Results 31 to 40 of about 1,080,302 (365)
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank [PDF]
International Conference on the Theory of Information Retrieval, 2022 Understanding why a model makes certain predictions is crucial when adapting it for real world decision making. LIME is a popular model-agnostic feature attribution method for the tasks of classification and regression.
Tanya Chowdhury, Razieh Rahimi, J. Allan
semanticscholar +1 more source
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
doaj +1 more source
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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Maximizing Marginal Fairness for Dynamic Learning to Rank [PDF]
The Web Conference, 2021 Rankings, especially those in search and recommendation systems, often determine how people access information and how information is exposed to people. Therefore, how to balance the relevance and fairness of information exposure is considered as one of ...
Tao Yang, Qingyao Ai
semanticscholar +1 more source
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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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 ...
George Sebastian Pirtoaca +2 more
openaire +3 more sources
Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach
AAAI Conference on Artificial Intelligence, 2021 Quantitative trading and investment decision making are intricate financial tasks that rely on accurate stock selection. Despite advances in deep learning that have made significant progress in the complex and highly stochastic stock prediction problem ...
Ramit Sawhney +4 more
semanticscholar +1 more source
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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Unbiased Learning-to-Rank with Biased Feedback [PDF]
Web Search and Data Mining, 2016 Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key ...
T. Joachims +2 more
semanticscholar +1 more source