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.
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31 May 2012TL;DR: In this article, a computing device may access a search query provided by a user, identify a set of search results in response to the search query, wherein one or more search result in the set are associated with a feature of a social-networking system, rank the search results based on one or multiple factors, boost one or several ranks of the one ormore search results associated with the feature to bring the feature of interest to the user's attention.
Abstract: In one embodiment, a computing device may access a search query provided by a user; identify a set of search results in response to the search query, wherein one or more search results in the set are associated with a feature of a social-networking system; rank the set of search results based on one or more factors; boost one or more ranks of the one or more search results associated with the feature to bring the feature to the user's attention; and present the set of search results to the user in order of its ranking.
103 citations
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19 Jul 2010TL;DR: This paper presents a probabilistic model which effectively ranks the possible interpretations of a keyword query over structured data, and introduces a scheme to diversify the search results by re-ranking query interpretations, taking into account redundancy of query results.
Abstract: Keyword queries over structured databases are notoriously ambiguous. No single interpretation of a keyword query can satisfy all users, and multiple interpretations may yield overlapping results. This paper proposes a scheme to balance the relevance and novelty of keyword search results over structured databases. Firstly, we present a probabilistic model which effectively ranks the possible interpretations of a keyword query over structured data. Then, we introduce a scheme to diversify the search results by re-ranking query interpretations, taking into account redundancy of query results. Finally, we propose α-nDCG-W and WS-recall, an adaptation of α-nDCG and S-recall metrics, taking into account graded relevance of subtopics. Our evaluation on two real-world datasets demonstrates that search results obtained using the proposed diversification algorithms better characterize possible answers available in the database than the results of the initial relevance ranking.
103 citations
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18 Jul 2005TL;DR: In this paper, the authors proposed a distribution of ads to be served with a document (for example, because they are relevant to the document) based on the ordinal ranking of a relevancy criteria of the document used to select the ad.
Abstract: Ads eligible to be served with a document (for example, because they are relevant to the document) may each be scored using a price parameter associated with the ad and an indication of relevancy of the ad to the document. The indication of relevancy of the ad to the document may be based on an ordinal ranking of a relevancy criteria of the document used to select the ad, and/or a value of a relevancy criteria of the document used to select the ad. The eligible ads may be determined by obtaining relevancy criteria for the document and selecting ads using at least some of the obtained relevancy criteria. The ads may be selected, and perhaps filtered, in a distributed manner.
103 citations
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01 Jan 2008TL;DR: The main contributions of this paper unfold into four main points: fully implemented models and algorithms for ranked XML retrieval with XPath Full-Text functionality, efficient and effective top-k query processing for semistructured data, support for integrating thesauri and ontologies with statistically quantified relationships among concepts, and a comprehensive description of the TopX system.
Abstract: Recent IR extensions to XML query languages such as Xpath 1.0 Full-Text or the NEXI query language of the INEX benchmark series reflect the emerging interest in IR-style ranked retrieval over semistructured data. TopX is a top-k retrieval engine for text and semistructured data. It terminates query execution as soon as it can safely determine the k top-ranked result elements according to a monotonic score aggregation function with respect to a multidimensional query. It efficiently supports vague search on both content- and structure-oriented query conditions for dynamic query relaxation with controllable influence on the result ranking. The main contributions of this paper unfold into four main points: (1) fully implemented models and algorithms for ranked XML retrieval with XPath Full-Text functionality, (2) efficient and effective top-k query processing for semistructured data, (3) support for integrating thesauri and ontologies with statistically quantified relationships among concepts, leveraged for word-sense disambiguation and query expansion, and (4) a comprehensive description of the TopX system, with performance experiments on large-scale corpora like TREC Terabyte and INEX Wikipedia.
103 citations
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04 Feb 2010TL;DR: There is a strong relationship between the amount and frequency of content change and relevance, and a novel probabilistic document ranking algorithm is developed that allows differential weighting of terms based on their temporal characteristics.
Abstract: Many web documents are dynamic, with content changing in varying amounts at varying frequencies. However, current document search algorithms have a static view of the document content, with only a single version of the document in the index at any point in time. In this paper, we present the first published analysis of using the temporal dynamics of document content to improve relevance ranking. We show that there is a strong relationship between the amount and frequency of content change and relevance. We develop a novel probabilistic document ranking algorithm that allows differential weighting of terms based on their temporal characteristics. By leveraging such content dynamics we show significant performance improvements for navigational queries.
103 citations