Eren A Manavoglu

8108390, Jan 31, 2012

Dec 21, 2006

11/644176

Eren Manavoglu - Santa Clara CA, US
Alexandrin Popescul - Mountain View CA, US
Byron Dom - Los Gatos CA, US
Cliff Brunk - Menlo Park CA, US

Yahoo! Inc. - Sunnyvale CA

G06F 17/30

707736, 705 144

A system is described for targeting data to a site referenced on a page based on a condition. The system may include a processor, a memory, and an interface. The memory may be operatively connected to the processor and the interface and may store a data, a site, a condition, and a page containing content. The interface may be operatively connected to the memory and the processor and may communicate the page to a user. The processor may identify the data, site, condition, and page containing content. The processor may add the data to the page if the content of the page satisfies the condition.

8364525, Jan 29, 2013

Nov 30, 2010

12/956496

Divy Kothiwal - Sunnyvale CA, US
Kannan Achan - Mountian View CA, US
Eren Manavoglu - Menlo Park CA, US
Erick Cantu-Paz - Sunnyvale CA, US

Yahoo! Inc. - Sunnyvale CA

G06Q 30/00

705 141

A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically). A system for implementing the method includes aggregating data from a historical database using selectors such as a position selector, a click feature selector, an impression-advertiser-campaign-creative selector, and a commercial intent selector.

8484077, Jul 9, 2013

Sep 29, 2010

12/893939

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

Yahoo! Inc. - Sunnyvale CA

G06Q 30/00

705 141

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.

20080172399, Jul 17, 2008

Jan 17, 2007

11/624072

Liang-Yu Chi - San Francisco CA, US
Dmitry Yurievich Pavlov - San Jose CA, US
Yun Fu - Sunnyvale CA, US
Eren Manavoglu - Menlo Park CA, US
Paul Heymann - Menlo Park CA, US
Zhichen Xu - San Jose CA, US

G06F 17/30

707100, 707E17005

The present invention is directed towards systems and method for organization of bookmarks. The method according to one embodiment comprises retrieving one or more bookmarks associated with one or more content items, a given bookmark generated by a user of a client device and identifying one or more tags associated with one or uniform resource locators corresponding to the or more bookmarks. A bookmark folder hierarchy is created through use of a clustering algorithm on the basis of the one or more tags associated with the one or more uniform resource locators corresponding to the one or more bookmarks.

20100306161, Dec 2, 2010

May 29, 2009

12/474668

Ye Chen - Sunnyvale CA, US
Dmitry Pavlov - San Jose CA, US
John Canny - Berkeley CA, US
Eren Manavoglu - Menlo Park CA, US

Yahoo! Inc. - Sunnyvale CA

G06N 7/02
G06Q 30/00
G06N 5/02
G06Q 10/00
G06F 17/30
G06F 15/18

706 52, 705 1441, 705 1452, 705 1454, 706 46, 706 12

Methods and systems are provided for predicting click through rate in connection with a particular user, keyword-based query, and advertisement using a probabilistic latent variable model. Click through rate may be predicted based on historical sponsored search activity information. Predicted click through rate may be used as a factor in determining advertisement rank.

20110125572, May 26, 2011

Nov 25, 2009

12/626462

Erick Cantu-Paz - Sunnyvale CA, US
Eren Manavoglu - Menlo Park CA, US

YAHOO! INC. - Sunnyvale CA

G06Q 30/00
G06F 17/30
G06Q 10/00

705 1443, 705 1444, 705 1445, 705 1446, 707713, 707E17017

Search and advertising systems may be optimized through the use of user feedback. Selected parameters such as ranking, filtering, placement, and pricing may be optimized to achieve certain objectives. The optimization may include real-time user monitoring of multiple configurations with various parameters. In one embodiment, a subset of user queries may be assigned to a particular configuration for monitoring and measuring the real-time performance of that configuration. The performance for multiple configurations may be used to identify optimal settings.

20110131157, Jun 2, 2011

Nov 28, 2009

12/626892

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

Yahoo! Inc. - Sunnyvale CA

G06F 17/30
G06F 15/18

706 12, 707759, 707706, 707E1707

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.

20110131205, Jun 2, 2011

Nov 28, 2009

12/626894

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

Yahoo! Inc. - Sunnyvale CA

G06F 17/30

707728, 707E17064

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.