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
Journal ArticleDOI
TL;DR: A mathematical framework is described that allows us to calculate centrality in multilayer networks and rank nodes accordingly, finding the ones that play the most central roles in the cohesion of the whole structure, bridging together different types of relations.
Abstract: A challenging problem is to identify the most central agents in interconnected multilayer networks. Here, De Domenico et al. present a mathematical framework to calculate centrality in such networks—versatility—and rank nodes accordingly.

348 citations

Journal ArticleDOI
TL;DR: A Max-Relevance-Max-Distance (MRMD) feature ranking method, which balances accuracy and stability of feature ranking and prediction task, and runs faster than other filtering and wrapping methods, such as mRMR and Information Gain.

344 citations

Proceedings ArticleDOI
11 Jun 2007
TL;DR: This paper proposes a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document and proposes several efficient query processing methods for the new ranking method.
Abstract: With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. Compared with previous approaches, our new ranking method is simple yet effective, and agrees with human perceptions. We also study efficient query processing methods for the new ranking method, and propose algorithms that have minimal accesses to the database. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.

343 citations

Patent
Jiong Wu1
02 Nov 1998
TL;DR: In this paper, a search query is applied to documents in a document repository wherein the documents are organized into a hierarchy and a search engine searches the hierarchy to return documents which match a query term either directly or indirectly.
Abstract: A search query is applied to documents in a document repository wherein the documents are organized into a hierarchy. A search engine searches the hierarchy to return documents which match a query term either directly or indirectly. A specific embodiment of the search engine organizes the query term into individual subterms and matches the subterms against documents, returning only those documents which indirectly match the entire search query term and directly match at least one of the query subterms.

342 citations

Journal ArticleDOI
TL;DR: This study proposes a new method for query expansion based on user interactions recorded in user logs that extracts correlations between query terms and document terms by analyzing user logs and can produce much better results than both the classical search method and the other query expansion methods.
Abstract: Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.

342 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