scholar.google.com › citations
LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items.
Feb 22, 2021 · We propose an architecture-agnostic approach based on a neural LETOR model to reduce the size of its input by up to 60% without affecting the system ...
Mar 30, 2021 · In this paper, we propose an approach for feature selection for Learning TO Rank (LETOR) based on a neural ranker.
People also ask
What is the best algorithm for learning-to-rank?
Can neural networks be used for feature selection?
What is feature transformation for neural ranking models?
Is feature selection necessary for deep learning?
LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items.
LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items.
This work proposes an architecture-agnostic approach based on a neural LETOR model to reduce the size of its input by up to 60% without affecting the system ...
Sep 16, 2017 · There are multiple possibilities. The obvious one is to sum weights of all connections from input layer to the first hidden layer per input neuron.
Feb 22, 2021 · LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set ...
Jan 4, 2019 · I have a dataset that contains around 30 features and I want to find out which features contribute the most to the outcome. I have 5 algorithms.
May 13, 2024 · In this work, we explore feature selection for neural learning-to-rank (LTR). In particular, we investigate six widely-used methods from the field of ...