Nonconvex regularizations for feature selection in ranking with sparse SVM
Feature selection in learning to rank has recently emerged as a crucial issue. Whereas
several preprocessing approaches have been proposed, only a few have focused on …
several preprocessing approaches have been proposed, only a few have focused on …
Deep neural network regularization for feature selection in learning-to-rank
A Rahangdale, S Raut - IEEE Access, 2019 - ieeexplore.ieee.org
Learning-to-rank is an emerging area of research for a wide range of applications. Many
algorithms are devised to tackle the problem of learning-to-rank. However, very few existing …
algorithms are devised to tackle the problem of learning-to-rank. However, very few existing …
From tf-idf to learning-to-rank: An overview
Ranking a set of documents based on their relevances with respect to a given query is a
central problem of information retrieval (IR). Traditionally people have been using …
central problem of information retrieval (IR). Traditionally people have been using …
A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis
JY Yeh, CJ Tsai - Computer Science and Information Systems, 2022 - doiserbia.nb.rs
This paper addresses the feature selection problem in learning to rank (LTR). We propose a
graph-based feature selection method, named FS-SCPR, which comprises four steps:(i) use …
graph-based feature selection method, named FS-SCPR, which comprises four steps:(i) use …
A feature selection method based on minimum redundancy maximum relevance for learning to rank
MB Shirzad, MR Keyvanpour - 2015 AI & Robotics (IRANOPEN …, 2015 - ieeexplore.ieee.org
Learning to rank has considered as a promising approach for ranking in information
retrieval. In recent years feature selection for learning to rank introduced as a crucial issue …
retrieval. In recent years feature selection for learning to rank introduced as a crucial issue …
Mofsrank: a multiobjective evolutionary algorithm for feature selection in learning to rank
F Cheng, W Guo, X Zhang - Complexity, 2018 - Wiley Online Library
Learning to rank has attracted increasing interest in the past decade, due to its wide
applications in the areas like document retrieval and collaborative filtering. Feature selection …
applications in the areas like document retrieval and collaborative filtering. Feature selection …
A Bayesian approach to sparse learning-to-rank for search engine optimization
O Krasotkina, V Mottl - Machine Learning and Data Mining in Pattern …, 2015 - Springer
Search engine optimization (SEO) is the process of affecting the visibility of a web page in
the engine's search results. SEO specialists must understand how search engines work and …
the engine's search results. SEO specialists must understand how search engines work and …
K-complex detection using a hybrid-synergic machine learning method
Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-
complex is an indicator for the sleep stage 2. However, due to the ambiguity of the …
complex is an indicator for the sleep stage 2. However, due to the ambiguity of the …
Dynamic feature generation and selection on heterogeneous graph for music recommendation
In the past decade, online music streaming services (MSS), eg, Pandora and Spotify,
revolutionized the way people access, consume and share music. MSS serve users with a …
revolutionized the way people access, consume and share music. MSS serve users with a …
[PDF][PDF] Greedy rankrls: a linear time algorithm for learning sparse ranking models
T Pahikkala, A Airola, P Naula… - Sigir 2010 workshop on …, 2010 - alex.smola.org
Ranking is a central problem in information retrieval. Much work has been done in the recent
years to automate the development of ranking models by means of supervised machine …
years to automate the development of ranking models by means of supervised machine …