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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
11 Jul 2021
TL;DR: In this article, a new first-stage ranker based on explicit sparsity regularization and a log-saturation effect on term weights is proposed, leading to highly sparse representations.
Abstract: In neural Information Retrieval, ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using efficient approximate nearest neighbors methods has proven to work well. Meanwhile, there has been a growing interest in learning sparse representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes. In this work, we present a new first-stage ranker based on explicit sparsity regularization and a log-saturation effect on term weights, leading to highly sparse representations and competitive results with respect to state-of-the-art dense and sparse methods. Our approach is simple, trained end-to-end in a single stage. We also explore the trade-off between effectiveness and efficiency, by controlling the contribution of the sparsity regularization.

108 citations

Proceedings ArticleDOI
Albert Gordo1, Diane Larlus1
21 Jul 2017
TL;DR: This work shows that, despite its subjective nature, the task of semantically ranking visual scenes is consistently implemented across a pool of human annotators and forms a good computable surrogate for semantic image retrieval in complex scenes.
Abstract: Querying with an example image is a simple and intuitive interface to retrieve information from a visual database. Most of the research in image retrieval has focused on the task of instance-level image retrieval, where the goal is to retrieve images that contain the same object instance as the query image. In this work we move beyond instance-level retrieval and consider the task of semantic image retrieval in complex scenes, where the goal is to retrieve images that share the same semantics as the query image. We show that, despite its subjective nature, the task of semantically ranking visual scenes is consistently implemented across a pool of human annotators. We also show that a similarity based on human-annotated region-level captions is highly correlated with the human ranking and constitutes a good computable surrogate. Following this observation, we learn a visual embedding of the images where the similarity in the visual space is correlated with their semantic similarity surrogate. We further extend our model to learn a joint embedding of visual and textual cues that allows one to query the database using a text modifier in addition to the query image, adapting the results to the modifier. Finally, our model can ground the ranking decisions by showing regions that contributed the most to the similarity between pairs of images, providing a visual explanation of the similarity.

108 citations

Proceedings ArticleDOI
Suqi Cheng1, Huawei Shen1, Junming Huang1, Wei Chen1, Xueqi Cheng1 
03 Jul 2014
TL;DR: Li et al. as mentioned in this paper developed an iterative ranking framework, i.e., IMRank, to efficiently solve influence maximization problem under independent cascade model, where starting from an initial ranking, e.g., one obtained from efficient heuristic algorithm, IMRank finds a selfconsistent ranking by reordering nodes iteratively in terms of their ranking-based marginal influence spread computed according to current ranking.
Abstract: Influence maximization, fundamental for word-of-mouth marketing and viral marketing, aims to find a set of seed nodes maximizing influence spread on social network. Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1) Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2) Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy. We first point out that greedy algorithms are essentially finding a self-consistent ranking, where nodes' ranks are consistent with their ranking-based marginal influence spread. This insight motivates us to develop an iterative ranking framework, i.e., IMRank, to efficiently solve influence maximization problem under independent cascade model. Starting from an initial ranking, e.g., one obtained from efficient heuristic algorithm, IMRank finds a self-consistent ranking by reordering nodes iteratively in terms of their ranking-based marginal influence spread computed according to current ranking. We also prove that IMRank definitely converges to a self-consistent ranking starting from any initial ranking. Furthermore, within this framework, a last-to-first allocating strategy and a generalization of this strategy are proposed to improve the efficiency of estimating ranking-based marginal influence spread for a given ranking. In this way, IMRank achieves both remarkable efficiency and high accuracy by leveraging simultaneously the benefits of greedy algorithms and heuristic algorithms. As demonstrated by extensive experiments on large scale real-world social networks, IMRank always achieves high accuracy comparable to greedy algorithms, while the computational cost is reduced dramatically, about 10-100 times faster than other scalable heuristics.

108 citations

Proceedings ArticleDOI
Xuanhui Wang1, Cheng Li1, Nadav Golbandi1, Michael Bendersky1, Marc Najork1 
17 Oct 2018
TL;DR: This paper shows that LambdaRank is a special configuration with a well-defined loss in the LambdaLoss framework, and thus provides theoretical justification for it, and allows us to define metric-driven loss functions that have clear connection to different ranking metrics.
Abstract: How to optimize ranking metrics such as Normalized Discounted Cumulative Gain (NDCG) is an important but challenging problem, because ranking metrics are either flat or discontinuous everywhere, which makes them hard to be optimized directly. Among existing approaches, LambdaRank is a novel algorithm that incorporates ranking metrics into its learning procedure. Though empirically effective, it still lacks theoretical justification. For example, the underlying loss that LambdaRank optimizes for remains unknown until now. Due to this, there is no principled way to advance the LambdaRank algorithm further. In this paper, we present LambdaLoss, a probabilistic framework for ranking metric optimization. We show that LambdaRank is a special configuration with a well-defined loss in the LambdaLoss framework, and thus provide theoretical justification for it. More importantly, the LambdaLoss framework allows us to define metric-driven loss functions that have clear connection to different ranking metrics. We show a few cases in this paper and evaluate them on three publicly available data sets. Experimental results show that our metric-driven loss functions can significantly improve the state-of-the-art learning-to-rank algorithms.

108 citations

01 Jan 2001
TL;DR: In this article, an outranking preference model based on the possibility theory is developed to model the imprecise preference relation between each pair of design concepts, and three types of indices are developed to determine the non-dominated design concepts for continuous improvement or further development at later design stages.
Abstract: Abstract The conceptual design evaluation is important, since the poor selection of a design concept can rarely be compensated at later design stages. Due to subjective and incomplete design information collected at the early design stage, it is difficult to select the “best” design concepts from a number of alternatives. To tackle this problem, an outranking preference model based on the possibility theory is developed in this paper. The fuzzy outranking relation is developed to model the imprecise preference relation between each pair of design concepts. A design concept outranked others if and only if there is sufficient evidence to support that the concept is superior or at least equal to the others. According to the fuzzy outranking relation identified between each pair of design concepts, three types of indices are developed to determine the non-dominated design concepts for continuous improvement or further development at later design stages. Moreover, the sensitivity analysis is used to examine the robustness of the result. The fuzzy outranking preference model developed is more suitable to be used for concept selection in the imprecise and uncertain design environment.

108 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