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: A general decision-theoretic model is developed and a formula which estimates the number of relevant documents in a database based on dictionary information is derived, which is similar to the single-broker case.
Abstract: In networked IR, a client submits a query to a broker, which is in contact with a large number of databases. In order to yield a maximum number of documents at minimum cost, the broker has to make estimates about the retrieval cost of each database, and then decide for each database whether or not to use it for the current query, and if, how many documents to retrieve from it. For this purpose, we develop a general decision-theoretic model and discuss different cost structures. Besides cost for retrieving relevant versus nonrelevant documents, we consider the following parameters for each database: expected retrieval quality, expected number of relevant documents in the database and cost factors for query processing and document delivery. For computing the overall optimum, a divide-and-conquer algorithm is given. If there are several brokers knowing different databases, a preselection of brokers can only be performed heuristically, but the computation of the optimum can be done similarily to the single-broker case. In addition, we derive a formula which estimates the number of relevant documents in a database based on dictionary information.
212 citations
01 Dec 2004
TL;DR: A simple, computer‐generated example is provided to illustrate the procedure for multimodel inference based on K‐L information and arguments are presented, based on statistical underpinnings that have been overlooked with time, that its theoretical basis renders it preferable to other approaches.
Abstract: Uncertainty of hydrogeologic conditions makes it important to consider alternative plausible models in an effort to evaluate the character of a ground water system, maintain parsimony, and make predictions with reasonable definition of their uncertainty. When multiple models are considered, data collection and analysis focus on evaluation of which model(s) is(are) most supported by the data. Generally, more than one model provides a similar acceptable fit to the observations; thus, inference should be made from multiple models. Kullback-Leibler (K-L) information provides a rigorous foundation for model inference that is simple to compute, is easy to interpret, selects parsimonious models, and provides a more realistic measure of precision than evaluation of any one model or evaluation based on other commonly referenced model selection criteria. These alternative criteria strive to identify the true (or quasi-true) model, assume it is represented by one of the models in the set, and given their preference for parsimony regardless of the available number of observations the selected model may be underfit. This is in sharp contrast to the K-L information approach, where models are considered to be approximations to reality, and it is expected that more details of the system will be revealed when more data are available. We provide a simple, computer-generated example to illustrate the procedure for multimodel inference based on K-L information and present arguments, based on statistical underpinnings that have been overlooked with time, that its theoretical basis renders it preferable to other approaches.
212 citations
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TL;DR: Two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances are presented and compared and indicate a large reduction in search effort in those complex domains where structural information can be inferred.
Abstract: Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches.
We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann's state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.
212 citations
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15 Aug 2005
TL;DR: A novel ranking scheme named Affinity Ranking (AR) is proposed to re-rank search results by optimizing two metrics: diversity -- which indicates the variance of topics in a group of documents; and information richness -- which measures the coverage of a single document to its topic.
Abstract: In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
211 citations
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21 Sep 2007
TL;DR: In this paper, a system and method for ranking items is described, which includes a ranking module that estimates the likelihood that a user will select the offerings and the likelihood of the user will purchase the items offered in the offerings.
Abstract: A system and method for ranking items is disclosed. In one embodiment, the items constitute offerings offered by at least one on-line vendor. The system includes a ranking module that estimates the likelihood that a user will select the offerings and the likelihood that the user will purchase the items offered in the offerings. Based on this information, the ranking module calculates the expected revenue to be generated by the offerings. The ranking module then ranks the offerings relative to one another so as to increase income received by the system administrator.
211 citations