Learning to rank for information retrieval
TY Liu - Foundations and Trends® in Information Retrieval, 2009 - nowpublishers.com
Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking
model using training data, such that the model can sort new objects according to their …
model using training data, such that the model can sort new objects according to their …
A review on instance ranking problems in statistical learning
T Werner - Machine Learning, 2022 - Springer
Ranking problems, also known as preference learning problems, define a widely spread
class of statistical learning problems with many applications, including fraud detection …
class of statistical learning problems with many applications, including fraud detection …
Pairwise preference regression for cold-start recommendation
ST Park, W Chu - Proceedings of the third ACM conference on …, 2009 - dl.acm.org
Recommender systems are widely used in online e-commerce applications to improve user
engagement and then to increase revenue. A key challenge for recommender systems is …
engagement and then to increase revenue. A key challenge for recommender systems is …
Learning to predict chemical reactions
Being able to predict the course of arbitrary chemical reactions is essential to the theory and
applications of organic chemistry. Approaches to the reaction prediction problems can be …
applications of organic chemistry. Approaches to the reaction prediction problems can be …
An experimental comparison of cross-validation techniques for estimating the area under the ROC curve
Reliable estimation of the classification performance of inferred predictive models is difficult
when working with small data sets. Cross-validation is in this case a typical strategy for …
when working with small data sets. Cross-validation is in this case a typical strategy for …
Robotic assistance in the coordination of patient care
We conducted a study to investigate trust in and dependence upon robotic decision support
among nurses and doctors on a labor and delivery floor. There is evidence that suggestions …
among nurses and doctors on a labor and delivery floor. There is evidence that suggestions …
A comparison of AUC estimators in small-sample studies
Reliable estimation of the classification performance of learned predictive models is difficult,
when working in the small sample setting. When dealing with biological data it is often the …
when working in the small sample setting. When dealing with biological data it is often the …
Explicit feedback meet with implicit feedback in GPMF: a generalized probabilistic matrix factorization model for recommendation
Recommender Systems focus on implicit and explicit feedback or parameters of users for
better rating prediction. Most of the existing recommender systems use only one type of …
better rating prediction. Most of the existing recommender systems use only one type of …
Apprenticeship scheduling: Learning to schedule from human experts
Coordinating agents to complete a set of tasks with intercoupled temporal and resource
constraints is computationally challenging, yet human domain experts can solve these …
constraints is computationally challenging, yet human domain experts can solve these …
[PDF][PDF] Ranking forests
S Clémençon, M Depecker, N Vayatis - The Journal of Machine Learning …, 2013 - jmlr.org
The present paper examines how the aggregation and feature randomization principles
underlying the algorithm RANDOM FOREST (Breiman, 2001) can be adapted to bipartite …
underlying the algorithm RANDOM FOREST (Breiman, 2001) can be adapted to bipartite …