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Hongyuan Yuan Zha

from Atlanta, GA
Age ~61
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Also known as:
Hongyuan Y Zha, Hongyuan H Zha, Hong Yan Zha, Hong Y Zha, Hongyan Zha, Zha Hongyuan
Phone and address:
1988 Carlotta Ct, Atlanta, GA 30345
Related to:
Za HongyuanMay Zha, 64
Connected to:
Jianyu Zha, 35Li Zha, 34Alan Zhai, 40Albert Y Zhai, 43Dong Zhai, 44Haibo Zhai, 48Huachun Zhai, 53Kelvin Zhai, 71Qi P Zhai, 60Qiwei Zhai, 59

Hongyuan Zha Phones & Addresses

  • 1988 Carlotta Ct, Atlanta, GA 30345
  • Norcross, GA
  • 673 Stoneledge Rd, State College, PA 16803 (814) 238-5899
  • 425 Waupelani Dr, State College, PA 16801
  • University Park, PA
  • San Mateo, CA

Resumes

Resumes

Hongyuan Zha Photo 1

Professor

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Location:
1988 Carlotta Ct, Atlanta, GA 30345
Industry:
Higher Education
Work:
Yahoo
Consultant
Georgia Institute of Technology
Professor
Penn State University Aug 1992 - Jul 2006
Professor
Proofpoint Aug 1992 - Jul 2006
Scientific Advisor
Inktomi Jan 1999 - Jun 2001
Engineer and Scientist
Education:
Stanford University 1990 - 1993
Fudan University 1980 - 1984
Bachelors, Bachelor of Science, Mathematics
Skills:
Machine Learning
Information Retrieval
Algorithms
Data Mining
Hongyuan Zha Photo 2

Professor

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Location:
Atlanta, GA
Industry:
Internet
Work:
Penn State University
Professor
Education:
Stanford University Jan 1990 - Aug 1992
Doctorates, Doctor of Philosophy

Publications

Us Patents

Associating Documents With Classifications And Ranking Documents Based On Classification Weights

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US Patent:
7028027, Apr 11, 2006
Filed:
Sep 30, 2002
Appl. No.:
10/262519
Inventors:
Hongyuan Zha - State College PA, US
Sean Suchter - San Mateo CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 17/00
US Classification:
707 3, 707102, 7071041, 707101
Abstract:
A method and apparatus for associating documents with classification values and ranking documents based on classification weights is provided. It is determined if a document is associated a classification. If the document is associated with a classification, then it is determined if a classification value, which is associated with the document, is associated with a weight. If the classification value is associated with a weight, then a rank of the document is adjusted based on the weight that is associated with the classification value.

Using Network Traffic Logs For Search Enhancement

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US Patent:
7398271, Jul 8, 2008
Filed:
Apr 16, 2002
Appl. No.:
10/124509
Inventors:
Arkady Borkovsky - San Francisco CA, US
Douglas M. Cook - San Francisco CA, US
Jean-Marc Langlois - Alameda CA, US
Tomi Poutanen - Toronto, CA
Hongyuan Zha - State College PA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 17/30
US Classification:
707 7, 707 3, 707 10, 709203, 709219
Abstract:
A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Associating Documents With Classifications And Ranking Documents Based On Classification Weights

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US Patent:
7533119, May 12, 2009
Filed:
Jan 18, 2006
Appl. No.:
11/335076
Inventors:
Hongyuan Zha - State College PA, US
Sean Suchter - San Mateo CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 17/00
US Classification:
707102, 707100, 707101
Abstract:
A method and apparatus for associating documents with classification values and ranking documents based on classification weights is provided. It is determined if a document is associated a classification. If the document is associated with a classification, then it is determined if a classification value, which is associated with the document, is associated with a weight. If the classification value is associated with a weight, then a rank of the document is adjusted based on the weight that is associated with the classification value.

Method And Appartus For Using Measures To Learn Balanced Relevance Functions From Expert And User Judgments

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US Patent:
7685078, Mar 23, 2010
Filed:
May 30, 2007
Appl. No.:
11/755134
Inventors:
Keke Chen - Sunnyvale CA, US
Ya Zhang - Sunnyvale CA, US
Zhaohui Zheng - Sunnyvale CA, US
Hongyuan Zha - Norcross GA, US
Gordon Sun - Redwood Shores CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06N 5/00
US Classification:
706 12, 706 45
Abstract:
The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion.

Learning Ranking Functions Incorporating Isotonic Regression For Information Retrieval And Ranking

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US Patent:
7849076, Dec 7, 2010
Filed:
Mar 31, 2008
Appl. No.:
12/060195
Inventors:
Zhaohui Zheng - Sunnyvale CA, US
Hongyuan Zha - Norcross GA, US
Gordon Sun - Redwood Shores CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 17/30
G06F 7/00
US Classification:
707715, 707735, 707748
Abstract:
Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs and determining one or more contradicting pairs in a given training sets. An optimization function to minimize the number of contradicting pairs in the training set is formulated, and modified by incorporating a grade difference between one or more content items corresponding to the query in the training set and applied to each query in the training set. A ranking function is determined based on the application of regression trees on the queries of the training set minimized by the optimization function and stored for application to content item-query pairs not contained in the one or more training sets.

Learning Ranking Functions Incorporating Boosted Ranking In A Regression Framework For Information Retrieval And Ranking

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US Patent:
8051072, Nov 1, 2011
Filed:
Mar 31, 2008
Appl. No.:
12/060179
Inventors:
Zhaohui Zheng - Sunnyvale CA, US
Hongyuan Zha - Norcross GA, US
Gordon Sun - Redwood Shores CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 17/30
US Classification:
707722, 707723
Abstract:
Embodiments of the present invention provide for methods, systems and computer program products for learning ranking functions to determine the ranking of one or more content items that are responsive to a query. The present invention includes generating one or more training sets comprising one or more content item-query pairs, determining preference data for the one or more query-content item pairs of the one or more training sets and determining labeled data for the one or more query-content item pairs of the one or more training sets. A ranking function is determined based upon the preference data and the labeled data for the one or more content-item query pairs of the one or more training sets. The ranking function is then stored for application to query-content item pairs not contained in the one or more training sets.

Using Network Traffic Logs For Search Enhancement

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US Patent:
8203952, Jun 19, 2012
Filed:
Jul 7, 2008
Appl. No.:
12/168797
Inventors:
Arkady Borkovsky - San Francisco CA, US
Douglas M. Cook - San Francisco CA, US
Jean-Marc Langlois - Alameda CA, US
Tomi Poutanen - Toronto, CA
Hongyuan Zha - State College PA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G01R 31/08
US Classification:
3702301
Abstract:
A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Learning Retrieval Functions Incorporating Query Differentiation For Information Retrieval

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US Patent:
8250061, Aug 21, 2012
Filed:
Jan 30, 2006
Appl. No.:
11/343910
Inventors:
Gordon Sun - Redwood Shores CA, US
Zhaohui Zheng - Mountain View CA, US
Hongyuan Zha - State College PA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 7/00
G06F 17/30
US Classification:
707713, 707728, 707748, 707999003
Abstract:
The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporate query differentiation. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating a training set comprising one or more content item-query pairs. Content item-query pairs in the training set are collectively used to determine the relevance function by minimizing a loss function according to a relevance score adjustment function that accounts for query differentiation. The monotocity of relevance score adjustment function allows the trained relevance function to be directly applied to new queries.

Isbn (Books And Publications)

Spectral Clustering, Ordering and Ranking: Statistical Learning With Matrix Factorizations

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Author

Hongyuan Zha

ISBN #

0387304487

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Hongyuan Yuan Zha from Atlanta, GA, age ~61 Get Report