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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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Patent
Ogawa Yasutsugu1
17 Nov 1993
TL;DR: A document retrieval system includes a query converter for converting the retrieval condition designated by the user into a query which has a predetermined normal form in which keywords and at least one type of logical operation out of logical operations AND, OR and NOT are connected, a bibliographical information indicator for indicating a relation between each of said registered documents and keywords and a keyword connection table having relationship values, each relationship values representing the degree of relationship between each two keywords as discussed by the authors.
Abstract: A document retrieval system retrieves one or a plurality of registered documents from a document database responsive to retrieval conditions designated by a user. The document retrieval system includes a query converter for converting the retrieval condition designated by the user into a query which has a predetermined normal form in which keywords and at least one type of logical operation out of logical operations AND, OR and NOT are connected, a bibliographical information indicator for indicating a relation between each of said registered documents and keywords and a keyword connection table having relationship values, each of the relationship values representing the degree of relationship between each two keywords. The document retrieval system also includes a selector for referring the inverted file and the keyword connection so as to select one or a plurality of registered documents which satisfy the query, and an outputting circuit for outputting one or a plurality of registered documents selected by the selecting means.

137 citations

Proceedings ArticleDOI
06 Aug 2006
TL;DR: This work shows how to incorporate the topical model within both PageRank and HITS without affecting the overall property and still render insight into topic-level transition, and indicates that this technique outperforms other ranking approaches that incorporate textual analysis.
Abstract: Traditional web link-based ranking schemes use a single score to measure a page's authority without concern of the community from which that authority is derived. As a result, a resource that is highly popular for one topic may dominate the results of another topic in which it is less authoritative. To address this problem, we suggest calculating a score vector for each page to distinguish the contribution from different topics, using a random walk model that probabilistically combines page topic distribution and link structure. We show how to incorporate the topical model within both PageRank and HITS without affecting the overall property and still render insight into topic-level transition. Experiments on multiple datasets indicate that our technique outperforms other ranking approaches that incorporate textual analysis.

137 citations

Journal ArticleDOI
TL;DR: A “softening” of the hard Boolean scheme for information retrieval is presented, in which linguistic quantifiers are defined which capture the intrinsic vagueness of information needs.
Abstract: A “softening” of the hard Boolean scheme for information retrieval is presented. In this approach, information retrieval is seen as a multicriteria decision-making activity in which the criteria to be satisfied by the potential solutions, i.e., the archived documents, are the requirements expressed in the query. the retrieval function is then an overall decision function evaluating the degree to which each potential solution satisfies a query consisting of information requirements aggregated by operators. Linguistic quantifiers and a connector dealing with primary and optional criteria are defined and introduced in the query language in order to specify the aggregation criteria of the single query requirements. These criteria make it possible for users to express queries in a simple and self-explanatory manner. In particular, linguistic quantifiers are defined which capture the intrinsic vagueness of information needs. © 1995 John Wiley & Sons, Inc.

137 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed several distributed randomized schemes for the computation of the PageRank, where the pages can locally update their values by communicating to those connected by links, and they asymptotically converge in the mean-square sense to the true PageRank values.
Abstract: In the search engine of Google, the PageRank algorithm plays a crucial role in ranking the search results. The algorithm quantifies the importance of each web page based on the link structure of the web. We first provide an overview of the original problem setup. Then, we propose several distributed randomized schemes for the computation of the PageRank, where the pages can locally update their values by communicating to those connected by links. The main objective of the paper is to show that these schemes asymptotically converge in the mean-square sense to the true PageRank values. A detailed discussion on the close relations to the multi-agent consensus problems is also given.

137 citations

Journal ArticleDOI
TL;DR: A methodology for ranking the relevant services for a given request is proposed, introducing objective measures based on dominance relationships defined among the services, and methods for clustering therelevant services in a way that reveals and reflects the different trade-offs between the matched parameters are investigated.
Abstract: As the web is increasingly used not only to find answers to specific information needs but also to carry out various tasks, enhancing the capabilities of current web search engines with effective and efficient techniques for web service retrieval and selection becomes an important issue. Existing service matchmakers typically determine the relevance between a web service advertisement and a service request by computing an overall score that aggregates individual matching scores among the various parameters in their descriptions. Two main drawbacks characterize such approaches. First, there is no single matching criterion that is optimal for determining the similarity between parameters. Instead, there are numerous approaches ranging from Information Retrieval similarity measures up to semantic logic-based inference rules. Second, the reduction of individual scores to an overall similarity leads to significant information loss. Determining appropriate weights for these intermediate scores requires knowledge of user preferences, which is often not possible or easy to acquire. Instead, using a typical aggregation function, such as the average or the minimum of the degrees of match across the service parameters, introduces undesired bias, which often reduces the accuracy of the retrieval process. Consequently, several services, e.g., those having a single unmatched parameter, may be excluded from the result set, while being potentially good candidates. In this work, we present two complementary approaches that overcome the aforementioned deficiencies. First, we propose a methodology for ranking the relevant services for a given request, introducing objective measures based on dominance relationships defined among the services. Second, we investigate methods for clustering the relevant services in a way that reveals and reflects the different trade-offs between the matched parameters. We demonstrate the effectiveness and the efficiency of our proposed techniques and algorithms through extensive experimental evaluation on both real requests and relevance sets, as well as on synthetic scenarios.

137 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