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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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Patent
06 Mar 2008
TL;DR: In this article, a solution for evaluating a plurality of entities includes assigning an attribute score to each entity for each of a multitude of attributes, which can be further processed to identify a set of suspicious entities.
Abstract: A solution for evaluating a plurality of entities includes assigning an attribute score to each entity for each of a multitude of attributes. For one or more of the attributes, the corresponding attribute score is assigned based on a ranking of each entity with respect to the other entities for the attribute. A composite score is generated for each entity based on the attribute scores for the attributes, which can be further processed to, for example, identify a set of suspicious entities.

176 citations

Book ChapterDOI
20 Oct 2004
TL;DR: The SPIRIT search engine provides a test bed for the development of web search technology that is specialised for access to geographical information and supports functionality for disambiguation, query expansion, relevance ranking and metadata extraction.
Abstract: The SPIRIT search engine provides a test bed for the development of web search technology that is specialised for access to geographical information. Major components include the user interface, a geographical ontology, maintenance and retrieval functions for a test collection of web documents, textual and spatial indexes, relevance ranking and metadata extraction. Here we summarise the functionality and interaction between these components before focusing on the design of the geo-ontology and the development of spatio-textual indexing methods. The geo-ontology supports functionality for disambiguation, query expansion, relevance ranking and metadata extraction. Geographical place names are accompanied by multiple geometric footprints and qualitative spatial relationships. Spatial indexing of documents has been integrated with text indexing through the use of spatio-textual keys in which terms are concatenated with spatial cells to which they relate. Preliminary experiments demonstrate considerable performance benefits when compared with pure text indexing and with text indexing followed by a spatial filtering stage.

175 citations

Journal ArticleDOI
01 Sep 2010
TL;DR: Empirical studies with real-world spatial data demonstrate that LkPT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby objects; and they show that the proposed algorithms are scalable and outperform a baseline approach significantly.
Abstract: The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous work considers the potential results of such a query as being independent when ranking them. However, a relevant result object with nearby objects that are also relevant to the query is likely to be preferable over a relevant object without relevant nearby objects.The paper proposes the concept of prestige-based relevance to capture both the textual relevance of an object to a query and the effects of nearby objects. Based on this, a new type of query, the Location-aware top-k Prestige-based Text retrieval (LkPT) query, is proposed that retrieves the top-k spatial web objects ranked according to both prestige-based relevance and location proximity.We propose two algorithms that compute LkPT queries. Empirical studies with real-world spatial data demonstrate that LkPT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby objects; and they show that the proposed algorithms are scalable and outperform a baseline approach significantly.

175 citations

Proceedings ArticleDOI
01 Jul 2018
TL;DR: A counterfactual inference framework is presented that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data, and a propensity-weighted ranking SVM is derived for discriminative learning from implicit feedback, where click models take the role of the propensity estimator.
Abstract: Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a Propensity-Weighted Ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. In contrast to most conventional approaches to de-biasing the data using click models, this allows training of ranking functions even in settings where queries do not repeat. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model misspecification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.

175 citations

Proceedings ArticleDOI
20 May 2003
TL;DR: This work formalizes general properties a matchmaker should have, then it presents a matchmaking facilitator, compliant with desired properties, that embeds a NeoClassic reasoner, whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories.
Abstract: More and more resources are becoming available on the Web, and there is a growing need for infrastructures that, based on advertised descriptions, are able to semantically match demands with suppliesWe formalize general properties a matchmaker should have, then we present a matchmaking facilitator, compliant with desired propertiesThe system embeds a NeoClassic reasoner, whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories Experiments carried out show the good correspondence between users and system rankings

175 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