Results 331 to 340 of about 1,080,302 (365)

LTRR: Learning To Rank Retrievers for LLMs

arXiv.org
Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs optimally across all query types.
To Eun Kim, Fernando Diaz
semanticscholar   +1 more source

Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application

Knowledge Discovery and Data Mining, 2018
In E-commerce platforms such as Amazon and TaoBao , ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widely applied to ranking problems.
Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, Yinghui Xu   +4 more
semanticscholar   +1 more source

Applications of Learning to Rank

2011
In this chapter, we introduce some applications of learning to rank. The major purpose is to demonstrate how to use an existing learning-to-rank algorithm to solve a real ranking problem. In particular, we will take question answering, multimedia retrieval, text summarization, online advertising, etc. as examples, for illustration.
openaire   +3 more sources

Deep learning-based learning to rank with ties for image re-ranking

2016 IEEE International Conference on Digital Signal Processing (DSP), 2016
In existing learning to rank problems, the learned ranking function sorts objects according to their predicted scores. Therefore, a full-ordering object list is obtained even if two or more objects have almost identical degrees of relevance (or called objects with ties).
Ou Wu, Weiming Hu, Piplong Zhao, Jinfeng Yang, Liyuan Guo   +4 more
openaire   +2 more sources

Manifold Learning and Ranking

2010
A prominent theme of this book is the spatial analysis of networks and data independent of an embedding in an ambient space. The topology and metric of the network/complex have been sufficient to define the domain upon which we may perform data analysis.
Leo Grady, Jonathan R. Polimeni
openaire   +2 more sources

Learning to rank for information retrieval

Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2007
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 degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to ...
openaire   +2 more sources

Learning to rank: from pairwise approach to listwise approach

International Conference on Machine Learning, 2007
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li   +4 more
semanticscholar   +1 more source

Learning to rank using gradient descent

International Conference on Machine Learning, 2005
C. Burges, T. Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Gregory N. Hullender   +6 more
semanticscholar   +1 more source

Learning to Rank by Optimizing Expected Reciprocal Rank

2011
Learning to rank is one of the most hot research areas in information retrieval, among which listwise approach is an important research direction and the methods that directly optimizing evaluation metrics in listwise approach have been used for optimizing some important ranking evaluation metrics, such as MAP, NDCG and etc.
Hongfei Lin, Ping Zhang, Jiajin Wu, Yuan Lin   +3 more
openaire   +2 more sources

Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015
A. Severyn, Alessandro Moschitti
semanticscholar   +1 more source