Abstract: A user associates other users or content with a representation of a topic area, in a computing system. Profile associations between users and the topic areas are generated by detecting that users are associated with the topic areas, or that users have added content to the topic areas. User profile information is automatically updated based upon the profile associations generated.

Abstract: Embodiments are configured to provide information based on a user query. In an embodiment, a system includes a search component having a ranking component that can be used to rank search results as part of a query response. In one embodiment, the ranking component includes a ranking algorithm that can use the length of documents returned in response to a search query to rank search results.

Abstract: Documents are ranked with topics within a graph. A user, a tag, and a document are placed as nodes in a graph. One or more relationships are established between the user, the tag, and the document. The nodes are connected with edges acting as the one or more relationships. The tag is promoted into a topic based on the one or more relationships.

Abstract: Document tags are rapidly indexed using a text based index and a graph index. A tag signal is received. A tag and a type of the tag that are located in the tag signal are stored in a data store. The tag is indexed as a tag document in the text based index. One or more relationships between the tag and a content document are managed in the graph index.

Abstract: A query pipeline for an enterprise search system is configurable by a user of the system. A user may create rules for custom query transformation and parallel query generation, federation of queries, mixing of results and application of display layouts to the received search results. A user interface (UI) assists a user in configuring the search pipeline. For example, a user may enter condition action rules for queries that affect how a query is transformed, how parallel queries are generated, how queries are federated, how search results are ranked and displayed, how rules are ordered and the like.

Abstract: Technologies are described herein for ordering messages. A computer-implemented method for ordering of messages may include collecting training data related to users of a messaging system, generating a label for a message, generating feature values based on the training data and the message, and training a relevance model based on the feature values using a machine learning algorithm. The relevance model is configured to rank new message candidates received by the messaging system for display by the messaging system.

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 system for ranking documents based on activity level is provided. A document promotion system generates a view score for a document based on the number of times the document was viewed and a freshness score for the document based on when the document was last updated. The document promotion system then generates an activity score for the document based on the view score and the freshness score for the document. The activity score for a document represents the level of activity associated with the document. The document promotion system ranks documents based on their generated activity scores and provides the documents to a user in the order of the ranking.

Abstract: A system for presenting help information relating to a user of a computer program based on context of the computer program is provided. The system receives an indication of a generic request and identifies the context of the computer program at the time of the request. The context may include information that is currently being displayed by the computer program and metadata relating to the user and the computer program. The system selects help information based on the context such that the selected help information varies based on information that is currently being displayed and the metadata, The system then presents the help information to the user.

Abstract: A system for scoring a discussion thread having posts in a question-and-answer form is provided. The system receives a query and then identifies features of the discussion thread that may include one or more term features and one or more non-term features. A term feature may be a combined post feature that combines the terms of multiple posts to treat them as a single document. A term feature may be a question feature that contains the question of the discussion thread. A term feature may also be a question post feature that contains the terms of the post that relate to the question of the post. The system then generates a feature score for each feature and combines the feature scores into a relevance score indicating the relevance of the discussion thread to the query. The system may also use the relevance score when ranking discussion threads.

Abstract: Technologies are described herein for ranking documents with social tags. A number ranking feature containing a number of times a document was tagged is received. A textual property ranking feature containing a union of each social tag associated with the document is also received. The number ranking feature is transformed into a static input value. Further, the textual property ranking feature is transformed into a dynamic input value. A document rank for the document is determined by inputting the static input value and/or the dynamic input value into a ranking function.

Abstract: A set of index keys is included in an index search system that are associated with the scope of the search rather than the content of the documents that are the target of the search. These scope related index keys, or scope keys allows the scope of the search to be selected, reducing the number of documents that a search is required to sift through to obtain results. Furthermore, compound scopes are recognized and stored such that an index of complex search scopes is provided to eliminate rehashing of the searches based on these complex search scopes.

Abstract: Architecture for extracting document information from documents received as search results based on a query string, and computing an edit distance between the data string and the query string. The edit distance is employed in determining relevance of the document as part of result ranking by detecting near-matches of a whole query or part of the query. The edit distance evaluates how close the query string is to a given data stream that includes document information such as TAUC (title, anchor text, URL, clicks) information, etc. The architecture includes the index-time splitting of compound terms in the URL to allow the more effective discovery of query terms. Additionally, index-time filtering of anchor text is utilized to find the top N anchors of one or more of the document results. The TAUC information can be input to a neural network (e.g., 2-layer) to improve relevance metrics for ranking the search results.

Abstract: An approach is described for performing a name search using a name search operation and a ranking operation. The name search operation may take text as input and apply a fuzzy matching operation and a lookup operation to generate a collection of candidate names with respective probability scores. In other cases, speech or handwriting recognition may generate the collection of candidate names and probability scores. The ranking operation may then rank these candidate names using a ranking function. The ranking function may rank the candidate names based on the probability scores associated with the names and at least one other factor. One such factor may reflect whether information provided by a user matches profile information associated with a candidate name under consideration. Another factor may reflect an extent of a nexus between the user and a person associated with the candidate name. Other types of factors can be used.

Abstract: Embodiments are directed to ranking search results using a junk profile. For a given corpus of documents, one or more junk profiles may be created and maintained. The junk profile provides reference metrics to represent known junk documents. For example, a junk profile may comprise a dictionary of document data that is automatically inserted into documents created using a particular system or template. A junk profile may also comprise one or more representations (e.g., histograms) of a distribution of a particular junk variable for known junk documents. The junk profile provides a usable representation of known junk documents, and the present systems and methods employ the junk profile to predict the likelihood that documents in the corpus are junk. In embodiments, junk scores are calculated and used to rank such documents higher or lower in response to a search query.

Abstract: This disclosure describes methods and systems for searching documents in a multi-tenant hosting environment. According to embodiments, to conserve hardware resources, a plurality of documents associated with a plurality of tenants may be mapped to the same search index in the multi-tenant hosting environment. In order to search documents associated only with a single tenant in the multi-tenant hosting environment, a tenant identifier is prepended to every key stored in the search index that is associated with the plurality of documents of the single tenant. Moreover, where one document links to another document within the multi-tenant hosting environment, the link is stored in a web graph when a source tenant identifier matches a target tenant identifier for the link. According to embodiments, when conducting a search, the link is resolved only if the link is stored in the web graph.

Abstract: Embodiments are configured to provide information relevant to individuals of interest to a searching user. In an embodiment, a method includes identifying relevant individuals of a network using a relevance model that includes the use of a number of managed properties and ranking features to identify relevant individuals of a defined network. The relevance model of one embodiment is defined by a schema that includes a textual matching ranking feature, social distance ranking feature, a levels to top ranking feature, and a proximity ranking feature.

Abstract: This disclosure describes methods and systems for searching documents in a multi-tenant hosting environment. According to embodiments, to conserve hardware resources, a plurality of documents associated with a plurality of tenants may be mapped to the same search index in the multi-tenant hosting environment. In order to search documents associated only with a single tenant in the multi-tenant hosting environment, a tenant identifier is prepended to every key stored in the search index that is associated with the plurality of documents of the single tenant. Moreover, where one document links to another document within the multi-tenant hosting environment, the link is stored in a web graph when a source tenant identifier matches a target tenant identifier for the link. According to embodiments, when conducting a search, the link is resolved only if the link is stored in the web graph.

Abstract: An approach is described for performing a name search using a name search operation and a ranking operation. The name search operation may take text as input and apply a fuzzy matching operation and a lookup operation to generate a collection of candidate names with respective probability scores. In other cases, speech or handwriting recognition may generate the collection of candidate names and probability scores. The ranking operation may then rank these candidate names using a ranking function. The ranking function may rank the candidate names based on the probability scores associated with the names and at least one other factor. One such factor may reflect whether information provided by a user matches profile information associated with a candidate name under consideration. Another factor may reflect an extent of a nexus between the user and a person associated with the candidate name. Other types of factors can be used.

Abstract: A query received from a user is directed to a particular search application (e.g. an Enterprise search portal) that is associated with a result source from which to retrieve results. The received query may be federated to additional result sources when the received query is determined to be a popular query in a result source. Query logs associated with the additional result sources are analyzed to determine when a query is popular as compared to the original result source. The query may be altered before being executed that uses one or more of the additional result sources. When the query (altered/unaltered) is determined to be popular for any of the additional result sources as compared to the original result source, the query is executed using that additional result source.