Aninda Ray - Bellevue WA, US Dmitriy Meyerzon - Bellevue WA, US
Assignee:
MICROSOFT CORPORATION - REDMOND WA
International Classification:
G06F 17/30 G06F 7/00
US Classification:
707728, 707723, 707732, 707E17075
Abstract:
Expertise mining features are provided based in part on the use of an expertise mining algorithm and expertise mining queries. A method of an embodiment operates to provide an expanded feedback query based in part on search results using an expertise mining query and a number of author-ranking heuristics used to rank authors and/or co-authors (e.g., primary authors, secondary authors, etc.) as part of an expertise mining operation. A search system of an embodiment includes an author ranker component to rank authors based in part on an expertise mining query and author-ranking heuristics, and a query expander component to provide expanded queries as part of identifying relevant search results. Other embodiments are also disclosed.
Enriched Search Features Based In Part On Discovering People-Centric Search Intent
Aninda Ray - Bellevue WA, US Dmitriy Meyerzon - Bellevue WA, US Uppinakuduru Raghavendra Udupa - Bangalore, IN
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
G06F 17/30
US Classification:
707706, 707E17109
Abstract:
A search environment of an embodiment includes name mining and matching features used in part to identify people-centric queries and provide an enriched search experience, but is not so limited. A method of an embodiment operates to provide an expanded query based in part on a geometric similarity measure, an edit distance measure, a string similarity measure, and a cumulative similarity measure. A search system of an embodiment includes a mined candidate generator component and a name matcher component used in part to identify name queries and provide an expanded query that includes original query terms and one or more valid mined names. Other embodiments are also disclosed.
Personalized Content Suggestions In Computer Networks
- Redmond WA, US Nikita Voronkov - Bothell WA, US Aninda Ray - Sammamish WA, US Alina Skarbovsky - Sammamish WA, US
International Classification:
G06F 16/9535 G06F 16/9032 G06F 16/248
Abstract:
Computer systems, devices, and associated methods of providing personalized content suggestion are disclosed herein. In one embodiment, a method performed by a search engine includes receiving an indication to perform a search for content items from a member. In response to the received indication, the search engine generates a list of content items represented as nodes in an interaction graph. The nodes uniquely correspond to the member from whom the indication to perform the search is received. In the nodes, at least one is indirectly connected to a node representing the member via at least one other node in the interaction graph.
- Redmond WA, US Ryan Nakhoul - Issaquah WA, US Dmitriy Meyerzon - Bellevue WA, US Naresh Kannan - Seattle WA, US David M. Cohen - Duvall WA, US Negin Arhami - Bellevue WA, US Aninda Ray - Sammamish WA, US
A cache that can be stored in a user partitioned region of storage and utilized to reduce the amount of time required to present content responsive to content requests is described. A request for content associated with a region of a user interface can be received and data corresponding to a list item in a cache can be accessed. Content associated with the data can be presented in the region of the user interface via a same presentation as a most recent presentation of the content. At a time subsequent to when the content is initially presented in the region, new data associated with the list item can be retrieved. In examples where the new data corresponds to updated data, the presentation can be modified based partly on the updated data and the new data can be written to the cache in a location corresponding to the list item.
Personalized Content Suggestions In Computer Networks
- Redmond WA, US Nikita Voronkov - Bothell WA, US Aninda Ray - Sammamish WA, US Alina Skarbovsky - Sammamish WA, US
International Classification:
G06F 17/30 G06F 17/30 G06F 17/30
Abstract:
Computer systems, devices, and associated methods of providing personalized content suggestion are disclosed herein. In one embodiment, a method performed by a search engine includes receiving an indication to perform a search for content items from a member. In response to the received indication, the search engine generates a list of content items represented as nodes in an interaction graph. The nodes uniquely correspond to the member from whom the indication to perform the search is received. In the nodes, at least one is indirectly connected to a node representing the member via at least one other node in the interaction graph.
Computerized Content Recommendation Based On Container Interactions
- Redmond WA, US David M. Cohen - Duvall WA, US Bjornstein Lilleby - Tromso, NO Aninda Ray - Sammamish WA, US Yauhen Shnitko - Sammamish WA, US Vidya Srinivasan - Issaquah WA, US Michael Taylor - Cambridge, GB Vidar Vikjord - Tromso, NO Nikita Voronkov - Bothell WA, US
International Classification:
G06F 3/0482 G06F 17/30 G06T 11/20
Abstract:
Relevant content can be surfaced via user interfaces presented via devices based at least partly on determining the relevant content from interactions between user(s), container(s), and/or container element(s). Techniques described herein include accessing data associated with interactions between a user and content (e.g., containers and container elements) associated with a collaborative computing environment. Based at least partly on the data, relationships between the user, container(s), and/or container element(s), and weights corresponding to individual relationships of the relationships can be determined. Techniques described herein include determining at least a portion of the content that is relevant to the user based at least partly on the weights and generating a content page associated with the collaborative computing environment configured with functionality to surface at least the portion of the content. The portion of the content can be prioritized on the user interface based at least partly on the weights.
- Redmond WA, US Dmitriy Meyerzon - Bellevue WA, US Yauhen Shnitko - Redmond WA, US Aninda Ray - Bellevue WA, US Manfred Berry - Tromso, NO Kjetil Bergstrand - Tromso, NO Johannes Gehrke - Tromso, NO Eirik Knutsen - Tromso, NO
International Classification:
G06F 17/30
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.
- Redmond WA, US Nikita Voronkov - Bothell WA, US Yauhen Shnitko - Redmond WA, US Aninda Ray - Bellevue WA, US Sebastian Blohm - Cambridge, GB Torbjorn Helvik - Tromso, NO
International Classification:
G06F 17/30
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.
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