Results 11 to 20 of about 1,093,151 (324)
ProtDec-LTR3.0: Protein Remote Homology Detection by Incorporating Profile-Based Features Into Learning to Rank [PDF]
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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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
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Differentiable Ranking Metric Using Relaxed Sorting for Top-K Recommendation
IEEE Access, 2021 Most recommenders generate recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top- $K$ -items of high scores.
Hyunsung Lee +4 more
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RankEval: Evaluation and investigation of ranking models
SoftwareX, 2020 RankEval is a Python open-source tool for the analysis and evaluation of ranking models based on ensembles of decision trees. Learning-to-Rank (LtR) approaches that generate tree-ensembles are considered the most effective solution for difficult ranking ...
Claudio Lucchese +4 more
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Effective Learning to Rank Persian Web Content [PDF]
Journal of Information Technology Management, 2019 Persian language is one of the most widely used languages in the Web environment. Hence, the Persian Web includes invaluable information that is required to be retrieved effectively.
Amir Hosein Keyhanipour
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Learning to rank spatio-temporal event hotspots
Crime Science, 2020 Background Crime, traffic accidents, terrorist attacks, and other space-time random events are unevenly distributed in space and time. In the case of crime, hotspot and other proactive policing programs aim to focus limited resources at the highest risk ...
George Mohler +3 more
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Fair pairwise learning to rank [PDF]
2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020 Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why a specific candidate, for instance, was not ...
Mattia Cerrato +4 more
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Journal of King Saud University: Computer and Information Sciences, 2020 This paper proposed a new method for learning to rank documents using enumerative feature subsetting in the presence of the implicit user feedback of the various classes of users.
Mohd Wazih Ahmad +2 more
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Learning-to-rank vs ranking-to-learn
Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020 In Continuous Integration (CI), regression testing is constrained by the time between commits. This demands for careful selection and/or prioritization of test cases within test suites too large to be run entirely. To this aim, some Machine Learning (ML) techniques have been proposed, as an alternative to deterministic approaches.
Antonia Bertolino +4 more
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Boosting the Learning for Ranking Patterns
Algorithms, 2023 Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user ...
Nassim Belmecheri +4 more
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