Wei Chen Chu

8244517, Aug 14, 2012

Nov 7, 2008

12/267534

H. Scott Roy - Palo Alto CA, US
Raghunath Ramakrishnan - Madison WI, US
Pradheep Elango - Mountain View CA, US
Nitin Motgi - Santa Clara CA, US
Deepak K. Agarwal - Sunnyvale CA, US
Wei Chu - Sunnyvale CA, US
Bee-Chung Chen - Mountain View CA, US

Yahoo! Inc. - Sunnyvale CA

G06F 9/45

703 22

Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

8301624, Oct 30, 2012

Mar 31, 2009

12/416036

Seung-Taek Park - San Jose CA, US
Wei Chu - Sunnyvale CA, US
Todd Beaupre - Santa Monica CA, US
Deepak K. Agarwal - Sunnyvale CA, US
Scott Roy - Palo Alto CA, US
Raghu Ramakrishnan - Los Altos CA, US

Yahoo! Inc. - Sunnyvale CA

G06F 17/30

707723, 707802

A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined. Based at least in part on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

8442929, May 14, 2013

Nov 5, 2009

12/613503

Seung-Taek Park - San Jose CA, US
Wei Chu - Sunnyvale CA, US
Wei Du - Burbank CA, US
Uminder Singh - Mountain View CA, US
Jessi Dong - Saratoga CA, US

Yahoo! Inc. - Sunnyvale CA

G06F 17/00
G06N 5/00

706 45, 715753, 707749

Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for each other are determined to have some similarity to each other, even if very few or no users who selected one of those items ever selected the other of those items. A first item that is determined to be sufficiently similar to second item in this manner may be recommended to a user who has selected the second item as potentially also being of interest to that user.

8560293, Oct 15, 2013

Aug 8, 2012

13/569728

H. Scott Roy - Palo Alto CA, US
Raghunath Ramakrishnan - Madison WI, US
Pradheep Elango - Mountain View CA, US
Nitin Motgi - Santa Clara CA, US
Deepak K. Agarwal - Sunnyvale CA, US
Wei Chu - Sunnyvale CA, US
Bee-Chung Chen - Mountain View CA, US

Yahoo! Inc. - Sunnyvale CA

G06F 17/50

703 13

Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

20100121801, May 13, 2010

Nov 7, 2008

12/267538

H. Scott Roy - Palo Alto CA, US
Nitin Motgi - Santa Clara CA, US
Deepak K. Agarwal - Sunnyvale CA, US
Wei Chu - Sunnyvale CA, US
Bee-Chung Chen - Mountain View, TW

G06N 5/02
G06F 17/00
G06N 7/00

706 46, 715234, 706 59

Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

20100125585, May 20, 2010

Nov 17, 2008

12/272607

Wei Chu - Sunnyvale CA, US
Seung-Taek Park - San Jose CA, US
Raghu Ramakrishnan - Los Altos CA, US
Bee-Chung Chen - Mountain View CA, US
Deepak K. Agarwal - Sunnyvale CA, US
Pradheep Elango - Mountain View CA, US
Scott Roy - Palo Alto CA, US
Todd Beaupre - Sunnyvale CA, US

Yahoo! Inc. - Sunnyvale CA

G06F 17/30
G06F 7/06
G06F 7/00

707748, 708207, 707E17017, 707E17046

Information with respect to users, items, and interactions between the users and items is collected. Each user is associated with a set of user features. Each item is associated with a set of item features. An expected score function is defined for each user-item pair, which represents an expected score a user assigns an item. An objective represents the difference between the expected score and the actual score a user assigns an item. The expected score function and the objective function share at least one common variable. The objective function is minimized to find best fit for some of the at least one common variable. Subsequently, the expected score function is used to calculate expected scores for individual users or clusters of users with respect to a set of items that have not received actual scores from the users. The set of items are ranked based on their expected scores.

20100211568, Aug 19, 2010

Feb 19, 2009

12/388941

Wei Chu - Santa Clara CA, US
Seung-Taek Park - San Jose CA, US

G06F 17/30

707732, 707E17071

This disclosure describes systems and methods for selecting and/or ranking web-based content predicted to have the greatest interest to individual users. In particular, articles are ranked in terms of predicted interest for different users. This is done by optimizing an interest model and in particular through a method of bilinear regression and Bayesian optimization. The interest model is populated with data regarding users, the articles, and historical interest trends that types of users have expressed towards types of articles.

20100241597, Sep 23, 2010

Mar 19, 2009

12/407785

Bee-Chung Chen - Mountain View CA, US
Pradheep Elango - Mountain View CA, US
Deepak K. Agarwal - Sunnyvale CA, US
Wei Chu - Sunnyvale CA, US

G06F 15/18
G06F 15/16

706 12, 709204

Techniques are presented for estimating the current popularity of web content. Click and view data for articles are used to estimate popularity of the articles by analyzing click-through rates. Click-though rates are estimated such that a current click-through rate reflects fluctuations in popularity of articles through time.