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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.


Papers
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Journal ArticleDOI
01 Jan 1994
TL;DR: This work approaches the subsumption problem in the setting of object-oriented databases, and finds that reasoning techniques from Artificial Intelligence can be applied and yield efficient algorithms.
Abstract: Subsumption between queries is a valuable information, eg, for semantic query optimization We approach the subsumption problem in the setting of object-oriented databases, and find that reasoning techniques from Artificial Intelligence can be applied and yield efficient algorithms

130 citations

Journal Article
TL;DR: The Inquirus meta search engine makes improvements over existing search engines in a number of areas, e.g.: more useful document summaries incorporating query term context, identification of both pages which no longer exist and pages which have no longer contain the query terms.

130 citations

Proceedings ArticleDOI
Ziqin Wang1, Jun Xu, Li Liu, Fan Zhu, Ling Shao 
TL;DR: This paper proposes a novel ranking attention module, which automatically ranks and selects these maps for fine-grained VOS performance, and develops a real-time yet very accurate Ranking Attention Network (RANet) for VOS.
Abstract: Despite online learning (OL) techniques have boosted the performance of semi-supervised video object segmentation (VOS) methods, the huge time costs of OL greatly restrict their practicality. Matching based and propagation based methods run at a faster speed by avoiding OL techniques. However, they are limited by sub-optimal accuracy, due to mismatching and drifting problems. In this paper, we develop a real-time yet very accurate Ranking Attention Network (RANet) for VOS. Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner. To better utilize the similarity maps, we propose a novel ranking attention module, which automatically ranks and selects these maps for fine-grained VOS performance. Experiments on DAVIS-16 and DAVIS-17 datasets show that our RANet achieves the best speed-accuracy trade-off, e.g., with 33 milliseconds per frame and J&F=85.5% on DAVIS-16. With OL, our RANet reaches J&F=87.1% on DAVIS-16, exceeding state-of-the-art VOS methods. The code can be found at this https URL.

130 citations

Proceedings ArticleDOI
Georges Dupret1, Ciya Liao1
04 Feb 2010
TL;DR: A new model to interpret the clickthrough logs of a web search engine is proposed based on explicit assumptions on the user behavior and used to predict document relevance and then used as feature for a "Learning to Rank" machine learning algorithm.
Abstract: We propose a new model to interpret the clickthrough logs of a web search engine. This model is based on explicit assumptions on the user behavior. In particular, we draw conclusions on a document relevance by observing the user behavior after he examined the document and not based on whether a user clicks or not a document url. This results in a model based on intrinsic relevance, as opposed to perceived relevance. We use the model to predict document relevance and then use this as feature for a "Learning to Rank" machine learning algorithm. Comparing the ranking functions obtained by training the algorithm with and without the new feature we observe surprisingly good results. This is particularly notable given that the baseline we use is the heavily optimized ranking function of a leading commercial search engine. A deeper analysis shows that the new feature is particularly helpful for non navigational queries and queries with a large abandonment rate or a large average number of queries per session. This is important because these types of query is considered to be the most difficult to solve.

130 citations

Journal ArticleDOI
TL;DR: The generalized Boolean query model must be reconciled with the vector space approach, suggested new lattice structures for weighted retrieval, and probabilistic retrieval models, and proper retrieval evaluation mechanisms reflecting the fuzzy nature of retrieval are needed.
Abstract: Substantial work has been done on the application of fuzzy subset theory to information retrieval. Boolean query processing has been generalized to allow for weights to be attached to individual terms, in either the document indexing or the query representation, or both. Problems with the generalized Boolean lattice structure have been noted, and an alternative approach using query thresholds and appropriate document evaluation functions has been suggested. Problems remain unsolved, however. Criteria generated for the query processing mechanism are inconsistent. The exact functional form and appropriate parameters for the query processing mechanism must be specified. Moreover, the generalized Boolean query model must be reconciled with the vector space approach, suggested new lattice structures for weighted retrieval, and probabilistic retrieval models. Finally, proper retrieval evaluation mechanisms reflecting the fuzzy nature of retrieval are needed.

130 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