Abstract: A social networking system builds a quality controlled and desired population-representative pool of human raters to provide ratings on content items to improve a feed ranking model used for providing its users with more relevant content. The system identifies a pool of candidate human raters for providing ratings on a feed of content items. For each candidate human rater of the pool of candidate human raters, the system presents a feed of content items based on a feed ranking model, obtains ratings on the feed of content items, and determines a score representing the consistency of the obtained ratings, the representativeness of the pool of human raters, or the relevance of the content provided by the ranking model. The system uses the computed scores to modify the ranking model used to present content to its users for improving the relevance of the presented content.

Abstract: A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and an expected value score for each candidate story is determined. An expected value score is based on the probability of a user performing various types of interactions with a candidate story and a numerical value for each type of interaction. The numerical value for a type of interaction represents a value to the social networking system of the type of interaction. Based on the expected value scores, the candidate stories are ranked and the ranking used to select candidate stories for the newsfeed.

Abstract: A system transmits selected news feed stories to a client device in advance of receiving a request for news feed stories. As a result, stories are immediately available for viewing when a user interacts with the system. The system selects news feed stories to push based on criteria such as a likelihood that a user will interact with a story and the sizes of pushed stories. For example, the system selects news feed stories such that a total size of stories selected does not exceed a threshold value based on local memory at the client device. The system may determine a scheduled time at which the stories are selected and pushed. The scheduled time is based on factors including patterns of network connection speed or past user interactions, for example, a time range of the day during which the user most frequently viewed pushed stories.

Abstract: News feed stories are ranked so that new stories appear above previously viewed stories for users of a social networking system. Top news stories are selected by the social networking system based on a ranking algorithm that incorporates an analysis of affinities for interests, users, and entities in the social networking system based on user interactions. Other new stories are provided after the top new stories in a chronological order. For each user of the social networking system, a view state is recorded that includes the order of news feed stories provided to users. Based on the view state and received user input, the social networking system may determine whether to provide new news feed stories to a viewing user.

Abstract: News feed stories are ranked so that new stories appear above previously viewed stories for users of a social networking system. Top news stories are selected by the social networking system based on a ranking algorithm that incorporates an analysis of affinities for interests, users, and entities in the social networking system based on user interactions. Other new stories are provided after the top new stories in a chronological order. For each user of the social networking system, a view state is recorded that includes the order of news feed stories provided to users. Based on the view state and received user input, the social networking system may determine whether to provide new news feed stories to a viewing user.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a set of candidate content items from a plurality of content items that are available in the social networking system for a first user. A corresponding score for each of the candidate content items can be generated based at least in part on one or more social affinity coefficients corresponding to the first user and a respective second user associated with a candidate content item, wherein a social affinity coefficient provides a quantitative measurement of the strength of a relationship between two users. A first set of content items from the set of candidate content items can be determined based at least in part on the respective scores, wherein content items in the first set are included in a content feed provided to the first user.

Abstract: A mobile device receives ranked news feed stories from a server of a social networking system. The mobile device determines whether to adjust the ranking based on local information generated by applications executing on the mobile device or hardware of the mobile device. Examples of local information of the mobile device include information describing the mobile devices' network connection, geographical location, sensor readings, downloaded data on memory storage, and interactions of software applications executing on the mobile device. For example, if a network co-processor of a mobile device detects a slow network connection, the mobile device lowers the ranking of news feed stories expected to require further use of network bandwidth.

Abstract: To increase the likelihood of a user viewing content items previously identified for presentation but not viewed by the user, a social networking system accesses a view state associated with the user that identifies content items provided to the user and provided content items viewed by the user. Based on the view state, content items previously provided to the user but not viewed by the user are identified. Additional content items whose state has changed since presentation of the content items to the user are also identified. Content items previously presented to the user but not viewed with at least a threshold relevance to the user are ranked along with the additional content items. Based on the ranking, content items are selected for presentation to the user.

Abstract: A social networking system presents a viewing user with a newsfeed of stories describing actions performed by other social networking system users. In particular, the social networking system provides a pre-fetched set of newsfeed stories to a user device of the viewing user and a presentation order for the stories. The social networking system does not “lock” the set of newsfeed stories or the presentation order. The user device locally stores the pre-fetched ranked set of newsfeed stories upon receipt. When the viewing user later requests presentation of newsfeed stories, the user device retrieves the set of newsfeed stories and presents, to the viewing user, stories from the set according to the presentation order. A request is also sent to the social networking system to “lock” the presented newsfeed stories and the order in which the newsfeed stories were presented.

Abstract: A social networking system builds a quality controlled and desired population-representative pool of human raters to provide ratings on content items to improve a feed ranking model used for providing its users with more relevant content. The system identifies a pool of candidate human raters for providing ratings on a feed of content items. For each candidate human rater of the pool of candidate human raters, the system presents a feed of content items based on a feed ranking model, obtains ratings on the feed of content items, and determines a score representing the consistency of the obtained ratings, the representativeness of the pool of human raters, or the relevance of the content provided by the ranking model. The system uses the computed scores to modify the ranking model used to present content to its users for improving the relevance of the presented content.

Abstract: In one embodiment, a method includes sending, from an application server to a first set of client systems, instructions for providing a treatment to a first set of users. The method includes determining an average treatment effect of the treatment for the first set of users based on information about user interactions associated with the treatment and a network exposure to the treatment for each user. Network exposure for a user is a measure of other users associated with the user that have been provided the treatment. The method includes modifying the treatment based on the determined average treatment effect. The method includes sending, to a second set of client systems, instructions for providing the modified treatment to a second set of users. The second set of users is a discrete, non-overlapping set of users with respect to the first set.

Abstract: To suggest new connections to a user of a social networking system, the system generates a set of candidate users to whom the user has not already formed a connection. The system determines the likelihood that the user will connect to each candidate user if suggested to do so, and it also computes the value to the social networking system if the user does connect to the candidate user. Then, the system computes an expected value score for each candidate user based on the corresponding likelihood and the value. The candidate users are ranked and the suggestions are provided to the user based on the candidate users' expected value scores. The social networking system can suggest other actions to a user in addition to forming a new connection with other users.

Abstract: Systems, methods, and non-transitory computer readable media configured to create objects associated with links to content items shared within a social networking system, the content items from content sources. Content sources having pages on the social networking system fanned by a user are determined. Objects associated with (links to) content items from the content sources having content source representations, such as pages, on the social networking system fanned by the user are collected. An aggregate score for a collected object associated with a link is determined based on popularity of a content item associated with the link within the social networking system and interest of the user in the content item. The collected object associated with the link is provided to be ranked for potential presentation of the link to the user based on satisfaction of a threshold.

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 to increase the likelihood of the user interacting with content items presented via the feed.

Abstract: A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a minimum bid amount associated with advertisements eligible for presentation to the user. Increasing the minimum bid amount decreases the number of advertisements presented to the user while decreasing the minimum bid amount increases the number of advertisements presented to the user. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined. A target score is determined based on the engagement scores, and a difference between the target score and a threshold value is used to modify a minimum price of advertisements eligible for presentation to the user.

Abstract: News feed stories are ranked so that new stories appear above previously viewed stories for users of a social networking system. Top news stories are selected by the social networking system based on a ranking algorithm that incorporates an analysis of affinities for interests, users, and entities in the social networking system based on user interactions. Other new stories are provided after the top new stories in a chronological order. For each user of the social networking system, a view state is recorded that includes the order of news feed stories provided to users. Based on the view state and received user input, the social networking system may determine whether to provide new news feed stories to a viewing user.

Abstract: A system transmits selected news feed stories to a client device in advance of receiving a request for news feed stories. As a result, stories are immediately available for viewing when a user interacts with the system. The system selects news feed stories to push based on criteria such as a likelihood that a user will interact with a story and the sizes of pushed stories. For example, the system selects news feed stories such that a total size of stories selected does not exceed a threshold value based on local memory at the client device. The system may determine a scheduled time at which the stories are selected and pushed. The scheduled time is based on factors including patterns of network connection speed or past user interactions, for example, a time range of the day during which the user most frequently viewed pushed stories.

Abstract: Systems, methods, and non-transitory computer readable media configured to create objects associated with links to content items shared within a social networking system, the content items from content sources. Content sources having pages on the social networking system fanned by a user are determined. Objects associated with (links to) content items from the content sources having content source representations, such as pages, on the social networking system fanned by the user are collected. An aggregate score for a collected object associated with a link is determined based on popularity of a content item associated with the link within the social networking system and interest of the user in the content item. The collected object associated with the link is provided to be ranked for potential presentation of the link to the user based on satisfaction of a threshold.

Abstract: A social networking system builds a quality controlled and desired population-representative pool of human raters to provide ratings on content items to improve a feed ranking model used for providing its users with more relevant content. The system identifies a pool of candidate human raters for providing ratings on a feed of content items. For each candidate human rater of the pool of candidate human raters, the system presents a feed of content items based on a feed ranking model, obtains ratings on the feed of content items, and determines a score representing the consistency of the obtained ratings, the representativeness of the pool of human raters, or the relevance of the content provided by the ranking model. The system uses the computed scores to modify the ranking model used to present content to its users for improving the relevance of the presented content.

Abstract: When an online system receives a request to present content items to a user, a content selection system included in the online system selects content items for presentation to the user during a latency period from the time the request was received until the time when the content items are sent. A feedback control mechanism communicates with each computing device of the content selection system to determine the latency period of each computing device. The feedback control mechanism also determines a target latency period in which content items are selected. By comparing the latency period of each computing device to the target latency period, an amount of information to be evaluated by each computing device is determined based on whether a computing device's latency period is greater than or less than the target latency period.