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» Relational peculiarity-oriented mining
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KDD
2009
ACM
152views Data Mining» more  KDD 2009»
14 years 9 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
2009
ACM
305views Data Mining» more  KDD 2009»
14 years 9 months ago
Grocery shopping recommendations based on basket-sensitive random walk
We describe a recommender system in the domain of grocery shopping. While recommender systems have been widely studied, this is mostly in relation to leisure products (e.g. movies...
Ming Li, M. Benjamin Dias, Ian H. Jarman, Wael El-...
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
14 years 9 months ago
Cross domain distribution adaptation via kernel mapping
When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the...
ErHeng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiang...
KDD
2009
ACM
227views Data Mining» more  KDD 2009»
14 years 9 months ago
User grouping behavior in online forums
Online forums represent one type of social media that is particularly rich for studying human behavior in information seeking and diffusing. The way users join communities is a re...
Xiaolin Shi, Jun Zhu, Rui Cai, Lei Zhang
KDD
2008
ACM
161views Data Mining» more  KDD 2008»
14 years 9 months ago
Locality sensitive hash functions based on concomitant rank order statistics
: Locality Sensitive Hash functions are invaluable tools for approximate near neighbor problems in high dimensional spaces. In this work, we are focused on LSH schemes where the si...
Kave Eshghi, Shyamsundar Rajaram