Abstract: Aggregation of content based on user-generated topics is provided. Users may associate one or more topics with content items stored across various workloads and repositories. A topic may be a word or phrase of the user's choice, and may be utilized for discoverability of information and aggregation of content items. Topics and content items associated with topics may be acted on (e.g., a user may add or delete topics to associate with a content item, associate or disassociate content items with a topic, embed a set of content items or a stream of content items associated with a topic into other experiences, follow topics, etc.). Content items identified as related to a specific topic may be automatically suggested as possible content items of interest to the user. Additionally, when a user follows a topic, the user may be notified of any changes that occur to the topic.

Abstract: Aggregation of content based on user-generated topics is provided. Users may associate one or more topics with content items stored across various workloads and repositories. A topic may be a word or phrase of the user's choice, and may be utilized for discoverability of information and aggregation of content items. Topics and content items associated with topics may be acted on (e.g., a user may add or delete topics to associate with a content item, associate or disassociate content items with a topic, embed a set of content items or a stream of content items associated with a topic into other experiences, follow topics, etc.). Content items identified as related to a specific topic may be automatically suggested as possible content items of interest to the user. Additionally, when a user follows a topic, the user may be notified of any changes that occur to the topic.

Abstract: An information feed is provided that surfaces information items that are determined to be interesting or relevant to a user. The information feed may be updated with new information items each time the feed is accessed by the user. Information not surfaced to the user in the feed may be accessed by the user via one or more other means, but only those items having a sufficient importance to the user are surfaced in the feed. Items in the feed are sorted by order of importance or relevance to the user. When new items are added to the feed, they are sorted relative to each other, but are displayed separately from previous groupings of items added to the feed. Items may be reposted to different positions in the feed, and items may be manually removed from the feed if desired.

Abstract: An information feed is provided that surfaces information items that are determined to be interesting or relevant to a user. The information feed may be updated with new information items each time the feed is accessed by the user. Information not surfaced to the user in the feed may be accessed by the user via one or more other means, but only those items having a sufficient importance to the user are surfaced in the feed. Items in the feed are sorted by order of importance or relevance to the user. When new items are added to the feed, they are sorted relative to each other, but are displayed separately from previous groupings of items added to the feed. Items may be reposted to different positions in the feed, and items may be manually removed from the feed if desired.

Abstract: Aggregation of content based on user-generated topics is provided. Users may associate one or more topics with content items stored across various workloads and repositories. A topic may be a word or phrase of the user's choice, and may be utilized for discoverability of information and aggregation of content items. Topics and content items associated with topics may be acted on (e.g., a user may add or delete topics to associate with a content item, associate or disassociate content items with a topic, embed a set of content items or a stream of content items associated with a topic into other experiences, follow topics, etc.). Content items identified as related to a specific topic may be automatically suggested as possible content items of interest to the user. Additionally, when a user follows a topic, the user may be notified of any changes that occur to the topic.

Abstract: Recommending relevant content to a user based on personalized implicit and explicit activity signals aggregated for various content items is provided. A user is provided with situational awareness of content they may use by aggregating and displaying content that has been acted on by people the user works with most closely. Relationships between people and activities around content may be represented in a work graph which may be surfaced to the user. Content and relationship information pertaining to content may be surfaced to the user via a user interface. The user may query the content according to a variety of queries such as “popular with my colleagues,” “viewed by me” (i.e., the querying user), “worked on by me,” “most viewed,” and the like.

Abstract: Aggregation of content based on user-generated topics is provided. Users may associate one or more topics with content items stored across various workloads and repositories. A topic may be a word or phrase of the user's choice, and may be utilized for discoverability of information and aggregation of content items. Topics and content items associated with topics may be acted on (e.g., a user may add or delete topics to associate with a content item, associate or disassociate content items with a topic, embed a set of content items or a stream of content items associated with a topic into other experiences, follow topics, etc.). Content items identified as related to a specific topic may be automatically suggested as possible content items of interest to the user. Additionally, when a user follows a topic, the user may be notified of any changes that occur to the topic.

Abstract: Aggregation of content based on user-generated topics is provided. Users may associate one or more topics with content items stored across various workloads and repositories. A topic may be a word or phrase of the user's choice, and may be utilized for discoverability of information and aggregation of content items. Topics and content items associated with topics may be acted on (e.g., a user may add or delete topics to associate with a content item, associate or disassociate content items with a topic, embed a set of content items or a stream of content items associated with a topic into other experiences, follow topics, etc.). Content items identified as related to a specific topic may be automatically suggested as possible content items of interest to the user. Additionally, when a user follows a topic, the user may be notified of any changes that occur to the topic.

Abstract: A personalized view of insights into social activity surrounding a content item is provided that may indicate to a user why a given piece of content may be interesting to him/her. A user's activities, as well as the activities of others (e.g., colleagues of the user) may be shown, including such items as a total number of views, comments, followers, and likes associated with the content item. The insights view may be personalized based on the user's relationships with other users in association with the content item. The user may learn through the personalized view who among his/her relationships have commented, edited, shared, or liked the content item, as well as, how many of the user's colleagues have viewed the content item. In addition, through a search function that may be applied to the view content, a user may learn about related content items based on the social insights.

Abstract: Relevant files are determined for a user upon detecting a user action such as save, load, open, view, share, or comparable ones associated with a file. The relevancy may be determined through one or more queries based on, a number of criteria, where the queries are executed on local or remote data stores related to the user. For example, files on the local computing device of the user, files in an enterprise network associated with the user, files on a social network subscribed by the user may be evaluated for various relevancy criteria. Files determined to be relevant may be prioritized, ordered, and/or grouped for suggestion to the user and presented through a user interface of an application performing the detected action.

Abstract: Generation and presentation of visual representations of search results may be provided. In response to any type of search for which one or more content or information items are returned, a preview image is obtained or generated and displayed in a graphical user interface for allowing a user to preview documents or other information items returned for the search query. The displayed preview image may be a selectable object such that selection of the displayed preview image allows the user to navigate to or launch the content item, for example, a searched document, corresponding to the preview image.

Abstract: Aggregation of content based on user-generated topics is provided. Users may associate one or more topics with content items stored across various workloads and repositories. A topic may be a word or phrase of the user's choice, and may be utilized for discoverability of information and aggregation of content items. Topics and content items associated with topics may be acted on (e.g., a user may add or delete topics to associate with a content item, associate or disassociate content items with a topic, embed a set of content items or a stream of content items associated with a topic into other experiences, follow topics, etc.). Content items identified as related to a specific topic may be automatically suggested as possible content items of interest to the user. Additionally, when a user follows a topic, the user may be notified of any changes that occur to the topic.

Abstract: An information feed is provided that surfaces information items that are determined to be interesting or relevant to a user. The information feed may be updated with new information items each time the feed is accessed by the user. Information not surfaced to the user in the feed may be accessed by the user via one or more other means, but only those items having a sufficient importance to the user are surfaced in the feed. Items in the feed are sorted by order of importance or relevance to the user. When new items are added to the feed, they are sorted relative to each other, but are displayed separately from previous groupings of items added to the feed. Items may be reposted to different positions in the feed, and items may be manually removed from the feed if desired.

Abstract: A personalized view of insights into social activity surrounding a content item is provided that may indicate to a user why a given piece of content may be interesting to him/her. A user's activities, as well as the activities of others (e.g., colleagues of the user) may be shown, including such items as a total number of views, comments, followers, and likes associated with the content item. The insights view may be personalized based on the user's relationships with other users in association with the content item. The user may learn through the personalized view who among his/her relationships have commented, edited, shared, or liked the content item, as well as, how many of the user's colleagues have viewed the content item. In addition, through a search function that may be applied to the view content, a user may learn about related content items based on the social insights.

Abstract: Recommending relevant content to a user based on personalized implicit and explicit activity signals aggregated for various content items is provided. A user is provided with situational awareness of content they may use by aggregating and displaying content that has been acted on by people the user works with most closely. Relationships between people and activities around content may be represented in a work graph which may be surfaced to the user. Content and relationship information pertaining to content may be surfaced to the user via a user interface. The user may query the content according to a variety of queries such as “popular with my colleagues,” “viewed by me” (i.e., the querying user), “worked on by me,” “most viewed,” and the like.