Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing provider device balance graphs reflecting device utilization ratio between provider devices and requester devices. In particular, in one or more embodiments, the disclosed systems determine and utilize probabilities to generate and provide provider device balance graphs based on probability distributions for device utilization ratios. The disclosed systems can apply various scaling models to address seasonality, special events, and/or differences between regions. The disclosed systems can also apply thresholds to generate device incentive graphs that more efficiently deploy provider devices across a transportation matching system.

Abstract: The present application discloses systems, methods, and computer-readable media that utilize computing devices to model multi-outcome transportation-value metrics that account for spatio-temporal trajectories across locations, times and other contextual features, and then utilize computer networks to dispatch provider devices to locations based on the multi-outcome transportation-value metrics. Moreover, the disclosed systems can utilize these multi-outcome transportation-value metrics and/or other models to manage and utilize dynamic transportation dispatch modes to more efficiently align provider devices and requestor devices across computer networks. For instance, the disclosed system can dispatch a provider device based on a discounted multi-outcome transportation-value metric.

Abstract: In one embodiment, a method includes one or more computing devices ranking multiple users of a social-networking system based on one or more criteria associated with a spotlight content item, wherein the spotlight content item is associated with a node in a social graph associated with the social-networking system, and wherein at least one of the criteria is based on social-networking information for each of the user, verifying the ranked users, wherein the verifying comprises confirming or eliminating each of the ranked users based on recent interactions by the ranked users with social-networking information corresponding to the node associated with the spotlight content item, and sending a notification about the spotlight content item to each of the confirmed users in accordance with a batching protocol, wherein the batching protocol is determined based at least in part on rankings for each of the confirmed users.

Abstract: This disclosure describes a pickup location determination system that determines a pickup location for a received transportation request by filtering out door points based on various factors and by utilizing a pickup location model to select a pickup location from the filtered door points. For example, the disclosed systems generate door points relative to intersection points within a request radius of a request location associated with a received transportation request. The disclosed systems generate potential pickup locations by filtering out door points that are impractical and/or inefficient based on proximity to other door points, locations relative to venues, locations relative to side streets, and/or locations between parallel road segments (e.g., medians). The disclosed systems further utilize a pickup location model to select a pickup location from the potential pickup locations.

Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: In one embodiment, a method includes one or more computing devices ranking multiple users of a social-networking system based on one or more criteria associated with a spotlight content item, wherein the spotlight content item is associated with a node in a social graph associated with the social-networking system, and wherein at least one of the criteria is based on social-networking information for each of the user, verifying the ranked users, wherein the verifying comprises confirming or eliminating each of the ranked users based on recent interactions by the ranked users with social-networking information corresponding to the node associated with the spotlight content item, and sending a notification about the spotlight content item to each of the confirmed users in accordance with a batching protocol, wherein the batching protocol is determined based at least in part on rankings for each of the confirmed users.

Abstract: In one embodiment, a method includes one or more computing devices ranking multiple users of a social-networking system based on one or more criteria associated with a spotlight content item, wherein the spotlight content item is associated with a node in a social graph associated with the social-networking system, and wherein at least one of the criteria is based on social-networking information for each of the user, verifying the ranked users, wherein the verifying comprises confirming or eliminating each of the ranked users based on recent interactions by the ranked users with social-networking information corresponding to the node associated with the spotlight content item, and sending a notification about the spotlight content item to each of the confirmed users in accordance with a batching protocol, wherein the batching protocol is determined based at least in part on rankings for each of the confirmed users.

Abstract: In one embodiment, a method includes accessing for a user one or more electronic notifications that include information about a social network. The social network includes a plurality of nodes connected by a plurality of edges, with at least one node corresponding to the user. The method further includes determining, for each of the electronic notifications, a score that estimates whether a user interaction with the social network will result if the electronic notification is provided to the user. The method further includes determining, based at least in part on the determined scores, an action to take with respect to the electronic notifications.

Abstract: Systems, methods, and non-transitory computer-readable media receive a source video having a source file size. A bit rate at which to encode the source video is determined. The source video is encoded at the determined bit rate to produce an encoded video having a file size less than the source file size. A video quality metric for the encoded video is determined. Whether or not the video quality metric for the encoded video is within an allowable deviation from a target quality metric is determined. The source video is encoded at another determined bit rate when the video quality metric for the encoded video is outside the allowable deviation from the target quality metric. Determining the bit rate and encoding the source video at the determined bit rate can repeat iteratively until the video quality metric for the encoded video is within the allowable deviation from the target quality metric.

Abstract: Systems, methods, and non-transitory computer-readable media receive a source video having a source file size. A bit rate at which to encode the source video is determined. The source video is encoded at the determined bit rate to produce an encoded video having a file size less than the source file size. A video quality metric for the encoded video is determined. Whether or not the video quality metric for the encoded video is within an allowable deviation from a target quality metric is determined. The source video is encoded at another determined bit rate when the video quality metric for the encoded video is outside the allowable deviation from the target quality metric. Determining the bit rate and encoding the source video at the determined bit rate can repeat iteratively until the video quality metric for the encoded video is within the allowable deviation from the target quality metric.

Abstract: Systems, methods, and non-transitory computer-readable media receive a source video having a source file size. A bit rate at which to encode the source video is determined. The source video is encoded at the determined bit rate to produce an encoded video having a file size less than the source file size. A video quality metric for the encoded video is determined. Whether or not the video quality metric for the encoded video is within an allowable deviation from a target quality metric is determined. The source video is encoded at another determined bit rate when the video quality metric for the encoded video is outside the allowable deviation from the target quality metric. Determining the bit rate and encoding the source video at the determined bit rate can repeat iteratively until the video quality metric for the encoded video is within the allowable deviation from the target quality metric.

Abstract: Systems, methods, and non-transitory computer-readable media receive a source video having a source file size. A bit rate at which to encode the source video is determined. The source video is encoded at the determined bit rate to produce an encoded video having a file size less than the source file size. A video quality metric for the encoded video is determined. Whether or not the video quality metric for the encoded video is within an allowable deviation from a target quality metric is determined. The source video is encoded at another determined bit rate when the video quality metric for the encoded video is outside the allowable deviation from the target quality metric. Determining the bit rate and encoding the source video at the determined bit rate can repeat iteratively until the video quality metric for the encoded video is within the allowable deviation from the target quality metric.

Abstract: In one embodiment, a method includes one or more computing devices ranking multiple users of a social-networking system based on one or more criteria associated with a spotlight content item, wherein the spotlight content item is associated with a node in a social graph associated with the social-networking system, and wherein at least one of the criteria is based on social-networking information for each of the user, verifying the ranked users, wherein the verifying comprises confirming or eliminating each of the ranked users based on recent interactions by the ranked users with social-networking information corresponding to the node associated with the spotlight content item, and sending a notification about the spotlight content item to each of the confirmed users in accordance with a batching protocol, wherein the batching protocol is determined based at least in part on rankings for each of the confirmed users.

Abstract: Systems, methods, and non-transitory computer-readable media receive a source video having a source file size. A bit rate at which to encode the source video is determined. The source video is encoded at the determined bit rate to produce an encoded video having a file size less than the source file size. A video quality metric for the encoded video is determined. Whether or not the video quality metric for the encoded video is within an allowable deviation from a target quality metric is determined. The source video is encoded at another determined bit rate when the video quality metric for the encoded video is outside the allowable deviation from the target quality metric. Determining the bit rate and encoding the source video at the determined bit rate can repeat iteratively until the video quality metric for the encoded video is within the allowable deviation from the target quality metric.