Author image not provided
 Fan Guo

Add personal information
  Affiliation history
Bibliometrics: publication history
Average citations per article31.56
Citation Count284
Publication count9
Publication years2007-2011
Available for download8
Average downloads per article563.50
Downloads (cumulative)4,508
Downloads (12 Months)194
Downloads (6 Weeks)27
Arrow RightAuthor only

See all colleagues of this author

See all subject areas


9 results found Export Results: bibtexendnoteacmrefcsv

Result 1 – 9 of 9
Sort by:

January 2011
Citation Count: 0

The emerging popularity of multimedia data, as digital representation of text, image, video and countless other milieus, with prodigious volumes and wild diversity, exhibits the phenomenal impact of modern technologies in reforming the way information is accessed, disseminated, digested and retained. This has iteratively ignited the data-driven perspective of research ...

2 published by ACM
October 2010 ACM Transactions on Knowledge Discovery from Data (TKDD): Volume 4 Issue 4, October 2010
Publisher: ACM
Citation Count: 8
Downloads (6 Weeks): 1,   Downloads (12 Months): 21,   Downloads (Overall): 577

Full text available: PDFPDF
A fundamental challenge in utilizing Web search click data is to infer user-perceived relevance from the search log. Not only is the inference a difficult problem involving statistical reasonings but the bulky size, together with the ever-increasing nature, of the log data imposes extra requirements on scalability. In this paper, ...
Keywords: Bayesian models, Web search, click log analysis

3 published by ACM
June 2009 KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher: ACM
Citation Count: 24
Downloads (6 Weeks): 0,   Downloads (12 Months): 10,   Downloads (Overall): 656

Full text available: PDFPDF
Given a quarter of petabyte click log data, how can we estimate the relevance of each URL for a given query? In this paper, we propose the Bayesian Browsing Model (BBM), a new modeling technique with following advantages: (a) it does exact inference; (b) it is single-pass and parallelizable; (c) ...
Keywords: click log analysis, bayesian models, web search

4 published by ACM
April 2009 WWW '09: Proceedings of the 18th international conference on World wide web
Publisher: ACM
Citation Count: 95
Downloads (6 Weeks): 7,   Downloads (12 Months): 34,   Downloads (Overall): 951

Full text available: PDFPDF
Given a terabyte click log, can we build an efficient and effective click model? It is commonly believed that web search click logs are a gold mine for search business, because they reflect users' preference over web documents presented by the search engine. Click models provide a principled approach to ...
Keywords: click log analysis, bayesian models, web search

5 published by ACM
February 2009 WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining
Publisher: ACM
Citation Count: 107
Downloads (6 Weeks): 8,   Downloads (12 Months): 48,   Downloads (Overall): 830

Full text available: PDFPDF
Many tasks that leverage web search users' implicit feedback rely on a proper and unbiased interpretation of user clicks. Previous eye-tracking experiments and studies on explaining position-bias of user clicks provide a spectrum of hypotheses and models on how an average user examines and possibly clicks web documents returned by ...
Keywords: web search, click log analysis, statistical models

6 published by ACM
February 2009 WSCD '09: Proceedings of the 2009 workshop on Web Search Click Data
Publisher: ACM
Citation Count: 14
Downloads (6 Weeks): 3,   Downloads (12 Months): 8,   Downloads (Overall): 434

Full text available: PDFPDF
Click models provide a principled way of understanding user interaction with web search results in a query session and a statistical tool for leveraging search engine click logs to analyze and improve user experience. An important component in all existing click models is the user behavior assumption -- how users ...
Keywords: click model, user behavior, web search

7 published by ACM
August 2008 KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher: ACM
Citation Count: 2
Downloads (6 Weeks): 0,   Downloads (12 Months): 6,   Downloads (Overall): 431

Full text available: PDFPDF
Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up data mining algorithms. Specifically, we present a parallel algorithm for approximate learning of Linear Dynamical Systems ...
Keywords: Expectation Maximization (EM), Kalman Filters, OpenMP, linear dynamical systems, multi-core, optimization

August 2008 Proceedings of the VLDB Endowment: Volume 1 Issue 2, August 2008
Publisher: VLDB Endowment
Citation Count: 1
Downloads (6 Weeks): 1,   Downloads (12 Months): 2,   Downloads (Overall): 79

Full text available: PDFPDF
The amount of biological data publicly available has experienced an exponential growth as the technology advances. Online databases are now playing an important role as information repositories as well as easily accessible platforms for researchers to communicate and contribute. Recent research projects in image bioinformatics produce a number of databases ...

9 published by ACM
June 2007 ICML '07: Proceedings of the 24th international conference on Machine learning
Publisher: ACM
Citation Count: 20
Downloads (6 Weeks): 3,   Downloads (12 Months): 20,   Downloads (Overall): 403

Full text available: PDFPDF
A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant networks, much less has been ...

The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us