Abstract: An online system applies content policies regulating presentation of sponsored content to its users. For example, content policies may prevent the presentation of sponsored content items in certain positions content feeds. The online system may relax a content policy when generating a content feed for a user based on characteristics of a user. For example, the online system generates a model determining a tolerance of the user for sponsored content, and relaxes one or more content policies if the tolerance of the user for sponsored content equals or exceeds a threshold. As another example, the online system determines whether to relax one or more content policies based on a comparison of a historical amount of compensation received from the user and an expected amount of compensation from presenting content items violating a content policy.

Abstract: An online system receives a request to present a content item to a viewing user who is associated with a set of user attributes. The online system retrieves a regression model for predicting an expected quality for a particular content item and a particular set of users attributes. The regression model was trained, using machine learning, based on user-assigned quality scores, each corresponding to a content item and provided by a quality-assigning user, and sets of user attributes, each set associated with one of the quality-assigning users. The online system uses the regression model to predict a quality score, indicating the quality of a content item to the viewing user, based on the set of user attributes that is associated with the viewing user. The online system determines to provide the content to the viewing user based on the quality score, and transmits the content item to the viewing user.

Abstract: An online system accounts for measures of quality of content items when selecting content items for presentation to users. For a content item including a link to a landing page outside a domain of the online system, the measure of quality accounts for a likelihood of a user presented with the content item returning to the online system after less than a threshold amount of time accessing the landing page. The measure of quality of the content item is based on the likelihood of the user returning to the online system after less than the threshold amount of time relative to likelihoods of other users returning to the online system after less than the threshold amount of time. The measure of quality of the content item is affected by a difference between the likelihood determined for the user and the likelihoods determined for other users.

Abstract: An online system applies content policies regulating presentation of sponsored content to its users. For example, content policies may prevent the presentation of sponsored content items in certain positions content feeds. The online system may relax a content policy when generating a content feed for a user based on characteristics of a user. For example, the online system generates a model determining a tolerance of the user for sponsored content, and relaxes one or more content policies if the tolerance of the user for sponsored content equals or exceeds a threshold. As another example, the online system determines whether to relax one or more content policies based on a comparison of a historical amount of compensation received from the user and an expected amount of compensation from presenting content items violating a content policy.

Abstract: An online system selects content items for presentation to viewing users of the online system based on a composite score associated with each content item that includes a quality component and a revenue component. The revenue component is based on a monetary amount an advertiser associated with the content item is willing to pay for each interaction with the content item by a prospective viewing user, while the quality component indicates the quality of the content item to the prospective viewing user. The quality component is predicted based on explicit user quality ratings received from viewing users for various content items previously presented to the viewing users, in which the viewing users have at least a threshold measure of similarity to the prospective viewing user and/or the various content items rated by the viewing users have at least a threshold measure of similarity to the content item being scored.

Abstract: Disclosed are a method and system for searching entities and keys that are associated with each other using entity-key associations stored in an in-memory object. When a query is issued for retrieving entity-key associations, the disclosed technique searches an in-memory object, such as a binary large object (BLOB) containing a plurality of entity-key association strings that represent the entity-key associations. An entity-key association string includes an entity name of an entity concatenated with a key name of a key that are associated with each other. Searching for the entity-key associations using the entity-key association strings in the BLOB is faster than a conventional search that includes querying multiple tables in a database. The disclosed technique creates the entity-key association strings by reading necessary data from a data structure stored in data storage unit. Both literal and regular expressions are supported without adversely affecting the query performance.

Abstract: An online system receives advertisement (“ad”) requests for presentation to online system users. An ad request may include an identifier of a landing page identifying a source external to the online system from which content is retrieved and presented to a user who interacts with content from the ad request. The online system determines a quality score for the source based on a frequency with which online system users request content from the source via the online system and various types of interactions by online system users with content associated with the source that is presented by the online system (e.g., interactions with content associated with the source indicating user interest in the source). Based on the quality score for the source, the online system modifies a score for the ad request used by the online system to determine whether to present content form the ad request to a user.

Abstract: A social networking system selects content items for presentation to a user. To promote user interaction with selected content items, the social networking system scores content items based at least in part on similarity in appearances of the content items to an appearance of a content item for which the social networking system is compensated for presentation (a “sponsored content item”). For example, a model is applied to features describing appearance of a content item to generate the score for a content item. When selecting content items for presentation, a score associated with a content item may modify the likelihood of the content item being selected. A content item with a score indicating greater than a threshold similarity in appearance to an appearance of a sponsored content item may be penalized when the social networking system selects content for presentation.

Abstract: Disclosed are a method and system for searching entities and keys that are associated with each other using entity-key associations stored in an in-memory object. When a query is issued for retrieving entity-key associations, the disclosed technique searches an in-memory object, such as a binary large object (BLOB) containing a plurality of entity-key association strings that represent the entity-key associations. An entity-key association string includes an entity name of an entity concatenated with a key name of a key that are associated with each other. Searching for the entity-key associations using the entity-key association strings in the BLOB is faster than a conventional search that includes querying multiple tables in a database. The disclosed technique creates the entity-key association strings by reading necessary data from a data structure stored in data storage unit. Both literal and regular expressions are supported without adversely affecting the query performance.

Abstract: Disclosed are a method and system for searching entities and keys that are associated with each other using entity-key associations stored in an in-memory object. When a query is issued for retrieving entity-key associations, the disclosed technique searches an in-memory object, such as a binary large object (BLOB) containing a plurality of entity-key association strings that represent the entity-key associations. An entity-key association string includes an entity name of an entity concatenated with a key name of a key that are associated with each other. Searching for the entity-key associations using the entity-key association strings in the BLOB is faster than a conventional search that includes querying multiple tables in a database. The disclosed technique creates the entity-key association strings by reading necessary data from a data structure stored in data storage unit. Both literal and regular expressions are supported without adversely affecting the query performance.

Abstract: Disclosed are a method and system for searching entities and keys that are associated with each other using entity-key associations stored in an in-memory object. When a query is issued for retrieving entity-key associations, the disclosed technique searches an in-memory object, such as a binary large object (BLOB) containing a plurality of entity-key association strings that represent the entity-key associations. An entity-key association string includes an entity name of an entity concatenated with a key name of a key that are associated with each other. Searching for the entity-key associations using the entity-key association strings in the BLOB is faster than a conventional search that includes querying multiple tables in a database. The disclosed technique creates the entity-key association strings by reading necessary data from a data structure stored in data storage unit. Both literal and regular expressions are supported without adversely affecting the query performance.