67 citations · 92 across the 7 of their papers we have counts for
7 papers
Factorizing LambdaMART for cold start recommendations
Phong Nguyen, Jun Wang, Alexandros Kalousis
Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This o…
Two-Stage Metric Learning
Jun Wang, Ke Sun, Fei Sha +2
In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of f…
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining
Phong Nguyen, Jun Wang, Melanie Hilario +1
The notion of meta-mining has appeared recently and extends the traditional meta-learning in two ways. First it does not learn meta-models that provide support only for the learnin…
Parametric Local Metric Learning for Nearest Neighbor Classification
Jun Wang, Adam Woznica, Alexandros Kalousis
We study the problem of learning local metrics for nearest neighbor classification. Most previous works on local metric learning learn a number of local unrelated metrics. While th…
Structuring Relevant Feature Sets with Multiple Model Learning
Jun Wang, Alexandros Kalousis
Feature selection is one of the most prominent learning tasks, especially in high-dimensional datasets in which the goal is to understand the mechanisms that underly the learning d…
Learning Neighborhoods for Metric Learning
Jun Wang, Adam Woznica, Alexandros Kalousis
Metric learning methods have been shown to perform well on different learning tasks. Many of them rely on target neighborhood relationships that are computed in the original featur…