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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
19 Jul 2009
TL;DR: An efficient document ranking algorithm is derived that generalizes the well-known probability ranking principle by considering both the uncertainty of relevance predictions and correlations between retrieved documents and the benefit of diversification is mathematically quantified.
Abstract: This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are dependent between each other. Inspired by the Modern Portfolio Theory, an economic theory dealing with investment in financial markets, we argue that ranking under uncertainty is not just about picking individual relevant documents, but about choosing the right combination of relevant documents. This motivates us to quantify a ranked list of documents on the basis of its expected overall relevance (mean) and its variance; the latter serves as a measure of risk, which was rarely studied for document ranking in the past. Through the analysis of the mean and variance, we show that an optimal rank order is the one that balancing the overall relevance (mean) of the ranked list against its risk level (variance). Based on this principle, we then derive an efficient document ranking algorithm. It generalizes the well-known probability ranking principle (PRP) by considering both the uncertainty of relevance predictions and correlations between retrieved documents. Moreover, the benefit of diversification is mathematically quantified; we show that diversifying documents is an effective way to reduce the risk of document ranking. Experimental results in text retrieval confirm performance.

254 citations

Proceedings ArticleDOI
23 Apr 2018
TL;DR: Qualitative studies demonstrate evidence that the proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback, ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.
Abstract: This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (Latent Relational Metric Learning) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learning approaches. This enables not only better performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The memory-based attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by 6%-7.5% in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.

253 citations

Proceedings ArticleDOI
01 Jul 2000
TL;DR: The results show that incorporating quality metrics can generally improve search effectiveness in both centralized and distributed search environments.
Abstract: Most information retrieval systems on the Internet rely primarily on similarity ranking algorithms based solely on term frequency statistics Information quality is usually ignored This leads to the problem that documents are retrieved without regard to their quality We present an approach that combines similarity-based similarity ranking with quality ranking in centralized and distributed search environments Six quality metrics, including the currency, availability, information-to-noise ratio, authority, popularity, and cohesiveness, were investigated Search effectiveness was significantly improved when the currency, availability, information-to-noise ratio and page cohesiveness metrics were incorporated in centralized search The improvement seen when the availability, information-to- noise ratio, popularity, and cohesiveness metrics were incorporated in site selection was also significant Finally, incorporating the popularity metric in information fusion resulted in a significant improvement In summary, the results show that incorporating quality metrics can generally improve search effectiveness in both centralized and distributed search environments

252 citations

25 Jun 1997
TL;DR: It is discovered that once a good basic ranking scheme is being used, the use of phrases does not have a major effect on precision at high ranks, and phrases are more useful at lower ranks where the connection between documents and relevance is more tenuous.
Abstract: As the amount of textual information available through the World Wide Web grows, there is a growing need for high-precision IR systems that enable a user to find useful information from the masses of available textual data. Phrases have traditionally been regarded as precision-enhancing devices and have proved useful as content-identifiers in representing documents. In this study, we compare the usefulness of phrases recognized using linguistic methods and those recognized by statistical techniques. We focus in particular on high-precision retrieval. We discover that once a good basic ranking scheme is being used, the use of phrases does not have a major effect on precision at high ranks. Phrases are more useful at lower ranks where the connection between documents and relevance is more tenuous. Also, we find that the syntactic and statistical methods for recognizing phrases yield comparable performance.

251 citations

Patent
19 Jun 2009
TL;DR: In this paper, a computer implemented method and system for ranking a user in an organization based on the user's information technology related activities and arriving at an end risk score used for determining the risk involved in activities performed by the user and for other purposes.
Abstract: Disclosed herein is a computer implemented method and system for ranking a user in an organization based on the user's information technology related activities and arriving at an end risk score used for determining the risk involved in activities performed by the user and for other purposes. Group risk ranking profiles and security policies for usage of the organization's resources are created. The user is associated with one or more group risk ranking profiles. A security client application tracks the user's activities. Points are assigned to the user's tracked activities based on each of the associated group risk ranking profiles. The assigned points are aggregated to generate a first risk score. The assigned points of the user's tracked activities are modified at different levels based on predefined rules. The modified points are aggregated to generate the end risk score which is used for compliance and governance purposes, optimizing resources, etc.

250 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