Abstract: A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).

Abstract: A system uses semantic analysis of text associated with content items to recommend content for display to a user. A subset of representative words from a content description are determined and a content embedding that models the content is generated using a combination of word embeddings associated with each of the representative words. User embeddings are generated using a combination of content embeddings for content that a user has had particular interactions with in a set period of time. Separate user embeddings may be generated to represent user interactions with different categories of content (e.g., travel, photography, apparel, comedy, etc.). The system uses the content embeddings and user embeddings as input to predictive functions which determine a candidate content item that a user is likely to interact with if the candidate content is displayed to the user.

Abstract: Systems and methods for providing a personalized visual display of multiple products are provided. A described method includes receiving product information for a set of multiple products and user information for a particular user or user device and selecting a plurality of the multiple products estimated to be most relevant to the particular user or user device based on the product information for the set of multiple products and the user information for the particular user or user device. The method further includes generating a personalized visual display of the selected products including product images for multiple of the selected products. All of the products in the personalized visual display may be associated with the same content provider. The method further includes causing the personalized visual display to be presented via the user device.

Abstract: Systems and methods for providing a personalized visual display of multiple products are provided. A described method includes receiving product information for a set of multiple products and user information for a particular user or user device and selecting a plurality of the multiple products estimated to be most relevant to the particular user or user device based on the product information for the set of multiple products and the user information for the particular user or user device. The method further includes generating a personalized visual display of the selected products including product images for multiple of the selected products. All of the products in the personalized visual display may be associated with the same content provider. The method further includes causing the personalized visual display to be presented via the user device.

Abstract: In some examples, a method includes receiving, from one or more client devices, a stream of messages composed by one or more users of the one or more client devices, wherein each of the messages includes a particular hashtag, determining, using a set of metrics that are based at least in part on the messages, a trending score that represents a magnitude of a trend for the particular hashtag, in response to determining that the trending score satisfies a threshold, sending, to a content provider system, a set of demographic data that describes one or more of the users who associated with the particular hashtag, and, in response to receiving, from the content provider system, targeted content that is based at least in part on the particular hashtag and the set of demographic data, sending, for display at the one or more of the one or more client devices, the targeted content.

Abstract: An online system determines a stability metric that indicates overlap between the set of entities associated with a particular entity when embeddings have been adjusted due to modifications in the input data of an embedding model. The online system generates a stability score for the embedding model by computing a statistic for one or more stability metrics. The online system determines a stability metric for a particular content provider by identifying a first cluster of content providers in a set of first embeddings, and a second cluster of content providers in a set of second embeddings. The second embeddings are generated after modifications have been made to input data. The online system determines the stability metric based on an overlap between the first cluster and the second cluster of content providers. The stability score can be an indicator of model performance that can be used to select embedding models.

Abstract: An online system maintains one or more models that determine likelihoods of a user performing various interactions after being presented with a content item. Additionally, the online system receives information identifying interactions by users with content, and generates embeddings for various users based on the interactions by the users with content. When determining whether to present a content item including an objective identifying an interaction to a user, the online system applies a maintained model to determine a likelihood of the user performing the interaction identified by the objective after being presented with the content item. Additionally, the online system determines a similarity of the embedding of the user to embeddings of users who performed the interaction identified by the objective. Based on a combination of the likelihood determined by the model and the similarity, the online system determines whether to present the content item to the user.

Abstract: A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).

Abstract: Systems and methods for providing a personalized visual display of multiple products are provided. A described method includes receiving product information for a set of multiple products and user information for a particular user or user device and selecting a plurality of the multiple products estimated to be most relevant to the particular user or user device based on the product information for the set of multiple products and the user information for the particular user or user device. The method further includes generating a personalized visual display of the selected products including product images for multiple of the selected products. All of the products in the personalized visual display may be associated with the same content provider. The method further includes causing the personalized visual display to be presented via the user device.

Abstract: Systems and methods for providing a personalized visual display of multiple products are provided. A described method includes receiving product information for a set of multiple products and user information for a particular user or user device and selecting a plurality of the multiple products estimated to be most relevant to the particular user or user device based on the product information for the set of multiple products and the user information for the particular user or user device. The method further includes generating a personalized visual display of the selected products including product images for multiple of the selected products. All of the products in the personalized visual display may be associated with the same content provider. The method further includes causing the personalized visual display to be presented via the user device.

Abstract: In some examples, a method includes receiving, from one or more client devices, a stream of messages composed by one or more users of the one or more client devices, wherein each of the messages includes a particular hashtag, determining, using a set of metrics that are based at least in part on the messages, a trending score that represents a magnitude of a trend for the particular hashtag, in response to determining that the trending score satisfies a threshold, sending, to a content provider system, a set of demographic data that describes one or more of the users who associated with the particular hashtag, and, in response to receiving, from the content provider system, targeted content that is based at least in part on the particular hashtag and the set of demographic data, sending, for display at the one or more of the one or more client devices, the targeted content.

Abstract: An online system receives tracking requests from client devices interacting with a web page to analyze user interactions with the web page. The online system extracts parameters from tracking requests such as a uniform resource locator (URL) associated with the web page that generated the tracking request and/or data tokens describing keywords within the URL. The online system may extract parameters by crawling web pages that generate tracking requests. The online system may compare extracted parameters to a taxonomy of categories maintained by the online system to determine a category describing the item displayed on the web page. The online system determines a category describing the item via an item catalog maintained by the online system comprised of previously determined categories for various items. The online system uses the determined categories, attributes, and temporal relevance scores to direct content to users.

Abstract: In some examples, a method includes receiving, from one or more client devices, a stream of messages composed by one or more users of the one or more client devices, wherein each of the messages includes a particular hashtag, determining, using a set of metrics that are based at least in part on the messages, a trending score that represents a magnitude of a trend for the particular hashtag, in response to determining that the trending score satisfies a threshold, sending, to a content provider system, a set of demographic data that describes one or more of the users who associated with the particular hashtag, and, in response to receiving, from the content provider system, targeted content that is based at least in part on the particular hashtag and the set of demographic data, sending, for display at the one or more of the one or more client devices, the targeted content.

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. A request is received for a content item for publication in a slot on a publisher site. A content item responsive to the request is identified. The publisher site is identified based on the request. A determination is made when to augment the content item with a feedback item. The content item is augmented to include the feedback item for obtaining feedback from a viewer of the content item. The feedback item includes a query as to a desire of the viewer to share the content item with other members of a community associated with the publisher site. The augmented content item is provided responsive to the request. Feedback is received from the viewer based on interaction with the feedback item once presented. The feedback is associated with the content item.

Abstract: An online system selects items for display in content provided to users by considering the value of each item to third-party content providers as well as user's interests. The online system receives a catalog including items that are each associated with weights from a third-party content provider for inclusion in sponsored content to be presented to users of an online system. The weights have values indicating measures of importance of the items to the third-party content provider on a per-item basis. The online system identifies a request for sponsored content, and selects one or more items from the catalog for inclusion in a dynamic sponsored content item. The online system calculates a weighted user preference score using a weight associated with an item and affinity information describing the user's affinity for the item.

Abstract: An online system determines a stability metric that indicates overlap between the set of entities associated with a particular entity when embeddings have been adjusted due to modifications in the input data of an embedding model. The online system generates a stability score for the embedding model by computing a statistic for one or more stability metrics. The online system determines a stability metric for a particular content provider by identifying a first cluster of content providers in a set of first embeddings, and a second cluster of content providers in a set of second embeddings. The second embeddings are generated after modifications have been made to input data. The online system determines the stability metric based on an overlap between the first cluster and the second cluster of content providers. The stability score can be an indicator of model performance that can be used to select embedding models.

Abstract: In some examples, a method includes receiving, from one or more client devices, a stream of messages composed by one or more users of the one or more client devices, wherein each of the messages includes a particular hashtag, determining, using a set of metrics that are based at least in part on the messages, a trending score that represents a magnitude of a trend for the particular hashtag, in response to determining that the trending score satisfies a threshold, sending, to a content provider system, a set of demographic data that describes one or more of the users who associated with the particular hashtag, and, in response to receiving, from the content provider system, targeted content that is based at least in part on the particular hashtag and the set of demographic data, sending, for display at the one or more of the one or more client devices, the targeted content.

Abstract: A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).

Abstract: In one embodiment, a method includes receiving a query inputted by the user; identifying a set of objects matching the query; calculating, for each identified object, a plurality of category-scores corresponding to a plurality of categories, respectively, wherein each category-score is calculated based on a plurality of sub-scores corresponding to a plurality of scoring axes; categorizing each identified object into a category of the plurality of categories based on the category-scores for the identified object; and sending, to the client system in response to the query, one or more search results corresponding to one or more of the categorized objects for display, each search result referencing the respective categorized object, wherein the one or more categorized objects of the search results comprises objects categorized into one or more selected categories.

Abstract: In one embodiment, a method includes receiving a query inputted by the user; identifying a set of objects matching the query; calculating, for each identified object, a plurality of category-scores corresponding to a plurality of categories, respectively, wherein each category-score is calculated based on a plurality of sub-scores corresponding to a plurality of scoring axes; categorizing each identified object into a category of the plurality of categories based on the category-scores for the identified object; and sending, to the client system in response to the query, one or more search results corresponding to one or more of the categorized objects for display, each search result referencing the respective categorized object, wherein the one or more categorized objects of the search results comprises objects categorized into one or more selected categories.