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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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Proceedings ArticleDOI
01 Jul 1995
TL;DR: This study explores the highly effective approach of feeding back passages of large documents and a less- expensive method which discards long documents is also reviewed and found to be effective if there are enough relevant documents.
Abstract: Relevance Feedbacvk With Too Much Data A UMass Technical Report James Allan IR-57 (ref.TR95-6) Modern text collections often contain large documents which span several subject areas. Such documents are problematic for relevance feedback since inappropriate terms can easily be chosen. This study explores the highly effective approach of feeding back passages of large documents. A less- expensive method which discards long documents is also reviewed and found to be effective if there are enough relevant documents. A hybrid approach which feeds back short documents and passages of long documents may be the best compromise.

103 citations

Proceedings ArticleDOI
05 Dec 2011
TL;DR: DREX (Developer Recommendation with k-nearest-neighbor search and Expertise ranking) to developer recommendation for bug resolution based on K-Nearest-Neighbor search with bug similarity and expertise ranking with various metrics, including simple frequency and social network metrics is proposed.
Abstract: This paper proposes a new approach called DREX (Developer Recommendation with k-nearest-neighbor search and Expertise ranking) to developer recommendation for bug resolution based on K-Nearest-Neighbor search with bug similarity and expertise ranking with various metrics, including simple frequency and social network metrics. We collect Mozilla Fire fox open bug repository as the experimental data set and compare different ranking metrics on the performance of recommending capable developers for bugs. Our experimental results demonstrate that, when recommending 10 developers for each one of the 250 testing bugs, DREX has produced better performance than traditional methods with multi-labeled text categorization. The best performance obtained by two metrics as Out-Degree and Frequency, is with recall as 0.6 on average. Moreover, other social network metrics such as Degree and Page Rank have produced comparable performance on developer recommendation as Frequency when used for developer expertise ranking.

102 citations

Journal ArticleDOI
TL;DR: An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources.
Abstract: With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

102 citations

Proceedings ArticleDOI
03 Oct 2013
TL;DR: This paper presents an expansion method for a concept based information retrieval that uses semantic relatedness to extend user query through an undirected graph of concepts.
Abstract: Concept based search is a method that enhances information retrieval systems using semantic relationships. The recall in concept based search is relatively low. That low recall comes from the fact that it is not easy to represent a concept completely. Query expansion intends to fill a gap because concept representation is always partial. Query expansion improves the recall. In this paper we present an expansion method for a concept based information retrieval. Our method uses semantic relatedness to extend user query through an undirected graph of concepts.

102 citations

Patent
28 Jun 2010
TL;DR: In this paper, a suggested search query is generated using various techniques, such as by applying an n-gram language model, and a classification of the suggested search queries is determined, and the suggested query is presented together with a visual indicator.
Abstract: The present invention is directed to presenting a suggested search query. Responsive to receiving a user-devised search parameter, a suggested search query is identified. The user-devised search parameter might have been previously received by a search system, or alternatively, might be a unique query that has not been previously received. A suggested search query might be generated using various techniques, such as by applying an n-gram language model. A classification of the suggested search query is determined, and the suggested search query is presented together with a visual indicator, which signifies the classification.

102 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