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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
01 Oct 2011
TL;DR: The study indicates that both the topical content of information sources and social network structure affect source credibility, and designs a novel method of automatically identifying and ranking social network users according to their relevance and expertise for a given topic.
Abstract: A task of primary importance for social network users is to decide whose updates to subscribe to in order to maximize the relevance, credibility, and quality of the information received. To address this problem, we conducted an experiment designed to measure the extent to which different factors in online social networks affect both explicit and implicit judgments of credibility. The results of the study indicate that both the topical content of information sources and social network structure affect source credibility. Based on these results, we designed a novel method of automatically identifying and ranking social network users according to their relevance and expertise for a given topic. We performed empirical studies to compare a variety of alternative ranking algorithms and a proprietary service provided by a commercial website specifically designed for the same purpose. Our findings show a great potential for automatically identifying and ranking credible users for any given topic.

171 citations

Proceedings ArticleDOI
06 Aug 2006
TL;DR: A new framework for associating ads with web pages based on Genetic Programming (GP), which aims at learning functions that select the most appropriate ads, given the contents of a Web page to optimize overall precision and minimize the number of misplacements.
Abstract: Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging technical problems and raises interesting questions. For instance, how to design ranking functions able to satisfy conflicting goals such as selecting advertisements (ads) that are relevant to the users and suitable and profitable to the publishers and advertisers? In this paper we propose a new framework for associating ads with web pages based on Genetic Programming (GP). Our GP method aims at learning functions that select the most appropriate ads, given the contents of a Web page. These ranking functions are designed to optimize overall precision and minimize the number of misplacements. By using a real ad collection and web pages from a newspaper, we obtained a gain over a state-of-the-art baseline method of 61.7% in average precision. Further, by evolving individuals to provide good ranking estimations, GP was able to discover ranking functions that are very effective in placing ads in web pages while avoiding irrelevant ones.

171 citations

Book ChapterDOI
31 Aug 2004
TL;DR: This work adapt and apply principles of probabilistic models from Information Retrieval for structured data to solve the problem of ranking answers to a database query when many tuples are returned.
Abstract: We investigate the problem of ranking answers to a database query when many tuples are returned. We adapt and apply principles of probabilistic models from Information Retrieval for structured data. Our proposed solution is domain independent. It leverages data and workload statistics and correlations. Our ranking functions can be further customized for different applications. We present results of preliminary experiments which demonstrate the efficiency as well as the quality of our ranking system.

171 citations

Journal ArticleDOI
TL;DR: Analysis shows that Alta Vista, Excite and Infoseek are the top three services, with their relative rank changing depending on how one operationally defines the concept of relevance.
Abstract: Five search engines, Alta Vista, Excite, HotBot, Infoseek, and Lycos, are compared for precision on the first 20 results returned for 15 queries, adding weight for ranking effectiveness. All searching was done from January 31 to March 12, 1997. In the study, steps are taken to ensure that bias has not unduly influenced the evaluation. Friedmann’s randomized block design is used to perform multiple comparisons for significance. Analysis shows that Alta Vista, Excite and Infoseek are the top three services, with their relative rank changing depending on how one operationally defines the concept of relevance. Correspondence analysis shows that Lycos performed better on short, unstructured queries, whereas HotBot performed better on structured queries.

171 citations

Proceedings ArticleDOI
29 Oct 2007
TL;DR: The proposed methods form the first steps to bring together advanced information retrieval and secure search capabilities for a wide range of applications including managing data in government and business operations, enabling scholarly study of sensitive data, and facilitating the document discovery process in litigation.
Abstract: This paper introduces a new framework for confidentiality preserving rank-ordered search and retrieval over large document collections. The proposed framework not only protects document/query confidentiality against an outside intruder, but also prevents an untrusted data center from learning information about the query and the document collection. We present practical techniques for proper integration of relevance scoring methods and cryptographic techniques, such as order preserving encryption, to protect data collections and indices and provide efficient and accurate search capabilities to securely rank-order documents in response to a query. Experimental results on the W3C collection show that these techniques have comparable performance to conventional search systems designed for non-encrypted data in terms of search accuracy. The proposed methods thus form the first steps to bring together advanced information retrieval and secure search capabilities for a wide range of applications including managing data in government and business operations, enabling scholarly study of sensitive data, and facilitating the document discovery process in litigation.

171 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