Results 31 to 40 of about 1,099,722 (325)
Groupwise Learning to Rank Algorithm with Introduction of Activated Weighting [PDF]
Jisuanji kexue yu tansuo, 2022 Learning to rank (LtR) applies supervised machine learning (SML) technologies to the ranking problems, aiming at optimizing the relevance of input document list. As regard to previous studies on the deep ranking model, the calculation of the relevance of
LI Yuxuan, HONG Xuehai, WANG Yang, TANG Zhengzheng, BAN Yan
doaj +1 more source
Simple to Complex Cross-modal Learning to Rank [PDF]
, 1998 The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure ...
Luo, Minnan +5 more
core +3 more sources
Recommending GitHub Projects for Developer Onboarding
IEEE Access, 2018 Open-source platform (e.g., GitHub) creates a tremendous opportunity for developers to learn and build experience. Contribution to open source can be rewarding for developers and advocates the evolutionary progress of the open-source software.
Chao Liu +4 more
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Improving Query Quality for Transductive Learning in Learning to Rank
IEEE Access, 2020 In traditional transductive learning, all queries are used in learning to rank in order to generate pseudo-labels when sufficient training data are not available. However, low quality queries may affect retrieval performance in transductive learning.
Xin Zhang, Zhi Cheng
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IEEE Access, 2019 Protein remote homology detection is one of the most challenging problems in the field of protein sequence analysis, which is an important step for both theoretical research (such as the understanding of structures and functions of proteins) and drug ...
Bin Liu, Yulin Zhu
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Learning to Rank Sports Teams on a Graph
Applied Sciences, 2020 To improve the prediction ability of ranking models in sports, a generalized PageRank model is introduced. In the model, a game graph is constructed from the perspective of Bayesian correction with game results. In the graph, nodes represent teams, and a
Jian Shi, Xin-Yu Tian
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Ranking Algorithms for Word Ordering in Surface Realization
Information, 2021 In natural language generation, word ordering is the task of putting the words composing the output surface form in the correct grammatical order. In this paper, we propose to apply general learning-to-rank algorithms to the task of word ordering in the ...
Alessandro Mazzei +3 more
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Controlling Fairness and Bias in Dynamic Learning-to-Rank
, 2020 Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.
Abdollahpouri Himan +5 more
core +1 more source
Learning to rank from medical imaging data [PDF]
, 2012 Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order.
F. Pedregosa +11 more
core +4 more sources
Proceedings of the 23rd International Conference on World Wide Web, 2014 User-generated content is a growing source of valuable information and its analysis can lead to a better understanding of the users needs and trends. In this paper, we leverage user feedback about YouTube videos for the task of affective video ranking.
Orellana-Rodriguez, Claudia +3 more
openaire +2 more sources