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121
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KDD
2009
ACM
152views Data Mining» more  KDD 2009»
16 years 4 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
137
Voted
KDD
2008
ACM
156views Data Mining» more  KDD 2008»
16 years 4 months ago
Can complex network metrics predict the behavior of NBA teams?
The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses th...
Antonio Alfredo Ferreira Loureiro, Pedro O. S. Vaz...
KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 4 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
151
Voted
KDD
2007
ACM
244views Data Mining» more  KDD 2007»
16 years 4 months ago
A Recommender System Based on Local Random Walks and Spectral Methods
In this paper, we design recommender systems for weblogs based on the link structure among them. We propose algorithms based on refined random walks and spectral methods. First, w...
Zeinab Abbassi, Vahab S. Mirrokni
KDD
2006
ACM
159views Data Mining» more  KDD 2006»
16 years 4 months ago
Global distance-based segmentation of trajectories
This work introduces distance-based criteria for segmentation of object trajectories. Segmentation leads to simplification of the original objects into smaller, less complex primi...
Aris Anagnostopoulos, Michail Vlachos, Marios Hadj...