Results 21 to 30 of about 1,080,302 (365)
Reducing Disparate Exposure in Ranking: A Learning To Rank Approach [PDF]
The Web Conference, 2018 Ranked search results have become the main mechanism by which we find content, products, places, and people online. Thus their ordering contributes not only to the satisfaction of the searcher, but also to career and business opportunities, educational ...
Meike Zehlike, Carlos Castillo
semanticscholar +3 more sources
DNorm: disease name normalization with pairwise learning to rank. [PDF]
Bioinformatics, 2013 Leaman R, Islamaj Dogan R, Lu Z.
europepmc +3 more sources
Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations. [PDF]
JMIR Med Inform, 2016 Chen J, Zheng J, Yu H.
europepmc +2 more sources
A General Framework for Counterfactual Learning-to-Rank
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018 Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by presentation bias.
Aman Agarwal +3 more
semanticscholar +3 more sources
Lero: A Learning-to-Rank Query Optimizer [PDF]
Proceedings of the VLDB Endowment, 2023 A recent line of works apply machine learning techniques to assist or rebuild cost-based query optimizers in DBMS. While exhibiting superiority in some benchmarks, their deficiencies, e.g., unstable performance, high training cost, and slow model ...
Rong Zhu +6 more
semanticscholar +1 more source
Learning to Rank in Generative Retrieval [PDF]
AAAI Conference on Artificial Intelligence, 2023 Generative retrieval stands out as a promising new paradigm in text retrieval that aims to generate identifier strings of relevant passages as the retrieval target.
Yongqing Li +4 more
semanticscholar +1 more source
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications.
Jiduan Liu +8 more
semanticscholar +1 more source
Differentiable Unbiased Online Learning to Rank [PDF]
, 2018 Online Learning to Rank (OLTR) methods optimize rankers based on user interactions. State-of-the-art OLTR methods are built specifically for linear models. Their approaches do not extend well to non-linear models such as neural networks.
Harrie Oosterhuis, Maarten de Rijke
openalex +3 more sources
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems [PDF]
The Web Conference, 2020 Learning effective feature crosses is the key behind building recommender systems. However, the sparse and large feature space requires exhaustive search to identify effective crosses.
Ruoxi Wang +6 more
semanticscholar +1 more source
PeerRank: Robust Learning to Rank With Peer Loss Over Noisy Labels
IEEE Access, 2022 User-generated data are extensively utilized in learning to rank as they are easy to collect and up-to-date. However, the data inevitably contain noisy labels attributed to users’ annotation mistakes, lack of domain knowledge, system failure, etc.,
Xin Wu, Qing Liu, Jiarui Qin, Yong Yu
doaj +1 more source