Results 21 to 30 of about 1,093,151 (324)
ACM Transactions on Information Systems, 2021 How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning algorithms with logged data, namely, the offline unbiased ...
Qingyao Ai +3 more
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Deep metric learning to rank [PDF]
, 2019 We propose a novel deep metric learning method by revisiting the learning to rank approach. Our method, named FastAP, optimizes the rank-based Average Precision measure, using an approximation derived from distance quantization.
Cakir, Fatih +4 more
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Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Fashion industry is driven by fashion cycles, in which a fashion item is launched, rises to mainstream appeal and becomes a trend, then diminishes and eventually becomes obsolete. These properties make it critical to incorporate temporal information when
Aayush Singha Roy +3 more
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Reinforcement Learning to Rank [PDF]
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019 Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent.
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Learning to Rank Retargeted Images [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 Image retargeting techniques that adjust images into different\ud sizes have attracted much attention recently. Objective\ud quality assessment (OQA) of image retargeting results\ud is often desired to automatically select the best results. Existing\ud OQA methods output an absolute score for each retargeted\ud image and use these scores to compare ...
Yang, Chen,, Yong-Jin, Liu,, Lai, Yukun
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Answering questions by learning to rank - Learning to rank by answering questions [PDF]
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019 Presented at EMNLP 2019; 10 pages, 5 ...
Pîrtoacă, George-Sebastian +2 more
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An Alternative Cross Entropy Loss for Learning-to-Rank
, 2021 Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval. These algorithms learn to rank a set of items by optimizing a loss that is a function of the entire set --
Bruch, Sebastian
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Cognitive biomarker prioritization in Alzheimer’s Disease using brain morphometric data
BMC Medical Informatics and Decision Making, 2020 Background Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer’s Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of ...
Bo Peng +6 more
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Learning to Rank for Multi-Step Ahead Time-Series Forecasting
IEEE Access, 2021 Time-series forecasting is a fundamental problem associated with a wide range of engineering, financial, and social applications. The challenge arises from the complexity due to the time-variant property of time series and the inevitable diminishing ...
Jiuding Duan, Hisashi Kashima
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Siamese-Network-Based Learning to Rank for No-Reference 2D and 3D Image Quality Assessment
IEEE Access, 2019 2D image quality assessment (IQA) and stereoscopic 3D IQA are considered as two different tasks in the literature. In this paper, we present an index for both no-reference 2D and 3D IQA.
Yuzhen Niu +3 more
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