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WWW
2004
ACM
14 years 7 months ago
Shilling recommender systems for fun and profit
Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have bec...
Shyong K. Lam, John Riedl
KDD
2009
ACM
305views Data Mining» more  KDD 2009»
14 years 7 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
198views Data Mining» more  KDD 2009»
14 years 7 months ago
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a coll...
Srivatsava Daruru, Nena M. Marin, Matt Walker, Joy...
KDD
2008
ACM
172views Data Mining» more  KDD 2008»
14 years 7 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
SIGMOD
2002
ACM
132views Database» more  SIGMOD 2002»
14 years 7 months ago
Clustering by pattern similarity in large data sets
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of th...
Haixun Wang, Wei Wang 0010, Jiong Yang, Philip S. ...