the online system240displayed in an image. A user profile in the user profile store242may also maintain references to actions by the corresponding user performed on content items in the content store243and stored in the action log250.

One or more content items included in the content store243include a creative, which is content for presentation to a user, and a bid amount. As used herein, a content item including a bid amount is referred to as a “sponsored content item,” while a content item (e.g., a social networking content item) that does not include a bid amount is referred to as an “organic content item.” The creative is text, image, audio, video, or any other suitable data presented to a user. In various embodiments, the creative also specifies a page of content.

Various sponsored content items may include an objective identifying an interaction that a user associated with a sponsored content item desires other users to perform when presented with content included in the sponsored content item. Example objectives include: installing an application associated with a sponsored content item, indicating a preference for a sponsored content item, sharing a sponsored content item with other users, interacting with an object associated with a sponsored content item, or performing any other suitable interaction. As content from a sponsored content item is presented to online system users, the online system240logs interactions between users presented with the sponsored content item or with objects associated with the sponsored content item. Additionally, the online system240receives compensation from a publishing user associated with a sponsored content item as online system users perform interactions with the sponsored content item that satisfy the objective included in the sponsored content item.

Additionally, a sponsored content item may include one or more targeting criteria specified by the publishing user who provided the sponsored content item to the online system240. Targeting criteria included in a sponsored content item request specify one or more characteristics of users eligible to be presented with the sponsored 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 publishing user to identify users having specific characteristics as eligible to be presented with content from a sponsored content item, 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 system240. Targeting criteria may also specify interactions between a user and objects performed external to the online system240, such as on an external system230. For example, targeting criteria identifies users who 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 an external system230, installed an application, or performed any other suitable action. Including actions in targeting criteria allows publishing users to further refine users eligible to be presented with sponsored 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 obj ect.

The action logger245receives communications about user actions internal to and/or external to the online system240, populating the action log250with information about user actions. Examples of actions include adding a connection to another user, sending a message to another user, uploading an image, reading a message from another user, viewing content associated with another user, attending an event posted by another user, among others. In addition, a number of actions may involve an object and one or more particular users, so these actions are associated with those users as well and stored in the action log250.

The action log250may be used by the online system240to track user actions on the online system240, as well as actions on external systems230that communicate information to the online system240. Users may interact with various objects on the online system240, and information describing these interactions is stored in the action log250. Examples of interactions with objects include: commenting on posts, sharing links, checking-in to physical locations via a client device210, accessing content items, and any other suitable interactions. Additional examples of interactions with objects on the online system240that are included in the action log250include: 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), expressing a reaction to an object, and engaging in a transaction. Additionally, the action log250may record a user’s interactions with advertisements on the online system240as well as with other applications operating on the online system240. In some embodiments, data from the action log250is 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.

The action log250may also store user actions taken on an external system230, such as an external website, and communicated to the online system240. For example, an e-commerce website may recognize a user of an online system240through a social plug-in enabling the e-commerce website to identify the user of the online system240. Because users of the online system240are uniquely identifiable, e-commerce websites, such as in the preceding example, may communicate information about a user’s actions outside of the online system240to the online system240for association with the user. Hence, the action log250may record information about actions users perform on an external system230, including webpage viewing histories, advertisements or other content with which the user engaged, purchases made, and other patterns from shopping and buying. Hence, the action log250may include information identifying content provided by one or more external systems230that a user of the online system240has accessed or content provided by one or more external systems230with which the user of the online system240otherwise interacted. Various external systems230may include tracking mechanisms in content comprising instructions that, when executed by a client device210, provide information identifying the content and identifying a user of the online system240associated with the client device210to the online system240. In various embodiments, the information provided by the tracking mechanism identifies one or more products associated with an external system230and include in, or otherwise associated with, the identified content. The information identifying the content is stored in the action log250in association with information identifying the user to the online system240. Additionally, actions a user performs via an application associated with an external system230and executing on a client device210may be communicated to the action logger245by the application for recordation and association with the user in the action log250.

In one embodiment, the edge store255stores information describing connections between users and other objects on the online system240as 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 system240, such as expressing interest in a page on the online system240, sharing a link with other users of the online system240, and commenting on posts made by other users of the online system240. Users and objects within the online system240can represented as nodes in a social graph that are connected by edges stored in the edge store255.

The edge store255also stores information about edges, such as affinity scores for objects, interests, and other users. Affinity scores, or “affinities,” may be computed by the online system240over time to approximate a user’s affinity for an object, interest, and other users in the online system240based on the actions performed by the user. A user’s affinity may be computed by the online system240over time to approximate a user’s affinity for an object, interest, and other users in the online system240based on the actions performed by the user. Computation of affinity is further described in U.S. Pat. Application No. 12/978,265, filed on Dec. 23, 2010, U.S. Pat. Application No. 13/690,254, filed on Nov. 30, 2012, U.S. Pat. Application No. 13/689,969, filed on Nov. 30, 2012, and U.S. Pat. Application No. 13/690,088, filed on Nov. 30, 2012, each of which is hereby incorporated by reference in its entirety. Multiple interactions between a user and a specific object may be stored as a single edge in the edge store255, in one embodiment. Alternatively, each interaction between a user and a specific object is stored as a separate edge. In some embodiments, connections between users may be stored in the user profile store242, or the user profile store242may access the edge store255to determine connections between users.

The model store260stores various computer models to evaluate a likelihood of user interaction with a content item. Examples of a computer model include a machine learning model (e.g., a supervised machine learning model, or an unsupervised machine learning model), a deep learning model (e.g., a neural network model). The model store260also stores training datasets for training the various computer models.

The content evaluation pipeline module265generates one or more content evaluation pipelines. The content evaluation pipeline module265generates rules to generate a content evaluation pipeline that includes an order of a plurality of stages each having one or more computer models and selects a decreasing number of content items, from each stage of the order, according to the order of the stages. The rules may include the number of stages, the order of stages, the number of computer models for a stage, the number of content items selected from a stage, a rule to select one or more computer models from the model store260for a stage, or a rule to select one or more computer models in a stage for delivering content items to the next stage (e.g., a rule to select a more complex model in a stage for delivering content items to the next stage). For example, in one embodiment, the rules may specify a content evaluation pipeline100A with three stages, each stage including a computer model. The first stage model130A may be a simple model that receives a large number (e.g., 10,000) of content items and thus requires significant compute power. The second stage model150may have greater computing complexity than the first stage model130A and require less compute power to select from the800input content items. Finally, the third stage model160may require the least compute power of the three models to accurately select from200input content items the three content items that are likely to be of the highest value for a given user.

The pipeline training module270trains the models in the content evaluation pipeline100A to mimic the output of later, more complex models, thereby optimizing the selection of content items provided to subsequent stages of the content evaluation pipeline100A as well as the content items ultimately selected for display to a given user of the online system240in the feed display110.

The pipeline training module270applies machine learning techniques to train the first stage model130A and the second stage model150to output predicted content selection values for content items under consideration for display to a given user. As part of the training, the pipeline training module270forms a training set of content items by identifying a positive set of content items that would be selected for input to a subsequent model and, in some embodiments, forms a negative training set of content items that would not be selected for subsequent processing. In one embodiment, training data for a model is generated by inputting training content items to the subsequent model in the content evaluation pipeline100A. For example, to train the first stage model130A, the pipeline training module270feeds to the second stage model150a first training set of content items for which a given user satisfies the targeting criteria. For each content item in the first training set, the second stage model150calculates a content selection value, as discussed above. In one embodiment, the content selection value represents the amount of compensation that the online system240would receive if a suitable condition is satisfied when one or more content items are presented to the user (e.g., if the user interacts with a content item). Alternatively, the content selection value is determined by multiplying a bid amount for a content item by a prediction likelihood indicating how likely a given user is to interact with the content item.

The second stage model150selects the content items with the highest content selection values to continue in the content selection pipeline100A. For example, if the second stage model150receives as input800training content items, the second stage model150selects the content items with the200highest content selection values for input to the third stage model160. The pipeline training module270uses machine learning to train the first stage model130A with the content selection values in the first training set serving as the input. Different machine learning techniques—such as linear support vector machine (linear SVM), boosting for other algorithms (e.g., AdaBoost), neural networks, logistic regression, naive Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, or boosted stumps— may be used in different embodiments.

The second stage model150is similarly trained to predict the output of the third stage model160. For example, for the200content items in the content evaluation pipeline100A that would be input to the third stage model160, the second stage model150is trained to predict the content selection value that the third stage model160would calculate for each content item and thus which three content items the third stage model160would select for display to a given user in the feed display110.

To train the second stage model150, the pipeline training module270forms a second training set of content items for which the given user satisfies the targeting criteria and inputs the content items to the third stage model160. The third stage model160calculates a content selection value for each content item in the second training set and outputs the content selection values and an indication of which three content items from the second training set would be of the highest value for a given user. The pipeline training module270then trains the second stage model150using the content selection values from the second training set serving as the input. In other embodiments, the pipeline training module270trains the models in the content evaluation pipeline100A from most to least complex (i.e., trains the third stage model160on the second training set, then trains the second stage model150based on predicted values from the trained third stage model160, then trains the first stage module130A based on predicted values from the trained second stage model150.

The newsfeed manager280may generate content for presentation to a user based on information in the action log250and in the edge store255or may select candidate stories included in content store243. One or more of the candidate stories are selected and presented to a user by the newsfeed manager280.

For example, the newsfeed manager280receives a request to present one or more stories to an online system user. The newsfeed manager280accesses one or more of the user profile store242, the content store243, the action log250, and the edge store255to retrieve information about the identified user. For example, stories or other data associated with users connected to the identified user are retrieved. The retrieved stories or other data is analyzed by the newsfeed manager280to identify content likely to be relevant to the identified user. For example, stories associated with users not connected to the identified user or stories associated with users for which the identified user has less than a threshold affinity are discarded as candidate stories. Based on various criteria, the newsfeed manager280selects one or more of the candidate stories for presentation to the identified user.

In various embodiments, the newsfeed manager280presents stories to a user through a newsfeed, which includes a plurality of stories selected for presentation to the user. The newsfeed may include a limited number of stories or may include a complete set of candidate stories. The number of stories included in a newsfeed may be determined in part by a user preference included in user profile store242. The newsfeed manager280may also determine the order in which selected stories are presented via the newsfeed. For example, the newsfeed manager280determines that a user has a highest affinity for a specific user and increases the number of stories in the newsfeed associated with the specific user or modifies the positions in the newsfeed where stories associated with the specific user are presented.

The newsfeed manager280may also account for actions by a user indicating a preference for types of stories and selects stories having the same, or similar, types for inclusion in the newsfeed. Additionally, newsfeed manager280may analyze stories received by an online system240from various users and obtains information about user preferences or actions from the analyzed stories. This information may be used to refine subsequent selection of stories for newsfeeds presented to various users.

The content serving module285receives content items from the various external systems230for provision to users to whom the content would be relevant and delivers appropriate content items to users, and stores the received content items in the content store243. The content serving module285sends the received content items to the content evaluation pipeline module265to select one or more content items to be delivered to the client device210for presentation to the user. For example, when an opportunity arises to present a content item to the user, the content evaluation pipeline module265selects a content item that a user is most likely to perform actions on (e.g., clicking on the content item, visiting a website via clicking on the content item, placing the content item in a virtual shopping cart, or purchasing the content item). The content serving module285delivers the selected content item to the user for the opportunity.

The web server290links the online system240via the network220to the one or more client devices210, as well as to the one or more external systems230. The web server290serves web pages, as well as other web-related content, such as JAVA®, FLASH®, XML and so forth. The web server290may receive and route messages between the online system240and the client device210, for example, instant messages, queued messages (e.g., email), text messages, short message service (SMS) messages, or messages sent using any other suitable messaging technique. A user may send a request to the web server290to upload information (e.g., images or videos) that are stored in the content store243. Additionally, the web server290may provide application programming interface (API) functionality to send data directly to native client device operating systems, such as IOS®, ANDROID™, WEBOS® or RIM®.

FIG.3is a flowchart illustrating a process300for training a first stage model130A in the content evaluation pipeline100A, in accordance with an embodiment. The process300may be performed by the online system240. The process300may include different or additional steps than those described in conjunction withFIG.3in some embodiments or perform steps in different orders than the order described in conjunction withFIG.3.

The online system240identifies305a content evaluation pipeline100A comprising an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item, and the content evaluation pipeline100A selects a decreasing number of content items, from each stage of the order, according to the order of the stages in the order. Examples of a content evaluation pipeline100A are described above with respect toFIGS.1A and1B. For example, in one embodiment, the content evaluation pipeline100A includes three stages, each having a computer model (e.g., a first stage model130A, a second stage model150, and a third stage model160) for evaluating the likelihood that a content item is likely to be of high value to a given user.

At310, the pipeline training module270generates a first training set of content items for which a given user satisfies the targeting criteria. For example, the first training set of content items might include sponsored content items for which the user has satisfied the targeting criteria by taking a particular action, such as using an application, joining a group, purchasing a product, installing an application, or performing any other suitable action. In one embodiment, the first training set contains 800 content items, although more or fewer content items may be used in other embodiments.

The pipeline training module150trains315the second stage model150on the first training set, as described above with respect toFIG.2. For each of the 800 content items in the first training set, the second stage model150generates a content selection value based in part on a likelihood that a given user will interact with the content item and outputs an indication of the content selection values and the200content items with the highest content selection values that would be selected for input to the subsequent model in the content evaluation pipeline100A. In one embodiment, the second stage model150outputs, for each content item, a content selection value between zero and one. Alternatively, the second stage model150provides a range of values representing a possible distribution of content selection values for each content item.

At320, the pipeline training module270uses machine learning to train the first stage model130A to mimic the output of the second stage model150. The first stage model130A is trained to predict the content selection value or range of values that the second stage model150would assign to a given content item to optimize the functioning of the content evaluation pipeline100A and the ultimate selection of content items for a given user.

FIG.4is a flowchart illustrating a process for training a second stage model150in the content evaluation pipeline100A, in accordance with an embodiment. The process400may be performed by the online system240. The process400may include different or additional steps than those described in conjunction withFIG.4in some embodiments or perform steps in different orders than the order described in conjunction withFIG.4. Further, the process400may occur after the process300, in parallel with the process300, or before the process300.

The online system240identifies405a content evaluation pipeline100A comprising an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item, and the content evaluation pipeline100A selects a decreasing number of content items, from each stage of the order, according to the order of the stages in the order. Examples of a content evaluation pipeline are described above inFIGS.1A and1B. For example, in one embodiment, the content evaluation pipeline100A includes three stages, each having a computer model (e.g., a first stage model130A, a second stage model150, and a third stage model160) for evaluating the likelihood that a content item is likely to be of high value to a given user. In some embodiments, the content evaluation pipeline100A is the same content evaluation pipeline100A discussed above with respect toFIG.3.

At410, the pipeline training module270generates a second training set of content items for which a given user satisfies the targeting criteria. In one embodiment, the second training set contains200content items, although more or fewer content items may be used in other embodiments.

The pipeline training module150trains415the third stage model160on the second training set, as described above with respect toFIG.2. For each of the200content items in the second training set, the third stage model160generates a content selection value based in part on a likelihood that a given user will interact with the content item and outputs an indication of the content selection values and the 3 content items with the highest selection values that would be selected for display to the user. In one embodiment, the third stage model160outputs, for each content item, a content selection value between zero and one. Alternatively, the third stage model160provides a range of values representing a possible distribution of content selection values for each content item.

At420, the pipeline training module270uses machine learning to train the second stage model150to mimic the output of the third stage model160. The second stage model150is trained to predict the content selection value or range of values that the third stage model160would assign to a given content item to optimize the functioning of the content evaluation pipeline100A and the ultimate selection of content items for a given user.

Embodiments may also relate to a product that is produced by a computing process described herein. Such a product may comprise information resulting from a computing process, where the information is stored on a non transitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein.