Abstract: Systems, methods, and non-transitory computer readable media can perform a first ranking to rank each ephemeral content item collection of a plurality of ephemeral content item collections based on a probability of a user selecting the ephemeral content item collection. One or more ephemeral content item collections from the first ranking to provide in an ephemeral content feed of the user can be selected. A second ranking to rank each ephemeral content item collection of the plurality of ephemeral content item collections other than the selected ephemeral content item collections from the first ranking based on a probability of the user spending time on the ephemeral content item collection can be performed. One or more ephemeral content item collections from the second ranking to provide in the ephemeral content feed of the user can be selected.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a first set of content items to be sent to a computing device of a user. A token is generated based at least in part on the first set of content items, the token being encoded with information for tracking content items sent to the computing device of the user. Data corresponding to the set of content items and the token is sent to the computing device of the 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: Systems, methods, and non-transitory computer readable media can perform a first ranking to rank each ephemeral content item collection of a plurality of ephemeral content item collections based on a probability of a user selecting the ephemeral content item collection, each ephemeral content item collection of the plurality of ephemeral content item collections including one or more ephemeral content items. One or more ephemeral content item collections from the first ranking can be provided in an ephemeral content feed of the user. A selection by the user of an ephemeral content item collection provided in the ephemeral content feed can be received. A second ranking to rank each ephemeral content item collection of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection based on a probability of the user spending time on the ephemeral content item collection can be performed.

Abstract: Systems, methods, and non-transitory computer readable media can determine a value of a boosting parameter, wherein the boosting parameter is indicative of a value of feedback to a first account type. Respective scores of a plurality of content items that are candidates for a feed of a user can be determined, wherein the plurality of content items are associated with at least one of the first account type or a second account type. Respective scores of content items associated with the first account type in the plurality of content items can be adjusted based on the value of the boosting parameter. The plurality of content items can be ranked based on the respective scores of the plurality of content items.

Abstract: Systems, methods, and non-transitory computer readable media can generate an ephemeral content item from a live stream video that has concluded, wherein the ephemeral content item from the live stream video is included in an ephemeral content item collection. A plurality of ephemeral content item collections, including the ephemeral content item collection, can be ranked based on a machine learning model. At least one of the ranked plurality of ephemeral content item collections is provided in an ephemeral content feed of a user.

Abstract: Systems, methods, and non-transitory computer readable media can perform a first ranking to rank each ephemeral content item collection of a plurality of ephemeral content item collections based on a probability of a user selecting the ephemeral content item collection, each ephemeral content item collection of the plurality of ephemeral content item collections including one or more ephemeral content items. One or more ephemeral content item collections from the first ranking can be provided in an ephemeral content feed of the user. A selection by the user of an ephemeral content item collection provided in the ephemeral content feed can be received. A second ranking to rank each ephemeral content item collection of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection based on a probability of the user spending time on the ephemeral content item collection can be performed.

Abstract: Systems, methods, and non-transitory computer readable media can obtain a plurality of ephemeral content item collections, wherein each ephemeral content item collection of the plurality of ephemeral content item collections includes one or more ephemeral content items. A score for each ephemeral content item collection of the plurality of ephemeral content item collections can be determined based on a probability of a user selecting the ephemeral content item collection and a probability of the user spending time on the ephemeral content item collection. The plurality of ephemeral content item collections can be ranked based on the respective scores of the plurality of ephemeral content item collections.

Abstract: Systems, methods, and non-transitory computer readable media can perform a first ranking to rank each ephemeral content item collection of a plurality of ephemeral content item collections based on a probability of a user selecting the ephemeral content item collection. One or more ephemeral content item collections from the first ranking to provide in an ephemeral content feed of the user can be selected. A second ranking to rank each ephemeral content item collection of the plurality of ephemeral content item collections other than the selected ephemeral content item collections from the first ranking based on a probability of the user spending time on the ephemeral content item collection can be performed. One or more ephemeral content item collections from the second ranking to provide in the ephemeral content feed of the user can be selected.

Abstract: Systems, methods, and non-transitory computer readable media can obtain a plurality of content item collections, each content item collection of the plurality of content item collections including one or more content items. A score for each content item collection of the plurality of content item collections can be determined, based on a probability of a user spending time on at least one of the one or more content items included in the content item collection. The plurality of content item collections can be ranked based on respective scores. Access to at least one content item collection of the plurality of content item collections can be provided to the user based on the ranking.

Abstract: Systems, methods, and non-transitory computer readable media can determine a value of a boosting parameter, wherein the boosting parameter is indicative of a value of feedback to a first account type. Respective scores of a plurality of content items that are candidates for a feed of a user can be determined, wherein the plurality of content items are associated with at least one of the first account type or a second account type. Respective scores of content items associated with the first account type in the plurality of content items can be adjusted based on the value of the boosting parameter. The plurality of content items can be ranked based on the respective scores of the plurality of content items.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a first set of content items to be sent to a computing device of a user. A token is generated based at least in part on the first set of content items, the token being encoded with information for tracking content items sent to the computing device of the user. Data corresponding to the set of content items and the token is sent to the computing device of the 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: Systems, methods, and non-transitory computer-readable media can access a content feed of a user in a social networking system. At least one content item to be added to the content feed is determined. One or more content items are added to the content feed, the content items being added from an explore feed in the social networking system, wherein the explore feed includes various content items that were posted by users of the social networking system.

Abstract: A system for processing cursor movements for predictive fetching includes a notification server, a notification client module, and a movement processing and predictive fetching module. The notification server is coupled to other systems to receive notification information. The notification client module is coupled to retrieve notification information from the notification server. The movement processing and predictive fetching module is configured to capture and detect cursor movements on a client device. The movement processing and predictive fetching module determines the direction and deceleration of the cursor. If the movement is decelerating and in the direction of the notification button on the user interface, the movement processing and predictive fetching module generates a signal for the notification client module to pre-fetch notification information from the notification server.