Results 291 to 300 of about 1,093,151 (324)

Learning to rank tags

Proceedings of the ACM International Conference on Image and Video Retrieval, 2010
Social images sharing websites, such as Flickr and Picasa, are becoming very popular nowadays. Users are generally recommended to annotate images with free tags, yet these tags are orderless, and thus quite limited for applications like image search, retrieval and management.
Zheng Wang, Jiashi Feng, Changshui Zhang, Shuicheng Yan   +3 more
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Unbiased Learning to Rank

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018
Implicit feedback (e.g., user clicks) is an important source of data for modern search engines. While heavily biased [8, 9, 11, 27], it is cheap to collect and particularly useful for user-centric retrieval applications such as search ranking. To develop an unbiased learning-to-rank system with biased feedback, previous studies have focused on ...
Qingyao Ai, Jiaxin Mao, Yiqun Liu, W. Bruce Croft   +3 more
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Learning to Rank with Ensemble Ranking SVM

Neural Processing Letters, 2014
In this paper, we propose a novel learning to rank method using Ensemble Ranking SVM. Ensemble Ranking SVM is based on Ranking SVM which has been commonly used for learning to rank. The basic idea of Ranking SVM is to formulate the problem of learning to rank as that of binary classification on instance pairs.
Cheolkon Jung, Yanbo Shen, Licheng Jiao
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Learning to re-rank

Proceedings of the 20th international conference on World wide web, 2011
Our objective is to improve the performance of keyword based image search engines by re-ranking their original results. To this end, we address three limitations of existing search engines in this paper. First, there is no straight-forward, fully automated way of going from textual queries to visual features.
Vidit Jain, Manik Varma
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Learning To Rank Resources

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017
We present a learning-to-rank approach for resource selection. We develop features for resource ranking and present a training approach that does not require human judgments. Our method is well-suited to environments with a large number of resources such as selective search, is an improvement over the state-of-the-art in resource selection for ...
Zhuyun Dai, Yubin Kim, Jamie Callan
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Ensemble Ranking SVM for learning to rank

2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011
This paper deals with the problem of learning to rank documents for information retrieval. Until now, Ranking SVM has been successfully used for learning to rank documents. The basic idea of Ranking SVM is to formalize learning to rank as a problem of binary classification on instance pairs and solve the problem using SVM.
null Cheolkon Jung, L.C. Jiao, null Yanbo Shen   +2 more
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Neural Learning to Rank using TensorFlow Ranking

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, 2019
A number of open source packages harnessing the power of deep learning have emerged in recent years and are under active development, including TensorFlow, PyTorch and others. Supervised learning is one of the main use cases of deep learning packages. However, compared with the comprehensive support for classification or regression in open-source deep ...
Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang   +3 more
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Learning to rank for information retrieval

Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2007
Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to ...
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Learning-to-Count by Learning-to-Rank

2023 20th Conference on Robots and Vision (CRV), 2023
Adriano C. D’ Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh   +2 more
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GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy

Nucleic Acids Research, 2022
Donovan H Parks, Christian Rinke, Aaron J Mussig   +2 more
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