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 published on a yearly basis
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TL;DR: A novel approach to balance objective and penalty functions stochastically, i.e., stochastic ranking, is introduced, and a new view on penalty function methods in terms of the dominance of penalty and objective functions is presented.
Abstract: Penalty functions are often used in constrained optimization. However, it is very difficult to strike the right balance between objective and penalty functions. This paper introduces a novel approach to balance objective and penalty functions stochastically, i.e., stochastic ranking, and presents a new view on penalty function methods in terms of the dominance of penalty and objective functions. Some of the pitfalls of naive penalty methods are discussed in these terms. The new ranking method is tested using a (/spl mu/, /spl lambda/) evolution strategy on 13 benchmark problems. Our results show that suitable ranking alone (i.e., selection), without the introduction of complicated and specialized variation operators, is capable of improving the search performance significantly.
1,571 citations
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01 Oct 2001TL;DR: This work proposes the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval and achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
Abstract: Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user's desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user's query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
1,512 citations
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01 Jan 1998TL;DR: The MaximalMarginal Relevance (MMR) criterion as mentioned in this paper aims to reduce redundancy while maintaining query relevance in retrieving retrieved documents and selecting appropriate passages for text summarization.
Abstract: This paper presents a method for combining
query-relevance with information-novelty in the context
of text retrieval and summarization. The Maximal
Marginal Relevance (MMR) criterion strives to reduce
redundancy while maintaining query relevance in
re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results
indicate some benefits for MMR diversity ranking
in document retrieval and in single document summarization.
The latter are borne out by the recent results of the
SUMMAC conference in the evaluation of summarization
systems. However, the clearest advantage is demonstrated
in constructing non-redundant multi-document
summaries, where MMR results are clearly superior to
non-MMR passage selection.
1,479 citations
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14 Sep 2010
TL;DR: An improved human user computer interface system, wherein a user characteristic or set of characteristics, such as demographic profile or societal role, is employed to define a scope or domain of operation, is proposed in this article, where user privacy and anonymity is maintained by physical and algorithmic controls over access to the personal profiles, and releasing only aggregate data without personally identifying information or of small groups.
Abstract: An improved human user computer interface system, wherein a user characteristic or set of characteristics, such as demographic profile or societal “role”, is employed to define a scope or domain of operation. The operation itself may be a database search, to interactively define a taxonomic context for the operation, a business negotiation, or other activity. After retrieval of results, a scoring or ranking may be applied according to user define criteria, which are, for example, commensurate with the relevance to the context, but may be, for example, by date, source, or other secondary criteria. A user profile is preferably stored in a computer accessible form, and may be used to provide a history of use, persistent customization, collaborative filtering and demographic information for the user. Advantageously, user privacy and anonymity is maintained by physical and algorithmic controls over access to the personal profiles, and releasing only aggregate data without personally identifying information or of small groups.
1,465 citations
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01 Jul 2000TL;DR: The novel evaluation methods and the case demonstrate that non-dichotomous relevance assessments are applicable in IR experiments, may reveal interesting phenomena, and allow harder testing of IR methods.
Abstract: This paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents This is desirable from the user point of view in modem large IR environments The proposed methods are (1) a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) two novel measures computing the cumulative gain the user obtains by examining the retrieval result up to a given ranked position We then demonstrate the use of these evaluation methods in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance The test was run with a best match retrieval system (In- Query I) in a text database consisting of newspaper articles The results indicate that the tested strong query structures are most effective in retrieving highly relevant documents The differences between the query types are practically essential and statistically significant More generally, the novel evaluation methods and the case demonstrate that non-dichotomous relevance assessments are applicable in IR experiments, may reveal interesting phenomena, and allow harder testing of IR methods
1,461 citations