Rukmini M Iyer

US Patent:

8229786, Jul 24, 2012

Filed:

Apr 6, 2010

Appl. No.:

12/755226

Inventors:

Ozgur Cetin - New York NY, US
Kannan Achan - Mountain View CA, US
Erick Cantu-Paz - Sunnyvale CA, US
Rukmini Iyer - Los Altos CA, US

Assignee:

Yahoo! Inc. - Sunnyvale CA

International Classification:

G06Q 30/00

US Classification:

705 141

Abstract:

Sponsored search advertising utilizes a click probability as one factor in selecting and ranking advertisements that are displayed with search results. The probability of click may also be referred to as a predicted click-through rate (“CTR”) that may be multiplied by an advertiser's bid for a particular advertisement to rank the display of advertisements. An accurate prediction of the click probability improves the potential revenue that is generated by advertisements in a pay per click system. Other advertising systems may benefit from an accurate and reliable estimate for an advertisement's probability of click in different environments and scenarios.

US Patent:

8392343, Mar 5, 2013

Filed:

Jul 21, 2010

Appl. No.:

12/840598

Inventors:

Ozgur Cetin - New York NY, US
Dongwei Cao - Los Angeles CA, US
Rukmini Iyer - Los Altos CA, US

Assignee:

Yahoo! Inc. - Sunnyvale CA

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

A machine-learning method for estimating probability of a click event in online advertising systems by computing and comparing an aggregated predictive model (a global model) and one or more data-wise sliced predictive models (local models). The method comprises receiving training data having a plurality of features stored in a feature set and constructing a global predictive model that estimates the probability of a click event for the processed feature set. Then, partitioning the global predictive model into one or more data-wise sliced training sets for training a local model from each of the data-wise slices, and then determining whether a particular local model estimates probability of click event for the feature set better than the global model. A given feature set may be collected from historical data, and may comprise a feature vector for a plurality of query-advertisement pairs and a corresponding indicator that represents a click on the advertisement.

US Patent:

8484077, Jul 9, 2013

Filed:

Sep 29, 2010

Appl. No.:

12/893939

Inventors:

Ozgur Cetin - New York NY, US
Eren Manavoglu - Menlo Park CA, US
Kannan Achan - Mountain View CA, US
Erick Cantu-Paz - Sunnyvale CA, US
Rukmini Iyer - Los Altos CA, US

Assignee:

Yahoo! Inc. - Sunnyvale CA

International Classification:

G06Q 30/00

US Classification:

705 141

Abstract:

A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e. g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination. Using an appropriate weighted distribution model, the combined predictive model reliably yields predictive estimates of occurrence of click events that are at least as good as the best predictive model in the slice-wise predictive model set.

US Patent:

20070112840, May 17, 2007

Filed:

Jun 29, 2006

Appl. No.:

11/479186

Inventors:

Chad Carson - Cupertino CA, US
Ashvin Kannan - Sunnyvale CA, US
Erick Cantu-Paz - Sunnyvale CA, US
Rukmini Iyer - Los Altos CA, US
Pero Subasic - Santa Clara CA, US
Christopher LuVogt - Sunnyvale CA, US
Christopher Leggetter - Belmont CA, US
Jan Pedersen - Los Altos Hills CA, US
David Ku - Palo Alto CA, US

Assignee:

Yahoo! Inc. - Sunnyvale CA

International Classification:

G06F 7/00

US Classification:

707102000

Abstract:

The present invention is directed towards systems and methods for predicting a frequency with which an advertisement displayed in response to a query will be selected. The method of the present invention comprises receiving analytics data associated with a display of one or more advertisements in response to one or more queries. One or more features associated with the one or more advertisements displayed in response to the one or more queries are identified. One or more functions are generated for predicting a frequency with which a given advertisement displayed in response to a query will be selected using the analytics data and features associated with the one or more advertisements displayed in response to the one or more queries.

US Patent:

20100017262, Jan 21, 2010

Filed:

Jul 18, 2008

Appl. No.:

12/176264

Inventors:

Rukmini Iyer - Los Altos CA, US
Hema Raghavan - Arlington MA, US

Assignee:

Yahoo! Inc. - Sunnyvale CA

International Classification:

G06Q 10/00
G06F 17/30

US Classification:

705 10, 707 2, 707E17009

Abstract:

The subject matter disclosed herein relates to predicting selection rates of web-based documents in response to a search query.

US Patent:

20110131157, Jun 2, 2011

Filed:

Nov 28, 2009

Appl. No.:

12/626892

Inventors:

Rukmini Iyer - Los Altos CA, US
Eren Manavoglu - Menlo Park CA, US
Hema Raghavan - Arlington MA, US

Assignee:

Yahoo! Inc. - Sunnyvale CA

International Classification:

G06F 17/30
G06F 15/18

US Classification:

706 12, 707759, 707706, 707E1707

Abstract:

An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.

US Patent:

20110131205, Jun 2, 2011

Filed:

Nov 28, 2009

Appl. No.:

12/626894

Inventors:

Rukmini Iyer - Los Altos CA, US
Eren Manavoglu - Menlo Park CA, US
Hema Raghavan - Arlington MA, US

Assignee:

Yahoo! Inc. - Sunnyvale CA

International Classification:

G06F 17/30

US Classification:

707728, 707E17064

Abstract:

An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.

US Patent:

20120290509, Nov 15, 2012

Filed:

Sep 16, 2011

Appl. No.:

13/234186

Inventors:

Larry Paul Heck - Los Altos CA, US
Rukmini Iyer - Los Altos CA, US
Gokhan Tur - Fremont CA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 11/00
G06F 15/18

US Classification:

706 11

Abstract:

Training for a statistical dialog manager may be provided. A plurality of log data associated with an intent may be received, and at least one step associated with completing the intent according to the plurality of log data may be identified. An understanding model associated with the intent may be created, including a plurality of queries mapped to the intent. In response to receiving a natural language query from a user that is associated with the intent a response to the user may be provided according to the understanding model.