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 Ben Tan

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Bibliometrics: publication history
Average citations per article3.00
Citation Count18
Publication count6
Publication years2013-2017
Available for download4
Average downloads per article483.50
Downloads (cumulative)1,934
Downloads (12 Months)546
Downloads (6 Weeks)66
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6 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
June 2017 ACM Transactions on Intelligent Systems and Technology (TIST): Volume 8 Issue 5, September 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 19,   Downloads (12 Months): 153,   Downloads (Overall): 153

Full text available: PDFPDF
Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, that is, data as the main input, algorithms for preference learning, and system for user interaction ...
Keywords: preference learning, Transfer to rank, behavior ranking, collaborative recommendation

2 published by ACM
August 2015 KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 38,   Downloads (12 Months): 281,   Downloads (Overall): 999

Full text available: PDFPDF
Transfer learning, which leverages knowledge from source domains to enhance learning ability in a target domain, has been proven effective in various applications. One major limitation of transfer learning is that the source and target domains should be directly related. If there is little overlap between the two domains, performing ...
Keywords: nonnegative matrix tri-factorizations, transitive transfer learning, transfer learning

3 published by ACM
August 2014 KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher: ACM
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 8,   Downloads (12 Months): 97,   Downloads (Overall): 702

Full text available: PDFPDF
Time-sync video tagging aims to automatically generate tags for each video shot. It can improve the user's experience in previewing a video's timeline structure compared to traditional schemes that tag an entire video clip. In this paper, we propose a new application which extracts time-sync video tags by automatically exploiting ...
Keywords: topic modeling, video tagging, crowdsourcing, temporal and personalized model

4
August 2014 Statistical Analysis and Data Mining: Volume 7 Issue 4, August 2014
Publisher: John Wiley & Sons, Inc.
Bibliometrics:
Citation Count: 1

Transfer learning, which aims to help learning tasks in a target domain by leveraging knowledge from auxiliary domains, has been demonstrated to be effective in different applications such as text mining, sentiment analysis, and so on. In addition, in many real-world applications, auxiliary data are described from multiple perspectives and ...
Keywords: multiple data sources, multi-view learning, transfer learning

5
December 2013 Pervasive and Mobile Computing: Volume 9 Issue 6, December, 2013
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 4

Demographics prediction is an important component of user profile modeling. The accurate prediction of users' demographics can help promote many applications, ranging from web search, personalization to behavior targeting. In this paper, we focus on how to predict users' demographics, including ''gender'', ''job type'', ''marital status'', ''age'' and ''number of ...
Keywords: Demographics, Multi-task learning, Mobile, Cost-sensitive classification, Ensemble, Feature construction

6 published by ACM
August 2013 KDD Cup '13: Proceedings of the 2013 KDD Cup 2013 Workshop
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 6,   Downloads (12 Months): 23,   Downloads (Overall): 78

Full text available: PDFPDF
We present the ideas and methodologies that we used to address the KDD Cup 2013 challenge on author-paper identification. We firstly formulate the problem as a personalized ranking task and then propose to solve the task through a supervised learning framework. The key point is to eliminate those incorrectly assigned ...



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