scispace - formally typeset
Search or ask a question
Topic

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
More filters
01 Jan 2012
TL;DR: This chapter presents the SVMs for binary classification in Section 2, SVR in Section 3, ranking SVM in Section 4, and another recently developed method for learningranking SVM called Ranking Vector Machine (RVM) in Section 5.
Abstract: Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively Two special properties of SVMs are that SVMs achieve (1) high generalization by maximizing the margin and (2) support an efficient learning of nonlinear functions by kernel trick This chapter introduces these general concepts and techniques of SVMs for learning classification, regression, and ranking functions In particular, we first present the SVMs for binary classification in Section 2, SVR in Section 3, ranking SVM in Section 4, and another recently developed method for learning ranking SVM called Ranking Vector Machine (RVM) in Section 5

163 citations

Proceedings ArticleDOI
06 Nov 2007
TL;DR: An opinion retrieval algorithm that has a traditional information retrieval component to find topic relevant documents from a document set, an opinion classification component tofind documents having opinions from the results of the IR step, and a component to rank the documents based on their relevance to the query, and their degrees of having opinions about the query.
Abstract: Opinion retrieval is a document retrieval process, which requires documents to be retrieved and ranked according to their opinions about a query topic. A relevant document must satisfy two criteria: relevant to the query topic, and contains opinions about the query, no matter if they are positive or negative. In this paper, we describe an opinion retrieval algorithm. It has a traditional information retrieval (IR) component to find topic relevant documents from a document set, an opinion classification component to find documents having opinions from the results of the IR step, and a component to rank the documents based on their relevance to the query, and their degrees of having opinions about the query. We implemented the algorithm as a working system and tested it using TREC 2006 Blog Track data in automatic title-only runs. Our result showed 28% to 32% improvements in MAP score over the best automatic runs in this 2006 track. Our result is also 13% higher than a state-of-art opinion retrieval system, which is tested on the same data set.

163 citations

Journal ArticleDOI
TL;DR: These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features.

162 citations

Journal ArticleDOI
TL;DR: A linear programming method is developed to solve the MCDM problems with probabilistic linguistic information, and a case study about the evaluation of hospitals is conducted to illustrate the proposed method.

162 citations


Network Information
Related Topics (5)
Web page
50.3K papers, 975.1K citations
83% related
Ontology (information science)
57K papers, 869.1K citations
82% related
Graph (abstract data type)
69.9K papers, 1.2M citations
82% related
Feature learning
15.5K papers, 684.7K citations
81% related
Supervised learning
20.8K papers, 710.5K citations
81% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168