Pairwise ranking is a machine learning technique used to rank items by comparing them in pairs and determining their relative order based on these comparisons.
Pairwise ranking has been widely studied and applied in various fields, including citation analysis, protein domain ranking, and medical image quality assessment. Researchers have developed different algorithms and models to improve the accuracy and efficiency of pairwise ranking, such as incorporating empirical Bayes methods, spectral seriation, and graph regularization. Some recent studies have also focused on addressing challenges like reducing annotation burden, handling missing or corrupted comparisons, and accounting for biases in crowdsourced pairwise comparisons.
A few notable research papers in this area include:
1. 'Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis' by Jiaying Gu and Roger Koenker, which adapts the pairwise comparison model for ranking and selection of journal influence.
2. 'Spectral Ranking using Seriation' by Fajwel Fogel, Alexandre d"Aspremont, and Milan Vojnovic, which introduces a seriation algorithm for ranking items based on pairwise comparisons and demonstrates its robustness to noise.
3. 'Active Ranking using Pairwise Comparisons' by Kevin G. Jamieson and Robert D. Nowak, which proposes an adaptive algorithm for ranking objects using pairwise comparisons under the assumption that objects can be embedded in a Euclidean space.
Practical applications of pairwise ranking include:
1. Ranking academic journals based on their influence in a specific field.
2. Identifying the most relevant protein domains in structural biology.
3. Assessing the quality of medical images for diagnostic purposes.
One company case study is the application of pairwise ranking in a medical image annotation software, which actively subsamples pairwise comparisons using a sorting algorithm with a human rater in the loop. This method reduces the number of comparisons required for a full ordinal ranking without compromising inter-rater reliability.
In conclusion, pairwise ranking is a powerful machine learning technique that has been applied to various domains and continues to evolve through ongoing research. By addressing challenges such as annotation burden, missing data, and biases, pairwise ranking can provide more accurate and efficient solutions for ranking tasks in diverse applications.
Pairwise Ranking
Pairwise Ranking Further Reading
1.Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis http://arxiv.org/abs/2112.11064v1 Jiaying Gu, Roger Koenker2.Spectral Ranking using Seriation http://arxiv.org/abs/1406.5370v4 Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic3.Simultaneous Rank Tests in Analysis of Covariance Based on Pairwise Ranking http://arxiv.org/abs/1802.03884v1 Hossein Mansouri, Fangyuan Zhang4.Active Ranking using Pairwise Comparisons http://arxiv.org/abs/1109.3701v2 Kevin G. Jamieson, Robert D. Nowak5.Aggregation of pairwise comparisons with reduction of biases http://arxiv.org/abs/1906.03711v1 Nadezhda Bugakova, Valentina Fedorova, Gleb Gusev, Alexey Drutsa6.Pairwise Ranking with Gaussian Kernels http://arxiv.org/abs/2304.03185v1 Guanhang Lei, Lei Shi7.Multiple graph regularized protein domain ranking http://arxiv.org/abs/1208.3779v3 Jim Jing-Yan Wang, Halima Bensmail, Xin Gao8.Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating http://arxiv.org/abs/2202.04823v1 Ikbeom Jang, Garrison Danley, Ken Chang, Jayashree Kalpathy-Cramer9.PromptShots at the FinNLP-2022 ERAI Tasks: Pairwise Comparison and Unsupervised Ranking http://arxiv.org/abs/2301.06606v1 Peratham Wiriyathammabhum10.Density-Ratio Based Personalised Ranking from Implicit Feedback http://arxiv.org/abs/2101.07481v1 Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi SatohPairwise Ranking Frequently Asked Questions
What is the pairwise ranking?
Pairwise ranking is a machine learning technique used to rank items by comparing them in pairs and determining their relative order based on these comparisons. It is widely applied in various fields, such as citation analysis, protein domain ranking, and medical image quality assessment. Researchers have developed different algorithms and models to improve the accuracy and efficiency of pairwise ranking, addressing challenges like reducing annotation burden, handling missing or corrupted comparisons, and accounting for biases in crowdsourced pairwise comparisons.
What is an example of pairwise ranking?
An example of pairwise ranking is ranking academic journals based on their influence in a specific field. By comparing journals in pairs and determining their relative order, researchers can create a ranking that reflects the journals" impact and relevance in the field. This can help researchers identify the most influential journals to publish their work or to find relevant research articles.
What is a pairwise comparison?
A pairwise comparison is a process of comparing two items or objects to determine their relative order or preference. In the context of pairwise ranking, pairwise comparisons are used to gather information about the relative order of items in a list, which can then be used to create a complete ranking of the items.
Why do we use pairwise comparison?
Pairwise comparison is used because it simplifies complex ranking problems by breaking them down into smaller, more manageable comparisons. By comparing items in pairs, it is easier to determine their relative order and create a complete ranking. Pairwise comparison also allows for the incorporation of various algorithms and models to improve the accuracy and efficiency of the ranking process.
What is paired comparison and ranking?
Paired comparison and ranking is a method that involves comparing items in pairs to determine their relative order or preference. This method is used in various fields, such as machine learning, psychology, and decision-making, to rank items based on their importance, relevance, or quality. Paired comparison and ranking can be applied to various types of data, including ordinal, interval, and ratio data.
What is pairwise in hockey?
Pairwise in hockey refers to a ranking system used by the NCAA to determine the seeding of teams in the national tournament. The pairwise ranking system compares teams in pairs based on various criteria, such as head-to-head results, strength of schedule, and overall win-loss record. This pairwise comparison method helps create a fair and accurate ranking of teams for the tournament.
How does pairwise ranking work in machine learning?
In machine learning, pairwise ranking works by comparing items in pairs and using the results of these comparisons to create a complete ranking. Various algorithms and models can be applied to improve the accuracy and efficiency of the ranking process. Some common approaches include incorporating empirical Bayes methods, spectral seriation, and graph regularization. These methods help address challenges like reducing annotation burden, handling missing or corrupted comparisons, and accounting for biases in crowdsourced pairwise comparisons.
What are some practical applications of pairwise ranking?
Practical applications of pairwise ranking include ranking academic journals based on their influence in a specific field, identifying the most relevant protein domains in structural biology, and assessing the quality of medical images for diagnostic purposes. Pairwise ranking can also be applied in other domains, such as sports rankings, product recommendations, and search engine optimization.
What are some notable research papers on pairwise ranking?
Some notable research papers on pairwise ranking include: 1. 'Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis' by Jiaying Gu and Roger Koenker, which adapts the pairwise comparison model for ranking and selection of journal influence. 2. 'Spectral Ranking using Seriation' by Fajwel Fogel, Alexandre d"Aspremont, and Milan Vojnovic, which introduces a seriation algorithm for ranking items based on pairwise comparisons and demonstrates its robustness to noise. 3. 'Active Ranking using Pairwise Comparisons' by Kevin G. Jamieson and Robert D. Nowak, which proposes an adaptive algorithm for ranking objects using pairwise comparisons under the assumption that objects can be embedded in a Euclidean space.
How can pairwise ranking be used in search engine optimization (SEO)?
Pairwise ranking can be used in search engine optimization (SEO) to improve the ranking of web pages in search engine results. By comparing web pages in pairs based on various factors, such as relevance, quality, and user engagement, pairwise ranking algorithms can create a more accurate and efficient ranking of web pages. This can help search engines provide better search results to users and improve the overall user experience.
Explore More Machine Learning Terms & Concepts
