Panayiotis Tsaparas

8275726, Sep 25, 2012

Mar 30, 2009

12/414065

Panayiotis Tsaparas - Palo Alto CA, US
Panagiotis Papadimitriou - Santa Cruz CA, US
Ariel D. Fuxman - Mountain View CA, US
Lise C. Getoor - Takoma Park MD, US
Rakesh Agrawal - San Jose CA, US

Microsoft Corporation - Redmond WA

G06E 1/00
G06E 3/00
G06F 15/18
G06G 7/00

706 20, 706 45, 706 62

As provided herein objects from a source catalog, such as a provider's catalog, can be added to a target catalog, such as an enterprise master catalog, in a scalable manner utilizing catalog taxonomies. A baseline classifier determines probabilities for source objects to target catalog classes. Source objects can be assigned to those classes with probabilities that meet a desired threshold and meet a desired rate. A classification cost for target classes can be determined for respective unassigned source objects, which can comprise determining an assignment cost and separation cost for the source objects for respective desired target classes. The separation and assignment costs can be combined to determine the classification cost, and the unassigned source objects can be assigned to those classes having a desired classification cost.

8612432, Dec 17, 2013

Jun 16, 2010

12/816389

Krishnaram N. G. Kenthapadi - Mountain View CA, US
Panayiotis Tsaparas - San Francisco CA, US
Sreenivas Gollapudi - Cupertino CA, US
Rakesh Agrawal - San Jose CA, US

Microsoft Corporation - Redmond WA

G06F 7/00
G06F 17/30
G06F 15/18

707731, 706 14

A tree structure has a node associated with each category of a hierarchy of item categories. Child nodes of the tree are associated with sub-categories of the categories associated with parent nodes. Training data including received queries and indicators of a selected item category for each received query is combined with the tree structure by associating each query with the node corresponding to the selected category of the query. When a query is received, a classifier is applied to the nodes to generate a probability that the query is intended to match an item of the category associated with the node. The classifier is applied until the probability is below a threshold. One or more categories associated with the nodes that are closest to the intent of the received query are selected and indicators of items of those categories that match the received query are output.

8612472, Dec 17, 2013

Dec 16, 2009

12/639021

Krishnaram N. G. Kenthapadi - Mountain View CA, US
Indraneel Mukherjee - Princeton NJ, US
Stelios Paparizos - Mountain View CA, US
Panayiotis Tsaparas - San Francisco CA, US

Microsoft Corporation - Redmond WA

G06F 17/30

707769

A query may be received at a computing device through a network. One or more attribute values that are preferences for a subset of the one or more terms of the query may be identified by the computing device. One or more products or services having associated attributes that have values that match a subset of the identified attribute values may be identified by the computing device, and a subset of the identified products or services may be presented by the computing device through the network. Implementations may also identify latent preferences, that is, preferences that are found for a query even where such a preference is not explicitly part of a term or token of the query.

20090228353, Sep 10, 2009

Mar 5, 2008

12/042531

KANNAN ACHAN - Mountain View CA, US
ARIEL FUXMAN - San Jose CA, US
RAKESH AGRAWAL - San Jose CA, US
PANAYIOTIS TSAPARAS - Palo Alto CA, US

MICROSOFT CORPORATION - Redmond WA

G06Q 30/00
G06F 7/10
G06F 17/30
G06F 7/38
G06F 7/52
G06F 7/556

705 14, 707 4, 708200, 708650, 708606, 707E17108

Methods are provided for the classification of search engine queries and associated documents based on search engine query click logs. One or more seed documents or queries are provided that contain content that is representative of a category. A query click log containing information regarding queries entered by at least one user into the search engine and documents subsequently clicked in search engine results corresponding with the queries is analyzed to determine which one or more queries resulted in clicks on the seed documents. Information is stored associating the one or more queries with the category if they resulted in clicks on the seed documents.

20090306996, Dec 10, 2009

Jun 5, 2008

12/133370

Panayiotis Tsaparas - Palo Alto CA, US
Krishnaram N. G. Kenthapadi - Mountain View CA, US
Alan Halverson - Sunnyvale CA, US

MICROSOFT CORPORATION - Redmond WA

G06Q 99/00

705 1

A social network may be used to determine a rating of a user with no prior history. The ratings for unrated nodes may be inferred from the existing ratings of users associated with the unrated node in either or both the underlying social network or other social networks. Additionally in some implementations, the effect of the rating of a rated node to an unrated node diminishes as the strength of their relationships decreases. In some cases, a social network may be modeled as an electrical network, and ratings may be modeled as voltages on the nodes of the social network, relationships in the social network may be modeled as connections in the electrical network, and in some cases the strength of relationships may be modeled as conductance of the connections. Ratings for nodes may be determined using Kirchhoff's Law and in some cases by solving a set of linear equations or by propagating positive and negative ratings using a random walk with absorbing states.

20090313286, Dec 17, 2009

Jun 17, 2008

12/140272

Nina Mishra - Newark CA, US
Rakesh Agrawal - San Jose CA, US
Sreenivas Gollapudi - Cupertino CA, US
Alan Halverson - Sunnyvale CA, US
Krishnaram N. G. Kenthapadi - Mountain View CA, US
Rina Panigrahy - Mountain View CA, US
John C. Shafer - Los Altos CA, US
Panayiotis Tsaparas - Palo Alto CA, US

MICROSOFT CORPORATION - Redmond WA

G06F 7/00
G06F 17/30

707102, 707E17005

Data from a click log may be used to generate training data for a search engine. The pages clicked as well as the pages skipped by a user may be used to assess the relevance of a page to a query. Labels for training data may be generated based on data from the click log. The labels may pertain to the relevance of a page to a query.

20110173191, Jul 14, 2011

Jan 14, 2010

12/687127

Panayiotis Tsaparas - San Francisco CA, US
Yue Lu - Urbana IL, US
Alexandros Ntoulas - Mountain View CA, US
Livia Polanyi - Palo Alto CA, US

Microsoft Corporation - Redmond WA

G06F 17/30

707723, 707E17084

User generated reviews and scores associated with the reviews may be used to train a review scoring model with textual features of the reviews. The review scoring model may be used to predict scores for newly received reviews. One or more constraints based on social networking application data associated with an author of a review may be used to adjust the predicted score of the review.

20110184893, Jul 28, 2011

Jan 27, 2010

12/694294

Stelios Paparizos - Mountain View CA, US
Nikolaos Sarkas - Toronto, CA
Panayiotis Tsaparas - San Francisco CA, US

Microsoft Corporation - Redmond WA

G06F 7/10
G06F 17/30
G06F 15/18

706 12, 707780, 707768, 707E17014, 707769, 707E17081

A query may be received at a computing device and may include one or more terms. For each set of structured data tuples, a set of tokens may be determined from the terms of the query by the computing device based on attribute values of attributes associated with the structured data tuples in the set of structured data tuples. An annotated query may be determined from each of the sets of tokens. A probability score may be determined for each of the determined annotated queries. The annotated query having the highest determined probability score may be selected, and one or more structured data tuples may be identified from the structured data tuples that have attributes with attribute values that match one or more tokens of the selected annotated query.