Abstract: A video material editing method and apparatus are provided. The method includes: acquiring a plurality of video materials; and determining a video split-screen template. By exhibiting a plurality of video track clips formed based on the plurality of video materials on a video editing track, and displaying a video editing image formed from video images of the plurality of video track clips according to a video split-screen template, video images of the plurality of video track clips are exhibited through a plurality of split regions in the video editing image in a split-screen manner; timeline intervals corresponding to the plurality of video track clips on the video editing track at least partially overlap with each other, to render that the finally obtained video has a video split-screen splice effect.

Abstract: A message spacing system evenly distributes the communication of one or more notifications to a computing device communicatively coupled with an online service. The message spacing system also instructs an application residing on the computing device to display a badge notification. The badge notification indicates a number of pending notifications awaiting review by a member of the online service. The badge notification may be overlaid an icon corresponding to an application that the member uses to access or interact with the online service. The badge notification may also be overlaid on an icon displayed on a webpage, where the icon represents a selectable topic that the member may select to interact with the online service. The notifications that the messaging spacing system may send include offline notifications and online notifications.

Abstract: Machine learning techniques for multi-objective content item selection are provided. In one technique, resource allocation data is stored that indicates, for each campaign of multiple campaigns, a resource allocation amount that is assigned by a central authority. In response to receiving the content request, a subset of the campaigns is identified based on targeting criteria. Multiple scores are generated, each score reflecting a likelihood that a content item of the corresponding campaign will be selected. Based on the scores, a particular campaign from the subset is selected and the corresponding content item transmitted over a computer network to be displayed on a computing device. A resource allocation amount that is associated with the particular campaign is identified. A resource reduction amount associated with displaying the content item of the particular campaign is determined. The particular resource allocation is reduced based on the resource reduction amount.

Abstract: A message spacing system evenly distributes the communication of one or more notifications to a computing device communicatively coupled with an online service. The message spacing system also instructs an application residing on the computing device to display a badge notification. The badge notification indicates a number of pending notifications awaiting review by a member of the online service. The badge notification may be overlaid an icon corresponding to an application that the member uses to access or interact with the online service. The badge notification may also be overlaid on an icon displayed on a webpage, where the icon represents a selectable topic that the member may select to interact with the online service. The notifications that the messaging spacing system may send include offline notifications and online notifications.

Abstract: A social networking system receives from a member an item for sharing on the social networking system. The system determines whether the item for sharing is a first sharing for the member or whether the member has not shared an item for a time period that transgresses a threshold. When the item for sharing is a first sharing or a sharing that transgresses the threshold, the system marks the item for a promotion in a feed of another member of the social networking system.

Abstract: Techniques for measuring user engagement with online products and content are described. According to various embodiments, user sequence flow information is generated for a given member, based on online session log data, the user sequence flow information indicating a chronological sequence of steps and associated activities, each of the activities being any one of a product page view activity and a user action activity. The generation of the user sequence flow information may comprise grouping one or more sequential page views of a product described in the online session log data for the member into a product page view activity in the user sequence flow information, and grouping one or more sequential user actions performed in conjunction with a product described in the online session log data for the member into a user action activity in the user sequence flow information.

Abstract: A social networking system receives from a member an item for sharing on the social networking system. The system determines whether the item for sharing is a first sharing for the member or whether the member has not shared an item for a time period that transgresses a threshold. When the item for sharing is a first sharing or a sharing that transgresses the threshold, the system marks the item for a promotion in a feed of another member of the social networking system.

Abstract: The disclosed embodiments relate to a system for analyzing performance in an online professional network. During operation, the system receives time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval. The system also receives time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval. The system then performs a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics.

Abstract: In an embodiment, a method may comprise acquiring behavioral data of one or more users that suggests a causal relationship between source content and a request for destination content separated from the source content by one or more requests for intermediate content; and determining, based on the behavioral data, content to be placed on a web page. The method may also comprise determining, based on the behavioral data, a location on the web page to place the content. The method may comprise generating the behavioral data by associating a source URL with one or more subsequent URLs that are requested within a predetermined temporal proximity to a request for the source URL. The method may comprise generating the behavioral data by monitoring one or more user pseudonyms comprising a member identifier, a browser identifier, a computing platform identifier, a session identifier, a cookie, an IP address, or a geolocation.

Abstract: This disclosure relates to systems and methods that include obtaining from a member activity database, with a processor, data indicative of past interactions by members of a social network with content items associated with the social network, the content items including sponsored content items and organic content items. A positioning condition for content items is determined for individual members, based on the data as obtained, including sponsored content items and organic content items, on a user interface displayed to the individual members that is estimated to meet a optimization criterion, the optimization criterion including engagement with the content items by members individually and by members overall. The user interface is provided to one of the members based, at least in part, on the positioning condition.

Abstract: Techniques for measuring user engagement with online products and content are described. According to various embodiments, user sequence flow information is generated for a given member, based on online session log data, the user sequence flow information indicating a chronological sequence of steps and associated activities, each of the activities being any one of a product page view activity and a user action activity. The generation of the user sequence flow information may comprise grouping one or more sequential page views of a product described in the online session log data for the member into a product page view activity in the user sequence flow information, and grouping one or more sequential user actions performed in conjunction with a product described in the online session log data for the member into a user action activity in the user sequence flow information.

Abstract: The disclosed embodiments relate to a system for analyzing performance in an online professional network. During operation, the system receives time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval. The system also receives time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval. The system then performs a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics.

Abstract: The disclosed embodiments relate to a system for analyzing performance in an online professional network. During operation, the system receives time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval. The system also receives time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval. The system then performs a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics.