Results 181 to 190 of about 250,954 (220)

SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019
In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents.
Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, Jirong Wen   +5 more
semanticscholar   +1 more source

Low‐rank Orthogonal Decompositions for Information Retrieval Applications

Numerical Linear Algebra with Applications, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Berry, M. W., Fierro, R. D.
openaire   +2 more sources

Swarming to rank for information retrieval

Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009
This paper presents an approach to automatically optimize the retrieval quality of ranking functions. Taking a Swarm Intelligence perspective, we present a novel method, Swarm-Rank, which is well-founded in a Particle Swarm Optimization framework. SwarmRank learns a ranking function by optimizing the combination of various types of evidences such ...
Ernesto Diaz-Aviles, Wolfgang Nejdl, Lars Schmidt-Thieme   +2 more
openaire   +1 more source

Learning to Rank for Biomedical Information Retrieval

2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 2021
With the continuous development of biomedical, the scale of data has also continued to increase, which makes it difficult for researchers to extract information manually. In order to satisfy the researcher's information requirement, information retrieval techniques for the biomedical field have been resolved.
Yuying Peng, Yonghu Yang
openaire   +1 more source

Webformer: Pre-training with Web Pages for Information Retrieval

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022
Pre-trained language models (PLMs) have achieved great success in the area of Information Retrieval. Studies show that applying these models to ad-hoc document ranking can achieve better retrieval effectiveness.
Yu Guo, Zhengyi Ma, Jiaxin Mao, Hongjin Qian, Xinyu Zhang, Hao Jiang, Zhao Cao, Zhicheng Dou   +7 more
semanticscholar   +1 more source

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

Relevance Vector Ranking for Information Retrieval

Journal of Convergence Information Technology, 2010
In recent years, learning ranking function for information retrieval has drawn the attentions of the researchers from information retrieval and machine learning community. In existing approaches of learning to rank, the sparse prediction model only can be learned by support vector learning approach.
Fengxia Wang -, Huixia Jin -, Xiao Chang -   +2 more
openaire   +1 more source

Dominating ranking algorithm for information retrieval

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010
There are lots of ranking algorithms used in Web information retrieval. However, current algorithms have some problems: these algorithms are based on different calculation formulas to calculate the documents and query similarity or train a lot of training data to get corresponding calculation formula which calculate documents and query similarity.
Huilin Liu, Zhiqing Li, Junchang Xin, Chen Chen   +3 more
openaire   +1 more source

A Cooperative Neural Information Retrieval Pipeline with Knowledge Enhanced Automatic Query Reformulation

Web Search and Data Mining, 2022
This paper presents a neural information retrieval pipeline that integrates cooperative learning of query reformulation and neural retrieval models. Our pipeline first exploits an automatic query reformulator to reformulate the user-issued query and then
Xiangsheng Li, Jiaxin Mao, Weizhi Ma, Zhijing Wu, Yiqun Liu, M. Zhang, Shaoping Ma, Zhaowei Wang, Xiuqiang He   +8 more
semanticscholar   +1 more source

RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs

Neural Information Processing Systems
Large language models (LLMs) typically utilize the top-k contexts from a retriever in retrieval-augmented generation (RAG). In this work, we propose a novel instruction fine-tuning framework RankRAG, which instruction-tunes a single LLM for the dual ...
Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, M. Shoeybi, Bryan Catanzaro   +7 more
semanticscholar   +1 more source