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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TL;DR: In this paper, the authors considered the use of imperfect information for risk sharing and incentive purposes when perfect observation of actions and outcomes is impossible, making complete contracting infeasible, and formulated a generalized agency model to analyse this problem.
Abstract: This paper considers the use of imperfect information for risk sharing and incentive purposes when perfect observation of actions and outcomes is impossible, making complete contracting infeasible. The incentive-insurance problem is defined to consist of two parts: the choice of an information system and the design of a sharing rule based on the information system. A generalized agency model is formulated to analyse this problem. The agency models of Ross (1973a, b), Wilson (1968), Stiglitz (1974), Mirrlees (1976), Harris and Raviv (1979), Holmstrom (1979) a.o. appear as special cases of the generalized model. The analysis focuses on the value of information in the agency information problem. The set of information systems which are valuable—i.e. improve risk sharing and incentives in a Pareto sense—is characterized. A problem-independent ranking of information systems for the agency information problem is then characterized under the assumption that the agent's preferences are additive in money and actions. The ranking may be viewed as a generalization of Blackwell's ranking of information systems for decision problems, to this particular game. When the agent's risk preferences depend on his choice of action, on the other hand, it is shown that the Blackwell ranking may be invalid. Randomized incentive schemes are shown to be efficient when the incentive effect of risk is positive and sufficiently large relative to the absolute risk aversion of the partners.
193 citations
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01 Jun 1996TL;DR: An initial investigation into the use of a 2-step query optimization strategy is described and hybrid-shipping is shown to at least match the best of the two "pure" policies, and in some situations to perform better than both.
Abstract: The construction of high-performance database systems that combine the best aspects of the relational and object-oriented approaches requires the design of client-server architectures that can fully exploit client and server resources in a flexible manner. The two predominant paradigms for client-server query execution are data-shipping and query-shipping We first define these policies in terms of the restrictions they place on operator site selection during query optimization. We then investigate the performance tradeoffs between them for bulk query processing. While each strategy has advantages, neither one on its own is efficient across a wide range of circumstances. We describe and evaluate a more flexible policy called hybrid-shipping, which can execute queries at clients, servers, or any combination of the two. Hybrid-shipping is shown to at least match the best of the two "pure" policies, and in some situations, to perform better than both. The implementation of hybrid-shipping raises a number of difficult problems for query optimization. We describe an initial investigation into the use of a 2-step query optimization strategy as a way of addressing these issues.
193 citations
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TL;DR: In this paper, the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of systems is large and initial samples from each system have already been taken is addressed.
Abstract: In this paper we address the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of systems is large and initial samples from each system have already been taken This problem may be encountered when a heuristic search procedure--perhaps one originally designed for use in a deterministic environment--has been applied in a simulation-optimization context Because of stochastic variation, the system with the best sample mean at the end of the search procedure may not coincide with the true best system encountered during the search This paper develops statistical procedures that return the best system encountered by the search (or one near the best) with a prespecified probability We approach this problem using combinations of statistical subset selection and indifference-zone ranking procedures The subset-selection procedures, which use only the data already collected, screen out the obviously inferior systems, while the indifference-zone procedures, which require additional simulation effort, distinguish the best from the less obviously inferior systems
190 citations
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19 Sep 2001TL;DR: An ontology of place is presented that combines limited coordinate data with qualitative spatial relationships between places and has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology.
Abstract: Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness as well as semantic closeness with respect to the topic of interest. Here we present an ontology of place that combines limited coordinate data with qualitative spatial relationships between places. This parsimonious model of place is intended to suppon information retrieval tasks that may be global in scope. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This can be combined with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects.
190 citations
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TL;DR: The aim of this paper is to investigate the multiple attribute decision making problems with linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of linguistic variables.
Abstract: The aim of this paper is to investigate the multiple attribute decision making problems with linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of linguistic variables. We first introduce some approaches to obtaining the weight information of attributes, and then establish an optimization model based on the ideal point of attribute values, by which the attribute weights can be determined. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the numerical weighting linguistic average (NWLA) operator to aggregate the linguistic variables corresponding to each alternative, and then rank the alternatives by means of the aggregated linguistic information. Finally, the developed method is applied to the ranking and selection of propulsion/manoeuvring system of a double-ended passenger ferry.
190 citations