Results 31 to 40 of about 1,093,151 (324)
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
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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
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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ADAPTATION OF LAMBDAMART MODEL TO SEMI-SUPERVISED LEARNING
Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології, 2023 The problem of information searching is very common in the age of the internet and Big Data. Usually, there are huge collections of documents and only multiple percent of them are relevant. In this setup brute-force methods are useless.
Klym Yamkovyi
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Extracting Emotion Causes Using Learning to Rank Methods From an Information Retrieval Perspective
IEEE Access, 2019 Emotion cause extraction is a challenging task for the fine-grained emotion analysis. Even though a few studies have addressed the task using clause-level classification methods, most of them have partly ignored emotion-level context information.
Bo Xu +5 more
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Conference Paper Recommendation for Academic Conferences
IEEE Access, 2018 With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers.
Shuchen Li +3 more
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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
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
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Learning to Rank from Samples of Variable Quality [PDF]
, 2018 Training deep neural networks requires many training samples, but in practice, training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of ...
Dehghani, Mostafa, Kamps, Jaap
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Image Retrieval Based on Learning to Rank and Multiple Loss
ISPRS International Journal of Geo-Information, 2019 Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried ...
Lili Fan +4 more
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