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KDD
2009
ACM
152views Data Mining» more  KDD 2009»
16 years 7 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
230views Data Mining» more  KDD 2009»
16 years 7 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
159views Data Mining» more  KDD 2009»
16 years 7 months ago
Adapting the right measures for K-means clustering
Clustering validation is a long standing challenge in the clustering literature. While many validation measures have been developed for evaluating the performance of clustering al...
Junjie Wu, Hui Xiong, Jian Chen
KDD
2009
ACM
164views Data Mining» more  KDD 2009»
16 years 7 months ago
Social influence analysis in large-scale networks
In large social networks, nodes (users, entities) are influenced by others for various reasons. For example, the colleagues have strong influence on one's work, while the fri...
Jie Tang, Jimeng Sun, Chi Wang, Zi Yang
KDD
2009
ACM
206views Data Mining» more  KDD 2009»
16 years 7 months ago
Ranking-based clustering of heterogeneous information networks with star network schema
A heterogeneous information network is an information network composed of multiple types of objects. Clustering on such a network may lead to better understanding of both hidden s...
Yizhou Sun, Yintao Yu, Jiawei Han
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