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96
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KDD
2009
ACM
152views Data Mining» more  KDD 2009»
16 years 1 months ago
TANGENT: a novel, 'Surprise me', recommendation algorithm
Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me" query: A user may be bored ...
Kensuke Onuma, Hanghang Tong, Christos Faloutsos
KDD
2005
ACM
194views Data Mining» more  KDD 2005»
16 years 1 months ago
Web object indexing using domain knowledge
Web object is defined to represent any meaningful object embedded in web pages (e.g. images, music) or pointed to by hyperlinks (e.g. downloadable files). Users usually search for...
Muyuan Wang, Zhiwei Li, Lie Lu, Wei-Ying Ma, Naiya...
112
Voted
KDD
2004
ACM
210views Data Mining» more  KDD 2004»
16 years 1 months ago
Visually mining and monitoring massive time series
Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/no-go decision. The cost of a false positive, allowing a launch in spi...
Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jef...
CHI
2003
ACM
16 years 29 days ago
Leaders leading? a shift in technology adoption
In the past, most early hands-on users of interactive software in organizations were individual contributors. Managers as early adopters is a new trend with significant implicatio...
Jonathan Grudin
92
Voted
STOC
2003
ACM
152views Algorithms» more  STOC 2003»
16 years 27 days ago
Cell-probe lower bounds for the partial match problem
Given a database of n points in {0, 1}d, the partial match problem is: In response to a query x in {0, 1, }d, find a database point y such that for every i whenever xi = , we have...
T. S. Jayram, Subhash Khot, Ravi Kumar, Yuval Raba...
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