Abstract: In one embodiment, a method performed by a client device includes receiving, at the client device, an input of n-grams, and identifying media items from a media-item index. This media-item index may index media items stored by the client device and respective keywords associated with each media item, and each media item may be identified based on one or more of its associated keywords matching at least one of the n-grams of the input. An auto-suggestion with the identified media items may then be presented responsive to the input.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: In one embodiment, a method performed by a client device includes receiving, at the client device, an input of n-grams, and identifying media items from a media-item index. This media-item index may index media items stored by the client device and respective keywords associated with each media item, and each media item may be identified based on one or more of its associated keywords matching at least one of the n-grams of the input. An auto-suggestion with the identified media items may then be presented responsive to the input.

Abstract: In one embodiment, a method includes receiving an input of n-grams from a client device, and identifying media items having keywords that match the n-grams from a media-item index that indexes a plurality of media items in association with respective keywords. A media-item score for each of the identified media items is calculated, and one or more of the identified media items having media-item scores greater than a threshold media-item score are returned to the client device.

Abstract: Systems, methods, and non-transitory computer readable media can obtain user navigation data associated with transitions by users between one or more pages associated with a system. Reduced dimensionality user navigation data can be generated based on the user navigation data. A plurality of clusters can be generated based on the reduced dimensionality user navigation data, wherein each cluster of the plurality of clusters corresponds to a user navigation pattern associated with the system.

Abstract: Systems, methods, and non-transitory computer readable media can obtain user navigation data associated with transitions by users between one or more pages associated with a system. Reduced dimensionality user navigation data can be generated based on the user navigation data. A plurality of clusters can be generated based on the reduced dimensionality user navigation data, wherein each cluster of the plurality of clusters corresponds to a user navigation pattern associated with the system.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: In one embodiment, a method includes sending, through a communications network, several volumes of notifications corresponding to a first notification type to multiple users and several volumes of notifications corresponding to a second notification type to multiple users. The method further determines visitation impacts of the volumes of notifications of the first and second notification types and trains a machine-learning model based on the visitation impacts. The machine-learning model generates an assessment of a likelihood of interaction by a recipient user with each of the notifications.

Abstract: Exemplary embodiments relate to techniques for determining social networking or messaging user affinity and engagement coefficients (e.g., a measure of the connectedness between two people in a network). The described techniques are particularly well-suited to cases in which only limited information is available, such as when a new user joins a network and only the user's contacts list is available. The available information may be used to determine a group of existing users to which the new user is connected. Some embodiments relate to calculating scores among these existing users in order to infer an affinity for the new user to the existing users. Other embodiments involve calculating bilateral scores that reflect a degree of mutual affinity between two users.

Abstract: Exemplary embodiments relate to techniques for determining social networking or messaging user affinity and engagement coefficients (e.g., a measure of the connectedness between two people in a network). The described techniques are particularly well-suited to cases in which only limited information is available, such as when a new user joins a network and only the user's contacts list is available. The available information may be used to determine a group of existing users to which the new user is connected. Some embodiments relate to calculating scores among these existing users in order to infer an affinity for the new user to the existing users. Other embodiments involve calculating bilateral scores that reflect a degree of mutual affinity between two users.

Abstract: Techniques for device configuration using messaging history information are described. In one embodiment, an apparatus may comprise a contact list component operative to determine a promotion contact list for the user account; a predicted interest component operative to determine a predicted communication interest for each contact on a promotion contact list; a predicted activity component operative to determine a user predicted likelihood of future activeness on the communication system; determine a contact predicted likelihood of future activeness on the communication system for each contact; and a contact ranking component operative to determine the ranking weight for each contact based on the predicted communication interest for each contact on the promotion contact list, the user predicted likelihood of future activeness on the communication system, and the contact predicted likelihood of future activeness on the communication system for each contact on the promotion contact list.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: In one embodiment, a method includes receiving an input of n-grams from a client device, and identifying media items having keywords that match the n-grams from a media-item index that indexes a plurality of media items in association with respective keywords. A media-item score for each of the identified media items is calculated, and one or more of the identified media items having media-item scores greater than a threshold media-item score are returned to the client device.

Abstract: In one embodiment, a method includes receiving, an input from a client device corresponding to n-grams; accessing a media-item index that indexes a plurality of media items with their respective keywords, each keyword being extracted from communications made by users of an online social network; identifying media items from the media-item index, each media item being identified based on its associated keywords matching the n-grams corresponding to the input; calculating a media-item score for each of the identified media items based on a number of occurrences in which the media item has been used in communications on the online social network in connection with its matched keywords; and sending, to the client device, data corresponding to the identified media items having a media-item score greater than a threshold media-item score.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: Exemplary methods, apparatuses, and systems for processing a search query of a user are detailed. For example, a search query may be received from a user at a social networking system, processed to generate a search result of a plurality of entity result cards and each result card that each include a plurality of order comments about the entity, a plurality of ordered images associated with the entity, contact information for the entity, wherein the comments and images take into account information about the user stored at the social networking system, and a result send to the user.

Abstract: Systems, methods, and non-transitory computer readable media can obtain user navigation data associated with transitions by users between one or more pages associated with a system. Reduced dimensionality user navigation data can be generated based on the user navigation data. A plurality of clusters can be generated based on the reduced dimensionality user navigation data, wherein each cluster of the plurality of clusters corresponds to a user navigation pattern associated with the system.

Abstract: In one embodiment, a method includes sending, through a communications network, several volumes of notifications corresponding to a first notification type to multiple users and several volumes of notifications corresponding to a second notification type to multiple users. The method further determines visitation impacts of the volumes of notifications of the first and second notification types and trains a machine-learning model based on the visitation impacts. The machine-learning model generates an assessment of a likelihood of interaction by a recipient user with each of the notifications.

Abstract: Exemplary embodiments relate to techniques for determining social networking or messaging user affinity and engagement coefficients (e.g., a measure of the connectedness between two people in a network). The described techniques are particularly well-suited to cases in which only limited information is available, such as when a new user joins a network and only the user's contacts list is available. The available information may be used to determine a group of existing users to which the new user is connected. Some embodiments relate to calculating scores among these existing users in order to infer an affinity for the new user to the existing users. Other embodiments involve calculating bilateral scores that reflect a degree of mutual affinity between two users.