Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.

Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.

Abstract: In aspects of selectable response options displayed based-on device grip position, a wireless device has a display screen to display an incoming notification associated with a device application, and actionable responses are associated with the incoming notification. The wireless device implements a grip detection module to detect a device grip position of a user grip holding the wireless device. The wireless device also implements a notification control module to determine the actionable responses of the incoming notification. The notification control module can also initiate to display selectable response options corresponding to the actionable responses associated with the incoming notification, where the selectable response options are displayable along a display edge of the display screen proximate the device grip position.

Abstract: For each retailer in the geographic region, an online system predicts a number of orders placed at the retailer and a capacity to fulfill orders during a forecast time period. The capacity of the retailer is predicted based on a number of pickers expected to be available to the retailer during the forecast time period. The online system determines demand for the services of a picker at the retailer based on a comparison of the predicted number of orders and the predicted capacity to fulfill those orders. The online system displays a user interactive map of the geographic region to the picker. The map displays a pin at the location of each retailer in the geographic region, which describes the categorization determined for the retailer. The picker selects a pin, which causes the user interactive map to display a notification characterizing the demand for services at the retailer.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: An input document data is received. The input document data is analyzed to automatically identify one or more response fields and one or more corresponding text labels laid out in the input document data. A conversation bot is automatically configured to provide one or more requests to provide one or more responses corresponding to the one or more response fields, receive the one or more responses, and store the one or more responses in a persistent computer storage.

Abstract: In order to facilitate the entity resolution and entity activity tracking and indexing, systems and methods include receiving first source records from a first database and second source records from a record database. A candidate set of second source records is determined by a heuristic search in the set of second source records. A candidate pair feature vector associated with each candidate pair of first and second source records is generated. An entity matching machine learning model predicts matching first source records for each candidate second source record based on the respective candidate pair feature vector. An aggregate quantity associated with the matching first source records is aggregated from a quantity associated with each first source record, and a quantity index for each candidate second source record is determined based the aggregate quantities. Each quantity index is displayed to a user.

Abstract: In aspects of selectable response options displayed based-on device grip position, a wireless device has a display screen to display an incoming notification associated with a device application, and actionable responses are associated with the incoming notification. The wireless device implements a grip detection module to detect a device grip position of a user grip holding the wireless device. The wireless device also implements a notification control module to determine the actionable responses of the incoming notification. The notification control module can also initiate to display selectable response options corresponding to the actionable responses associated with the incoming notification, where the selectable response options are displayable along a display edge of the display screen proximate the device grip position.

Abstract: Systems, methods, and non-transitory computer-readable media can determine that broadcasting of a live content stream has concluded. The concluded live content stream is converted to a post live media content item. The post live media content item corresponding to the concluded live content stream is published through the social networking system. The post live media content item remains accessible through the social networking system for a pre-determined period of time.

Abstract: In order to facilitate the entity resolution and entity activity tracking and indexing, systems and methods include receiving first source records from a first database and second source records from a record database. A candidate set of second source records is determined by a heuristic search in the set of second source records. A candidate pair feature vector associated with each candidate pair of first and second source records is generated. An entity matching machine learning model predicts matching first source records for each candidate second source record based on the respective candidate pair feature vector. An aggregate quantity associated with the matching first source records is aggregated from a quantity associated with each first source record, and a quantity index for each candidate second source record is determined based the aggregate quantities. Each quantity index is displayed to a user.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate the entity resolution and entity activity tracking and indexing, systems and methods include receiving first source records from a first database and second source records from a record database. A candidate set of second source records is determined by a heuristic search in the set of second source records. A candidate pair feature vector associated with each candidate pair of first and second source records is generated. An entity matching machine learning model predicts matching first source records for each candidate second source record based on the respective candidate pair feature vector. An aggregate quantity associated with the matching first source records is aggregated from a quantity associated with each first source record, and a quantity index for each candidate second source record is determined based the aggregate quantities. Each quantity index is displayed to a user.

Abstract: In order to facilitate the entity resolution and entity activity tracking and indexing, systems and methods include receiving first source records from a first database and second source records from a record database. A candidate set of second source records is determined by a heuristic search in the set of second source records. A candidate pair feature vector associated with each candidate pair of first and second source records is generated. An entity matching machine learning model predicts matching first source records for each candidate second source record based on the respective candidate pair feature vector. An aggregate quantity associated with the matching first source records is aggregated from a quantity associated with each first source record, and a quantity index for each candidate second source record is determined based the aggregate quantities. Each quantity index is displayed to a user.

Abstract: A source device includes various sensors, such as a GPS receiver. The source device provides operation context data to a server that indicates movement of the source device. The source device may also provide a movement context. The server also evaluates usage of data from the device by other applications or devices to determine a usage context. Based on the movement, operation, and usage contexts, the server selects a frequency at which data is collected for a sensor and a frequency at which the data is transmitted to the server. For example, where the device is not moving or is indoors, less location data is collected. Where no user is tracking the device, location data may be transmitted less frequently and may also be collected less frequently.

Abstract: A source device includes various sensors, such as a GPS receiver. The source device provides operation context data to a server that indicates movement of the source device. The source device may also provide a movement context. The server also evaluates usage of data from the device by other applications or devices to determine a usage context. Based on the movement, operation, and usage contexts, the server selects a frequency at which data is collected for a sensor and a frequency at which the data is transmitted to the server. For example, where the device is not moving or is indoors, less location data is collected. Where no user is tracking the device, location data may be transmitted less frequently and may also be collected less frequently.