# Wei Chu

## Wei Chu, Sunnyvale, CA US

Patent application number | Description | Published |
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20100121624 | ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES - 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. | 05-13-2010 |

20100121801 | ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES - 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. | 05-13-2010 |

20100125585 | Conjoint Analysis with Bilinear Regression Models for Segmented Predictive Content Ranking - 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. | 05-20-2010 |

20100241597 | DYNAMIC ESTIMATION OF THE POPULARITY OF WEB CONTENT - 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. | 09-23-2010 |

20100250556 | Determining User Preference of Items Based on User Ratings and User Features - 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. | 09-30-2010 |

20110087673 | METHODS AND SYSTEMS RELATING TO RANKING FUNCTIONS FOR MULTIPLE DOMAINS - Methods and systems are disclosed that relate to ranking functions for multiple different domains. By way of example but not limitation, ranking functions for multiple different domains may be trained based on inter-domain loss, and such ranking functions may be used to rank search results from multiple different domains so that they may be blended without normalizing relevancy scores. | 04-14-2011 |

20110107260 | PREDICTING ITEM-ITEM AFFINITIES BASED ON ITEM FEATURES BY REGRESSION - 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. | 05-05-2011 |

20120303349 | ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES - 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. | 11-29-2012 |

20130054593 | DETERMINING USER PREFERENCE OF ITEMS BASED ON USER RATINGS AND USER FEATURES - 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. Based on user features of a user and items a user has consumed, a set of nearest neighbor items are identified as a set of candidate items, and affinity scores of candidate items are determined. Based on the affinity scores, a candidate item from the set of candidate items is recommended to the user. | 02-28-2013 |

## Wei Chu, Santa Clara, CA US

Patent application number | Description | Published |
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20100211568 | PERSONALIZED RECOMMENDATIONS ON DYNAMIC CONTENT - 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. | 08-19-2010 |

20110112981 | Feature-Based Method and System for Cold-Start Recommendation of Online Ads - A method and a system are provided for recommending an ad (e.g., item) for a user. In one example, the system constructs one or more user profiles. Each user profile is represented by a user feature set including user attributes. The system constructs one or more item profiles. Each item profile is represented by an item feature set including item attributes. The system receives historical item ratings given by one or more users. The system then generates one or more preference scores by modeling at least one relationship among the user profiles, the item profiles and the historical item ratings. | 05-12-2011 |

## Wei Chu, San Jose, CA US

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20120016642 | CONTEXTUAL-BANDIT APPROACH TO PERSONALIZED NEWS ARTICLE RECOMMENDATION - Methods and apparatus for performing computer-implemented personalized recommendations are disclosed. User information pertaining to a plurality of features of a plurality of users may be obtained. In addition, item information pertaining to a plurality of features of the plurality of items may be obtained. A plurality of sets of coefficients of a linear model may be obtained based at least in part on the user information and/or the item information such that each of the plurality of sets of coefficients corresponds to a different one of a plurality of items, where each of the plurality of sets of coefficients includes a plurality of coefficients, each of the plurality of coefficients corresponding to one of the plurality of features. In addition, at least one of the plurality of coefficients may be shared among the plurality of sets of coefficients for the plurality of items. Each of a plurality of scores for a user may be calculated using the linear model based at least in part upon a corresponding one of the plurality of sets of coefficients associated with a corresponding one of the plurality of items, where each of the plurality of scores indicates a level of interest in a corresponding one of a plurality of items. A plurality of confidence intervals may be ascertained, each of the plurality of confidence intervals indicating a range representing a level of confidence in a corresponding one of the plurality of scores associated with a corresponding one of the plurality of items. One of the plurality of items for which a sum of a corresponding one of the plurality of scores and a corresponding one of the plurality of confidence intervals is highest may be recommended. | 01-19-2012 |

20150051973 | CONTEXTUAL-BANDIT APPROACH TO PERSONALIZED NEWS ARTICLE RECOMMENDATION - Methods and apparatus for performing computer-implemented personalized recommendations are disclosed. User information pertaining to a plurality of features of a plurality of users may be obtained. In addition, item information pertaining to a plurality of features of the plurality of items may be obtained. A plurality of sets of coefficients of a linear model may be obtained based at least in part on the user information and/or the item information such that each of the plurality of sets of coefficients corresponds to a different one of a plurality of items, where each of the plurality of sets of coefficients includes a plurality of coefficients, each of the plurality of coefficients corresponding to one of the plurality of features. In addition, at least one of the plurality of coefficients may be shared among the plurality of sets of coefficients for the plurality of items. Each of a plurality of scores for a user may be calculated using the linear model based at least in part upon a corresponding one of the plurality of sets of coefficients associated with a corresponding one of the plurality of items, where each of the plurality of scores indicates a level of interest in a corresponding one of a plurality of items. A plurality of confidence intervals may be ascertained, each of the plurality of confidence intervals indicating a range representing a level of confidence in a corresponding one of the plurality of scores associated with a corresponding one of the plurality of items. One of the plurality of items for which a sum of a corresponding one of the plurality of scores and a corresponding one of the plurality of confidence intervals is highest may be recommended. | 02-19-2015 |

## Wei Chu, Redmond, WA US

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20130111005 | Online Active Learning in User-Generated Content Streams | 05-02-2013 |

## Wei Chu, Xi'An CN

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20140178593 | METHOD AND APPARATUS FOR NANOCRYSTALLIZING A METAL SURFACE BY SHOCK WAVE-ACCELERATED NANOPARTICLES - A method and apparatus for nanocrystallizing a metal surface by laser-induced shock wave-accelerated nanoparticles. The apparatus comprises a control system, a light guiding system, a workbench control system and an auxiliary system, wherein the auxiliary system comprises an air compressor, a paint feeder device, a nanoparticle nozzle, a powder feeder device, an exhaust, a sealed working chamber and a metal nanoparticle recycler device. The method comprises the following steps: pre-processing and fixing a workpiece; activating the air compressor to feed a powder; controlling and adjusting the paint feeder device to eject a black paint; transmitting a high-power pulse laser beam; recycling excess metal nanoparticles; and rinsing non-vaporized/ionized black paint off a surface of the workpiece. | 06-26-2014 |

20140205764 | METHOD AND APPARATUS FOR ACQUIRING NANOSTRUCTURED COATING BY EFFECT OF LASER-INDUCED CONTINUOUS EXPLOSION SHOCK WAVE - A method and apparatus for acquiring a nanostructured coating on a metal surface by using an intense shock wave generated by continuous explosion of a laser-induced plasma is provided. The method comprises: irradiating a laser beam on a black paint surface of an upper opening of a high pressure resistant glass pipe having a black paint strip arranged therein; the black paint absorbing the light energy and producing a plasma; generating an initial plasma explosion shock wave; transmitting the initial plasma explosion shock wave in the high pressure resistant glass pipe; generating a plasma cloud reaching a lower opening of a glass catheter; and, the shock wave pressure outputted embedding nanoparticles into a surface of a workpiece. The apparatus comprises the high pressure-resistant glass pipe with a zigzagging switchback shape or a spiral and inverted cone shape. | 07-24-2014 |