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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


Papers
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Proceedings ArticleDOI
02 Nov 2009
TL;DR: This article proposes Supervised Semantic Indexing (SSI), an algorithm that is trained on (query, document) pairs of text documents to predict the quality of their match and proposes several improvements to the basic model, including low rank (but diagonal preserving) representations, and correlated feature hashing (CFH).
Abstract: In this article we propose Supervised Semantic Indexing (SSI), an algorithm that is trained on (query, document) pairs of text documents to predict the quality of their match. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained with a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, and correlated feature hashing (CFH). We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.

100 citations

Journal ArticleDOI
TL;DR: Question Answering (QA) systems give the ability to answer questions posed in natural language by extracting, from a repository of documents, fragments of documents that contain material relevant to the answer.
Abstract: Question Answering (QA) is a specific type of information retrieval. Given a set of documents, a Question Answering system attempts to find out the correct answer to the question pose in natural language. Question answering is multidisciplinary. It involves information technology, artificial intelligence, natural language processing, knowledge and database management and cognitive science. From the technological perspective, question answering uses natural or statistical language processing, information retrieval, and knowledge representation and reasoning as potential building blocks. It involves text classification, information extraction and summarization technologies. In general, question answering system (QAS) has three components such as question classification, information retrieval, and answer extraction. These components play a essential role in QAS. Question classification play primary role in QA system to categorize the question based upon on the type of its entity. Information retrieval method is get of identify success by extracting out applicable answer post by their intelligent question answering system. Finally, answer extraction module is rising topics in the QAS where these systems are often requiring ranking and validating a candidate’s answer. Most of the Question Answering systems consists of three main modules: question processing, document processing and answer processing. Question processing module plays an important part in QA systems. If this module doesn't work correctly, it will make problems for other sections. Moreover answer processing module is an emerging topic in Question Answering, in which these systems are often required to rank and validate candidate answers. These techniques aiming at discovering the short and precise answers are often based on the semantic classification. QA systems give the ability to answer questions posed in natural language by extracting, from a repository of documents, fragments of documents that contain material relevant to the answer.

100 citations

Journal ArticleDOI
TL;DR: This study tries to answer the question how similar ranking methods are in practice, i.e., how likely they are to induce the same ranking, by means of numerical simulations.

100 citations

Journal ArticleDOI
TL;DR: While, perhaps inevitably given the size of the domain, the book misses quite a large amount of relevant material, it does provide an excellent, accessible, and stimulating discussion of the material it does cover and makes a valuable addition to the canon of rating and ranking.
Abstract: Who's #1? The science of rating and ranking, by Amy N. Langville and Carl D. Meyer, Princeton, Princeton University Press, 2012, xvi+247 pp., £19.95 or US$29.95 (hardback), ISBN 978-0-691-15422-0 T...

100 citations

Proceedings ArticleDOI
19 Jul 2018
TL;DR: In this article, a ranking distillation (RD) method was proposed to train a student model to learn to rank documents/items from both the training data and the supervision of a larger teacher model.
Abstract: We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. Knowledge distillation (KD) was shown to be successful in image recognition to achieve both effectiveness and efficiency. We propose a KD technique for learning to rank problems, called ranking distillation (RD). Specifically, we train a smaller student model to learn to rank documents/items from both the training data and the supervision of a larger teacher model. The student model achieves a similar ranking performance to that of the large teacher model, but its smaller model size makes the online inference more efficient. RD is flexible because it is orthogonal to the choices of ranking models for the teacher and student. We address the challenges of RD for ranking problems. The experiments on public data sets and state-of-the-art recommendation models showed that RD achieves its design purposes: the student model learnt with RD has less than an half size of the teacher model while achieving a ranking performance similar tothe teacher model and much better than the student model learnt without RD.

100 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168