Abstract:
An online system analyzes videos from video hosting systems to identify embedded contents in the videos. The online system associates embedded content with videos that include the embedded content. The online system determines statistics describing distribution of the embedded content by the video hosting system, for example, the rate at which the embedded content is included in videos and demographics of the users targeted for the embedded content. The online system may use the information describing distribution of the embedded content by other video hosting systems to modify the distribution of embedded content by the online system.
Abstract:
A social networking system selects and presents content items to a user via a feed. Additionally, the social networking system predicts heights associated with various content items, such as content items selected for presentation via the feed. Characteristics of a content item (e.g., a type of content included in the content item, a language of the content item, and a number of comments associated with the content item) as well as characteristics of a client device associated with the user are used to predict a height associated with the content item. When selecting content items for presentation to the user, the social networking system accounts for the predicted heights of various content items when ordering the content items in the news feed.
Abstract:
An online system penalizes content items having features matching features of additional content items previously presented to a user within a specified time interval. The online system identifies various features of the content item and identifies features of content items previously presented to the user within the specified time interval. Feature penalties are determined for various features of the content item based on a number of previously presented content items having a common feature with the content item. Weights may be associated with various content items having a feature matching a feature of the content item based on a time between presentation of the previously presented content item and a current time. A penalty for the content item is determined based on the feature penalties for the features of the content item, and the penalty is applied to a bid amount associated with the content item.
Abstract:
An online system presents advertisements and content items to its users in a feed of content items (e.g., a newsfeed). The online system enforces one or more advertisement policies regulating insertion of advertisements into the feed and determines a predicted likelihood that enforcing the advertising policies will prevent insertion of additional advertisements into the feed of content items when a request to present content via the feed is received from a user of the online system. Advertising policies describe conditions preventing insertion of additional advertisements into the feed (e.g., positions in the feed that may not be occupied by advertisements, a minimum distance separating advertisements in the feed, etc.). Based on the predicted likelihood, the online system determines whether to request one or more additional advertisements for insertion into the feed from an advertisement service.
Abstract:
A social networking system presents a content feed including organic content items and sponsored content items to a user. To maintain user interaction with the content feed, the social networking system determines probabilities of the user performing various types of interactions with a sponsored content item and accounts for the determined probabilities when selecting content items for presentation via the content feed. For example, the social networking system generates a value for the sponsored content item based on the determined probabilities and determines a score for the sponsored content item based on the value and a bid amount associated with the sponsored content item. When selecting content for the content feed, the social networking system evaluates the sponsored content item based on its associated score. Prior interactions between the user and previously presented content may be used when determining the score for the sponsored content item.
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:
Competitive bidding tools, including a competitive bidding scaler tool and a competitive report generator, may be implemented by an advertiser to improve their advertisement's performance in online advertising auctions. The competitive bidding scaler tool increases the bid amount associated with an advertiser's ad request when a competing advertiser submits a rival ad request to the same online advertising auction. The competitive report tool generates a competitive report for an ad request that benchmarks its performance against rival ad requests. The competitive report comprises a summary of wins and losses experienced by the ad request in an online advertising auction as well as an option to implement the competitive bidding scaler tool for the ad request.