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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.


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Proceedings ArticleDOI
26 Oct 2008
TL;DR: It is found that searchers are more likely to be successful when the frequencies of the query and destination URL are similar, and it is shown that the benefits obtained by search and navigation actions depend on the frequency of the information goal.
Abstract: We describe results from Web search log studies aimed at elucidating user behaviors associated with queries and destination URLs that appear with different frequencies. We note the diversity of information goals that searchers have and the differing ways that goals are specified. We examine rare and common information goals that are specified using rare or common queries. We identify several significant differences in user behavior depending on the rarity of the query and the destination URL. We find that searchers are more likely to be successful when the frequencies of the query and destination URL are similar. We also establish that the behavioral differences observed for queries and goals of varying rarity persist even after accounting for potential confounding variables, including query length, search engine ranking, session duration, and task difficulty. Finally, using an information-theoretic measure of search difficulty, we show that the benefits obtained by search and navigation actions depend on the frequency of the information goal.

166 citations

Patent
29 Jun 2001
TL;DR: In this article, a method and system for constructing a text summarization is presented, where a user profile indicative of a user's interests is defined in terms of the ontology concepts and a document's relevance to the user is determined based upon the user profile.
Abstract: A method and system for constructing a text summarization. At least one domain ontology that includes a set of concepts is selected. A user profile indicative of a user's interests is defined in terms of the ontology concepts. A document's relevance to the user is determined based upon the user profile. If the document is relevant, at least a portion of the ontology is used to extract concepts from the document. The degree of match between the extracted concepts and the user profile concepts is determined and the document text summary is generated if the degree of match exceeds a predetermined threshold. Generating the summary may include selecting sentences based on the concepts in the user profile, ranking the selected sentences by relevance to the user profile, selecting sentences for inclusion in the document text summary based upon the ranking, and merging the selected sentences into the document text summary.

166 citations

Patent
22 Dec 2004
TL;DR: In this paper, a system and a method are directed to targeted graphical advertisements, which may involve identifying a graphical advertisement associated with an entity (e.g., advertiser), associating one or more concepts with the graphical advertisement, and delivering the graphical advertisements associated with the concept.
Abstract: A system and a method are directed to targeted graphical advertisements, which may involve identifying a graphical advertisement associated with an entity (e.g., advertiser); associating one or more concepts with the graphical advertisement; receiving a request for an advertisement associated with a concept; and delivering the graphical advertisement associated with the concept, wherein the graphical advertisement is positioned for display based on a ranking among advertisements for the concept, the ranking being based at least on a price parameter amount offered by the entity.

166 citations

PatentDOI
TL;DR: In this article, the authors proposed a voice query extension method that detects voice activity of a user from an input signal and extracting a feature vector from the voice activity, converting the feature vector into at least one phoneme sequence and generating the at least phoneme sequences; matching the matching words with words registered in a dictionary, extracting a string of matched words with a linguistic meaning, and selecting the string of the matched words as a query; determining whether the query is in a predetermined first language, and when the query was not in the first language as a result of the determining,
Abstract: A voice query extension method and system. The voice query extension method includes: detecting voice activity of a user from an input signal and extracting a feature vector from the voice activity; converting the feature vector into at least one phoneme sequence and generating the at least one phoneme sequence; matching the at least one phoneme sequence with words registered in a dictionary, extracting a string of the matched words with a linguistic meaning, and selecting the string of the matched words as a query; determining whether the query is in a predetermined first language, and when the query is not in the first language as a result of the determining, converting the query using a phoneme to grapheme rule, and generating a query in the first language; and searching using the query in the first language.

165 citations

Proceedings ArticleDOI
18 Jun 2007
TL;DR: Overall, TableSeer eliminates the burden of manually extract table data from digital libraries and enables users to automatically examine tables, and proposes an extensive set of medium-independent metadata for tables that scientists and other users can adopt for representing table information.
Abstract: Tables are ubiquitous in digital libraries. In scientific documents, tables are widely used to present experimental results or statistical data in a condensed fashion. However, current search engines do not support table search. The difficulty of automatic extracting tables from un-tagged documents, the lack of a universal table metadata specification, and the limitation of the existing ranking schemes make table search problem challenging. In this paper, we describe TableSeer, a search engine for tables. TableSeer crawls digital libraries, detects tables from documents, extracts tables metadata, indexes and ranks tables, and provides a user-friendly search interface. We propose an extensive set of medium-independent metadata for tables that scientists and other users can adopt for representing table information. In addition, we devise a novel page box-cutting method to improve the performance of the table detection. Given a query, TableSeer ranks the matched tables using an innovative ranking algorithm - TableRank. TableRank rates each query, tableℂ pair with a tailored vector space model and a specific term weighting scheme. Overall, TableSeer eliminates the burden of manually extract table data from digital libraries and enables users to automatically examine tables. We demonstrate the value of TableSeer with empirical studies on scientific documents.

165 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